Abbe, E. 2018. Community Detection and Stochastic Block Models: Recent Developments. Journal of Machine Learning Research, 18(177), 1–86. (Cited on p. 356.)Google Scholar

Acebrón, J. A., Bonilla, L. L., Pérez Vicente, C. J., Ritort, F., and Spigler, R. 2005. The Kuramoto Model: A Simple Paradigm for Synchronization Phenomena. Reviews of Modern Physics, 77(1), 137–185. (Cited on p. 235.)CrossRefGoogle Scholar

Adamic, L. A., and Adar, E. 2003. Friends and Neighbors on the Web. Social Networks, 25(3), 211–230. (Cited on p. 131.)CrossRefGoogle Scholar

Aggarwal, C. C., Zhao, Y., and Yu, P. S. 2011. Outlier Detection in Graph Streams. Pages 399–409 of: 2011 IEEE 27th International Conference on Data Engineering. Hannover, Germany: IEEE. (Cited on p. 469.)Google Scholar

Ahn, Y.-Y., Bagrow, J. P., and Lehmann, S. 2010. Link Communities Reveal Multiscale Complexity in Networks. Nature, 466(7307), 761–764. (Cited on pp. 184, 201.)CrossRefGoogle ScholarPubMed

Ahn, Y.-Y., Ahnert, S. E., Bagrow, J. P., and Barabási, A.-L. 2011. Flavor Network and the Principles of Food Pairing. Scientific Reports, 1(1), 196. (Cited on pp. 9, 10, 22, 24, 26.)CrossRefGoogle Scholar

Airoldi, E. M., Blei, D., Fienberg, S., and Xing, E. 2008. Mixed Membership Stochastic Blockmodels. In: Advances in Neural Information Processing Systems, vol. 21. Curran Associates, Inc. (Cited on p. 358.)Google Scholar

Al Hasan, M., and Dave, V. S. 2018. Triangle Counting in Large Networks: A Review. WIREs Data Mining and Knowledge Discovery, 8(2), e1226. (Cited on p. 469.)CrossRefGoogle Scholar

Albert, R., Jeong, H., and Barabási, A.-L. 2000. Error and Attack Tolerance of Complex Networks. Nature, 406(6794), 378–382. (Cited on p. 167.)CrossRefGoogle ScholarPubMed

Allen, A. 2007. Vaccine: The Controversial Story of Medicine's Greatest Lifesaver. New York, NY: W.W. Norton. (Cited on p. 67.)Google Scholar

Almeida-Neto, M., Guimarães, P., GuimarãesJr, P. R., Loyola, R. D., and Ulrich, W. 2008. A Consistent Metric for Nestedness Analysis in Ecological Systems: Reconciling Concept and Measurement. Oikos, 117(8), 1227–1239. (Cited on p. 202.)CrossRefGoogle Scholar

Alon, U., and Yahav, E. 2021. On the Bottleneck of Graph Neural Networks and Its Practical Implications. In: International Conference on Learning Representations. Virtual Event, Austria: OpenReview.net. (Cited on p. 444.)Google Scholar

Alstott, J., Bullmore, E., and Plenz, D. 2014. Powerlaw: A Python Package for Analysis of Heavy-Tailed Distributions. PLoS ONE, 9(1), e85777. (Cited on pp. 156, 163.)CrossRefGoogle ScholarPubMed

Amstrup, S. C., McDonald, T. L., and Manly, B. F. J. (eds.). 2005. Handbook of Capture-Recapture Analysis. Princeton, NJ: Princeton University Press. (Cited on pp. 391, 395.)Google Scholar

Ashburner, M., Ball, C. A., Blake, J. A. et al. 2000. Gene Ontology: Tool for the Unification of Biology. Nature Genetics, 25(1), 25–29. (Cited on p. 116.)CrossRefGoogle ScholarPubMed

Ashmore, R., Calinescu, R., and Paterson, C. 2021. Assuring the Machine Learning Lifecycle: Desiderata, Methods, and Challenges. ACM Computing Surveys, 54(5), 111:1–111:39. (Cited on p. 31.)Google Scholar

Atmar, W., and Patterson, B. D. 1993. The Measure of Order and Disorder in the Distribution of Species in Fragmented Habitat. Oecologia, 96(3), 373–382. (Cited on p. 202.)CrossRefGoogle ScholarPubMed

Babai, L. 2016. Graph Isomorphism in Quasipolynomial Time [Extended Abstract]. Pages 684–697 of: Proceedings of the Forty-Eighth Annual ACM Symposium on Theory of Computing. STOC ’16. New York, NY: Association for Computing Machinery. (Cited on p. 443.)Google Scholar

Babai, L. 2019. Canonical Form for Graphs in Quasipolynomial Time: Preliminary Report. Pages 1237–1246 of: Proceedings of the 51st Annual ACM SIGACT Symposium on Theory of Computing. STOC 2019. New York, NY: Association for Computing Machinery. (Cited on p. 443.)Google Scholar

Backstrom, L., Boldi, P., Rosa, M., Ugander, J., and Vigna, S. 2012. Four Degrees of Separation. Pages 33–42 of: Proceedings of the 4th Annual ACM Web Science Conference. Evanston, IL: Association for Computing Machinery. (Cited on pp. 341, 458.)Google Scholar

Baek, J., Kang, M., and Hwang, S. J. 2022. Accurate Learning of Graph Representations with Graph Multiset Pooling. In: International Conference on Learning Representations. Virtual Event, Austria: OpenReview.net. (Cited on p. 442.)Google Scholar

Bagrow, J., and Ahn, Y.-Y. 2022. Network Cards: Concise, Readable Summaries of Network Data. Applied Network Science, 7(1), 1–17. (Cited on pp. 225, 232, 474.)CrossRefGoogle Scholar

Bagrow, J. P. 2008. Evaluating Local Community Methods in Networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(05), P05001. (Cited on p. 184.)CrossRefGoogle Scholar

Bagrow, J. P. 2012. Communities and Bottlenecks: Trees and Treelike Networks Have High Modularity. Physical Review E, 85(6), 066118. (Cited on p. 179.)CrossRefGoogle ScholarPubMed

Bagrow, J. P. 2020. Democratizing AI: Non-Expert Design of Prediction Tasks. PeerJ Computer Science, 6(Sept.), e296. (Cited on p. 374.)CrossRefGoogle ScholarPubMed

Bagrow, J. P., and Bollt, E. M. 2005. Local Method for Detecting Communities. Physical Review E, 72(4), 046108. (Cited on pp. 184, 185, 201.)CrossRefGoogle ScholarPubMed

Bagrow, J. P., and Bollt, E. M. 2019. An Information-Theoretic, All-Scales Approach to Comparing Networks. Applied Network Science, 4(1), 45. (Cited on pp. 24, 216, 228.)CrossRefGoogle Scholar

Bagrow, J. P., Lehmann, S., and Ahn, Y.-Y. 2015. Robustness and Modular Structure in Networks. Network Science, 3(4), 509–525. (Cited on pp. 386, 395.)CrossRefGoogle Scholar

Bagrow, J. P., Danforth, C. M., and Mitchell, L. 2017. Which Friends Are More Popular than You?: Contact Strength and the Friendship Paradox in Social Networks. Pages 103–108 of: Proceedings of the 2017 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2017. Sydney, Australia: Association for Computing Machinery. (Cited on p. 323.)Google Scholar

Bagrow, J. P., Liu, X., and Mitchell, L. 2019. Information Flow Reveals Prediction Limits in Online Social Activity. Nature Human Behaviour, 3(2), 122–128. (Cited on p. 37.)CrossRefGoogle ScholarPubMed

Baker, R. J. 2010. CMOS: Circuit Design, Layout, and Simulation. 3rd edn. IEEE Press Series on Microelectronic Systems. Piscataway, NJ; Hoboken, NJ: IEEE. (Cited on p. 26.)Google Scholar

Balcilar, M., Renton, G., Héroux, P., Gaüzère, B., Adam, S., and Honeine, P. 2022. Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective. In: International Conference on Learning Representations. Virtual Event, Austria: OpenReview.net. (Cited on p. 443.)Google Scholar

Ball, P. 2006. Critical Mass: How One Thing Leads to Another. New York, NY: Farrar, Straus and Giroux. (Cited on p. 5.)Google Scholar

Banerjee, O., El Ghaoui, L., and d’Aspremont, A. 2008. Model Selection Through Sparse Maximum Likelihood Estimation for Multivariate Gaussian or Binary Data. The Journal of Machine Learning Research, 9(June), 485–516. (Cited on p. 371.)Google Scholar

Barabási, A.-L. 2016. Network Science. Cambridge: Cambridge University Press. (Cited on pp. 16, 472.)Google Scholar

Barabási, A.-L., and Albert, R. 1999. Emergence of Scaling in Random Networks. Science, 286(5439), 509–512. (Cited on pp. 344, 346, 349.)Google Scholar

Barabási, A.-L., and Oltvai, Z. N. 2004. Network Biology: Understanding the Cell's Functional Organization. Nature Reviews Genetics, 5(2), 101–113. (Cited on p. 25.)CrossRefGoogle ScholarPubMed

Barber, M. J., and Clark, J. W. 2009. Detecting Network Communities by Propagating Labels under Constraints. Physical Review E, 80(2), 026129. (Cited on p. 469.)CrossRefGoogle ScholarPubMed

Barnes, E. R. 1982. An Algorithm for Partitioning the Nodes of a Graph. SIAM Journal on Algebraic Discrete Methods, 3(4), 541–550. (Cited on p. 427.)CrossRefGoogle Scholar

Bartomeus, I., Vilà, M., and Santamaría, L. 2008. Contrasting Effects of Invasive Plants in Plant–Pollinator Networks. Oecologia, 155(4), 761–770. (Cited on p. 23.)CrossRefGoogle ScholarPubMed

Bascompte, J. 2007. Networks in Ecology. Basic and Applied Ecology, 8(6), 485–490. (Cited on p. 25.)CrossRefGoogle Scholar

Basser, P. J., Mattiello, J., and LeBihan, D. 1994. Estimation of the Effective Self-Diffusion Tensor from the NMR Spin Echo. Journal of Magnetic Resonance, Series B, 103(3), 247–254. (Cited on p. 8.)CrossRefGoogle ScholarPubMed

Basser, P. J., Mattiello, J., and LeBihan, D. 1994. MR Diffusion Tensor Spectroscopy and Imaging. Biophysical Journal, 66(1), 259–267. (Cited on p. 8.)CrossRefGoogle ScholarPubMed

Bassett, D. S., and Sporns, O. 2017. Network Neuroscience. Nature Neuroscience, 20(3), 353–364. (Cited on pp. 25, 82.)CrossRefGoogle ScholarPubMed

Batagelj, V., and Brandes, U. 2005. Efficient Generation of Large Random Networks. Physical Review E, 71(3), 036113. (Cited on p. 469.)CrossRefGoogle ScholarPubMed

Batson, J., Spielman, D. A., Srivastava, N., and Teng, S.-H. 2013. Spectral Sparsification of Graphs: Theory and Algorithms. Communications of the ACM, 56(8), 87–94. (Cited on p. 134.)CrossRefGoogle Scholar

Battiston, S., Caldarelli, G., May, R. M., Roukny, T., and Stiglitz, J. E. 2016. The Price of Complexity in Financial Networks. Proceedings of the National Academy of Sciences, 113(36), 10031–10036. (Cited on p. 22.)CrossRefGoogle Scholar

Belkin, M., and Niyogi, P. 2003. Laplacian Eigenmaps for Dimensionality Reduction and Data Representation. Neural Computation, 15(6), 1373–1396. (Cited on p. 430.)CrossRefGoogle Scholar

Belkin, M., Hsu, D., Ma, S., and Mandal, S. 2019. Reconciling Modern Machine-Learning Practice and the Classical Bias–Variance Trade-Off. Proceedings of the National Academy of Sciences, 116(32), 15849–15854. (Cited on p. 277.)CrossRefGoogle ScholarPubMed

Bender, E. M., Gebru, T., McMillan-Major, A., and Shmitchell, S. 2021. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big? Pages 610–623 of: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. Virtual Event, Canada: Association for Computing Machinery. (Cited on p. 29.)Google Scholar

Bengio, Y., Ducharme, R., Vincent, P., and Janvin, C. 2003. A Neural Probabilistic Language Model. The Journal of Machine Learning Research, 3(null), 1137–1155. (Cited on p. 445.)Google Scholar

Bentley, J., Knuth, D., and McIlroy, D. 1986. Programming Pearls: A Literate Program. Communications of the ACM, 29(6), 471–483. (Cited on p. 313.)CrossRefGoogle Scholar

Bentley, J. L. 1975. Multidimensional Binary Search Trees Used for Associative Searching. Communications of the ACM, 18(9), 509–517. (Cited on p. 426.)CrossRefGoogle Scholar

Bharat, K., Broder, A., Henzinger, M., Kumar, P., and Venkatasubramanian, S. 1998. The Connectivity Server: Fast Access to Linkage Information on the Web. Computer Networks and ISDN Systems, 30(1-7), 469–477. (Cited on p. 469.)CrossRefGoogle Scholar

Bhattacharya, A., Friedland, S., and Peled, U. N. 2008. On the First Eigenvalue of Bipartite Graphs. The Electronic Journal of Combinatorics [electronic only], 15(1). (Cited on p. 197.)Google Scholar

Bishop, C. M. 2006. Pattern Recognition and Machine Learning. Berlin, Heidelberg: Springer. (Cited on pp. 277, 472.)Google Scholar

Blondel, V. D., Guillaume, J.-L., Lambiotte, R., and Lefebvre, E. 2008. Fast Unfolding of Communities in Large Networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008. (Cited on pp. 179, 358.)CrossRefGoogle Scholar

Bloom, B. H. 1970. Space/Time Trade-Offs in Hash Coding with Allowable Errors. Communications of the ACM, 13(7), 422–426. (Cited on p. 455.)CrossRefGoogle Scholar

Boguñá, M., Pastor-Satorras, R., and Vespignani, A. 2004. Cut-Offs and Finite Size Effects in Scale-Free Networks. The European Physical Journal B, 38(2), 205–209. (Cited on pp. 163, 173.)CrossRefGoogle Scholar

Boguñá, M., Bonamassa, I., De Domenico, M., Havlin, S., Krioukov, D., and Serrano, M. Á. 2021. Network Geometry. Nature Reviews Physics, 3(2), 114–135. (Cited on p. 438.)CrossRefGoogle Scholar

Böhning, D., van der Heijden, P. G. M., and Bunge, J. (eds.). 2018. Capture-Recapture Methods for the Social and Medical Sciences. Boca Raton, FL: CRC Press/Taylor & Francis Group. (Cited on pp. 391, 395.)Google Scholar

Boldi, P., and Vigna, S. 2004. The WebGraph Framework I: Compression Techniques. Pages 595–602 of: Proceedings of the 13th International Conference on World Wide Web. New York, NY: Association for Computing Machinery. (Cited on pp. 451, 469.)Google Scholar

Boldi, P., Rosa, M., and Vigna, S. 2011. HyperANF: Approximating the Neighbourhood Function of Very Large Graphs on a Budget. Pages 625–634 of: Proceedings of the 20th International Conference on World Wide Web. WWW ’11. New York, NY: Association for Computing Machinery. (Cited on pp. 456, 457, 469.)CrossRefGoogle Scholar

Boldi, P., Rosa, M., Santini, M., and Vigna, S. 2011. Layered Label Propagation: A Multiresolution Coordinate-Free Ordering for Compressing Social Networks. Pages 587–596 of: Proceedings of the 20th International Conference on World Wide Web. WWW ’11. New York, NY: Association for Computing Machinery. (Cited on pp. 452, 469.)Google Scholar

Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., and Yakhnenko, O. 2013. Translating Embeddings for Modeling Multi-relational Data. In: Advances in Neural Information Processing Systems, vol. 26. Curran Associates, Inc. (Cited on p. 437.)Google Scholar

Borgatti, S. P., and Everett, M. G. 2000. Models of Core/Periphery Structures. Social Networks, 21(4), 375–395. (Cited on pp. 194, 195, 201.)CrossRefGoogle Scholar

Borgatti, S. P., Carley, K. M., and Krackhardt, D. 2006. On the Robustness of Centrality Measures under Conditions of Imperfect Data. Social Networks, 28(2), 124–136. (Cited on pp. 377, 395.)CrossRefGoogle Scholar

Borwein, J., and Bailey, D. 2013. The Reinhart-Rogoff Error – or How Not to Excel at Economics. The Conversation, Apr. (Cited on p. 36.)Google Scholar

Bostrom, N. 2014. Superintelligence: Paths, Dangers, Strategies. Oxford: Oxford University Press. (Cited on p. 277.)Google Scholar

Boswell, D., and Foucher, T. 2011. The Art of Readable Code. Theory in Practice. Sebastopol, CA: O’Reilly Media. (Cited on pp. 293, 300.)Google Scholar

Bouveyron, C., Celeux, G., Murphy, T. B., and Raftery, A. E. 2019. Model-Based Clustering and Classification for Data Science: With Applications in R. Cambridge: Cambridge University Press. (Cited on p. 428.)CrossRefGoogle Scholar

boyd, d., Golder, S., and Lotan, G. 2010. Tweet, Tweet, Retweet: Conversational Aspects of Retweeting on Twitter. Pages 1–10 of: 2010 43rd Hawaii International Conference on System Sciences. Honolulu, HI: IEEE. (Cited on p. 84.)Google Scholar

Breiger, R. L., Boorman, S. A., and Arabie, P. 1975. An Algorithm for Clustering Relational Data with Applications to Social Network Analysis and Comparison with Multidimensional Scaling. Journal of Mathematical Psychology, 12(3), 328–383. (Cited on p. 374.)CrossRefGoogle Scholar

Brenner, S. 2009. In the Beginning Was the Worm …. Genetics, 182(2), 413–415. (Cited on p. 82.)CrossRefGoogle Scholar

Brin, S., and Page, L. 1998. The Anatomy of a Large-Scale Hypertextual Web Search Engine. Computer Networks and ISDN Systems, 30(1), 107–117. (Cited on p. 189.)CrossRefGoogle Scholar

Broder, A. 1997. On the Resemblance and Containment of Documents. Pages 21–29 of: Proceedings. Compression and Complexity of SEQUENCES 1997 (Cat. No.97TB100171). Salerno, Italy: IEEE. (Cited on p. 455.)Google Scholar

Brody, S., Alon, U., and Yahav, E. 2022. How Attentive Are Graph Attention Networks? In: International Conference on Learning Representations. Virtual Event, Austria: OpenReview.net. (Cited on pp. 273, 442.)Google Scholar

Broido, A. D., and Clauset, A. 2019. Scale-Free Networks Are Rare. Nature Communications, 10(1), 1017. (Cited on p. 346.)CrossRefGoogle ScholarPubMed

Brown, T., Mann, B., Ryder, N. et al. 2020. Language Models Are Few-Shot Learners. Pages 1877–1901 of: Advances in Neural Information Processing Systems, vol. 33. Vancouver, BC: Curran Associates, Inc. (Cited on p. 261.)Google Scholar

Bryan, K., and Leise, T. 2006. The $25,000,000,000 Eigenvector: The Linear Algebra behind Google. SIAM Review, 48(3), 569–581. (Cited on p. 427.)CrossRefGoogle Scholar

Buehrer, G., and Chellapilla, K. 2008. A Scalable Pattern Mining Approach to Web Graph Compression with Communities. Pages 95–106 of: Proceedings of the 2008 International Conference on Web Search and Data Mining. WSDM ‘08. New York, NY: Association for Computing Machinery. (Cited on p. 452.)Google Scholar

Buluç, A., Meyerhenke, H., Safro, I., Sanders, P., and Schulz, C. 2016. Recent Advances in Graph Partitioning. Pages 117–158 of: Kliemann, L., and Sanders, P. (eds.), Algorithm Engineering, vol. 9220. Cham, Switzerland: Springer. (Cited on pp. 409, 427.)Google Scholar

Buolamwini, J., and Gebru, T. 2018. Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification. Pages 77–91 of: Proceedings of the 1st Conference on Fairness, Accountability and Transparency. PMLR. (Cited on p. 28.)Google Scholar

Burnham, K. P., and Anderson, D. R. 2004. Multimodel Inference: Understanding AIC and BIC in Model Selection. Sociological Methods & Research, 33(2), 261–304. (Cited on p. 162.)CrossRefGoogle Scholar

Burt, R. S. 1992. Structural Holes: The Social Structure of Competition. Cambridge, MA: Harvard University Press. (Cited on p. 15.)CrossRefGoogle Scholar

Busetto, L., Wick, W., and Gumbinger, C. 2020. How to Use and Assess Qualitative Research Methods. Neurological Research and Practice, 2(1), 14. (Cited on p. 226.)CrossRefGoogle ScholarPubMed

Butts, C. T. 2003. Network Inference, Error, and Informant (in)Accuracy: A Bayesian Approach. Social Networks, 25(2), 103–140. (Cited on p. 134.)CrossRefGoogle Scholar

Butts, C. T. 2009. Revisiting the Foundations of Network Analysis. Science, 325(5939), 414–416. (Cited on p. 86.)Google Scholar

Callaway, D. S., Hopcroft, J. E., Kleinberg, J. M., Newman, M. E. J., and Strogatz, S. H. 2001. Are Randomly Grown Graphs Really Random? Physical Review E, 64(4), 041902. (Cited on p. 201.)CrossRefGoogle ScholarPubMed

Callaway, E. 2022. ‘The Entire Protein Universe’: AI Predicts Shape of Nearly Every Known Protein. Nature, 608(7921), 15–16. (Cited on p. 17.)CrossRefGoogle ScholarPubMed

Cameron, A. C., and Trivedi, P. K. 2013. Regression Analysis of Count Data. 2nd edn. Econometric Society Monographs. Cambridge; New York, NY: Cambridge University Press. (Cited on pp. 159, 163.)Google Scholar

Cantwell, G. T., Kirkley, A., and Newman, M. E. J. 2021. The Friendship Paradox in Real and Model Networks. Journal of Complex Networks, 9(2), cnab011. (Cited on pp. 317, 324, 325.)Google Scholar

Cao, J., Packer, J. S., Ramani, V. et al. 2017. Comprehensive Single-Cell Transcriptional Profiling of a Multicellular Organism. Science, 357(6352), 661–667. (Cited on p. 375.)CrossRefGoogle ScholarPubMed

Cao, M., Shu, N., Cao, Q., Wang, Y., and He, Y. 2014. Imaging Functional and Structural Brain Connectomics in Attention-Deficit/Hyperactivity Disorder. Molecular Neurobiology, 50(3), 1111–1123. (Cited on p. 85.)CrossRefGoogle ScholarPubMed

Caretta Cartozo, C., and De Los Rios, P. 2009. Extended Navigability of Small World Networks: Exact Results and New Insights. Physical Review Letters, 102(23), 238703. (Cited on p. 344.)CrossRefGoogle ScholarPubMed

Carmi, S., Carter, S., Sun, J., and ben-Avraham, D. 2009. Asymptotic Behavior of the Kleinberg Model. Physical Review Letters, 102(23), 238702. (Cited on p. 344.)CrossRefGoogle Scholar

Casey, B. J., Cannonier, T., Conley, M. I. et al. 2018. The Adolescent Brain Cognitive Development (ABCD) Study: Imaging Acquisition across 21 Sites. Developmental Cognitive Neuroscience, 32(Aug.), 43–54. (Cited on pp. 19, 80.)CrossRefGoogle ScholarPubMed

Chami, I., Wolf, A., Juan, D.-C., Sala, F., Ravi, S., and Ré, C. 2020. Low-Dimensional Hyperbolic Knowledge Graph Embeddings. Pages 6901–6914 of: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Online: Association for Computational Linguistics. (Cited on p. 438.)Google Scholar

Chami, I., Abu-El-Haija, S., Perozzi, B., Ré, C., and Murphy, K. 2022. Machine Learning on Graphs: A Model and Comprehensive Taxonomy. Journal of Machine Learning Research, 23(89), 1–64. (Cited on p. 445.)Google Scholar

Cheeger, J. 1969. A Lower Bound for the Smallest Eigenvalue of the Laplacian. Pages 195–200 of: Gunning, R. C. (ed.), Problems in Analysis: A Symposium in Honor of Salomon Bochner (PMS-31). Princeton, NJ: Princeton University Press. (Cited on p. 427.)Google Scholar

Chen, B. L., Hall, D. H., and Chklovskii, D. B. 2006. Wiring Optimization Can Relate Neuronal Structure and Function. Proceedings of the National Academy of Sciences, 103(12), 4723–4728. (Cited on p. 227.)CrossRefGoogle Scholar

Chen, M., Mao, S., and Liu, Y. 2014. Big Data: A Survey. Mobile Networks and Applications, 19(2), 171–209. (Cited on p. 469.)CrossRefGoogle Scholar

Ching, A., Edunov, S., Kabiljo, M., Logothetis, D., and Muthukrishnan, S. 2015. One Trillion Edges: Graph Processing at Facebook-Scale. Proceedings of the VLDB Endowment, 8(12), 1804–1815. (Cited on pp. 448, 453.)CrossRefGoogle Scholar

Christen, P. 2012. Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Data-Centric Systems and Applications. Berlin; New York: Springer. (Cited on p. 116.)Google Scholar

Chu, C.-C., and Iu, H. H.-C. 2017. Complex Networks Theory For Modern Smart Grid Applications: A Survey. IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 7(2), 177–191. (Cited on p. 26.)CrossRefGoogle Scholar

Chung, F. 2007. Four Proofs for the Cheeger Inequality and Graph Partition Algorithms. Page 378 of: Proceedings of ICCM, vol. 2. Providence, RI; Boston, MA: American Mathematical Society & International Press of Boston, for Citeseer. (Cited on p. 427.)Google Scholar

Chung, F., and Lu, L. 2002. Connected Components in Random Graphs with Given Expected Degree Sequences. Annals of Combinatorics, 6(2), 125–145. (Cited on pp. 467, 468.)CrossRefGoogle Scholar

Chung, F. R. K. 1997. Spectral Graph Theory. Providence, RI: American Mathematical Society. (Cited on pp. 406, 413, 414, 427.)Google Scholar

Clauset, A., Shalizi, C. R., and Newman, M. E. J. 2009. Power-Law Distributions in Empirical Data. SIAM Review, 51(4), 661–703. (Cited on pp. 153, 163.)CrossRefGoogle Scholar

Cleveland, W. S., and McGill, R. 1984. Graphical Perception: Theory, Experimentation, and Application to the Development of Graphical Methods. Journal of the American Statistical Association, 79(387), 531–554. (Cited on p. 214.)CrossRefGoogle Scholar

Cohen, R., Erez, K., ben-Avraham, D., and Havlin, S. 2000. Resilience of the Internet to Random Breakdowns. Physical Review Letters, 85(21), 4626–4628. (Cited on pp. 167, 384, 386, 395.)CrossRefGoogle Scholar

Cohen, R., Erez, K., ben-Avraham, D., and Havlin, S. 2001. Breakdown of the Internet under Intentional Attack. Physical Review Letters, 86(16), 3682–3685. (Cited on p. 167.)CrossRefGoogle Scholar

Colizza, V., Flammini, A., Serrano, M. A., and Vespignani, A. 2006. Detecting Rich-Club Ordering in Complex Networks. Nature Physics, 2(2), 110–115. (Cited on p. 202.)CrossRefGoogle Scholar

Cong, J., and Liu, H. 2014. Approaching Human Language with Complex Networks. Physics of Life Reviews, 11(4), 598–618. (Cited on p. 26.)CrossRefGoogle ScholarPubMed

Conover, M. D., Gonçalves, B., Flammini, A., and Menczer, F. 2012. Partisan Asymmetries in Online Political Activity. EPJ Data Science, 1(1), 6. (Cited on p. 84.)CrossRefGoogle Scholar

Conte, D., Foggia, P., Sansone, C., and Vento, M. 2004. Thirty Years of Graph Matching in Pattern Recognition. International Journal of Pattern Recognition and Artificial Intelligence, 18(03), 265–298. (Cited on p. 232.)CrossRefGoogle Scholar

Cormen, T. H., Leiserson, C. E., Rivest, R. L., and Stein, C. 2022. Introduction to Algorithms. 4th edn. Cambridge, MA: MIT Press. (Cited on p. 61.)Google Scholar

Cutler, A. 1991. Nested Faunas and Extinction in Fragmented Habitats. Conservation Biology, 5(4), 496–504. (Cited on p. 202.)CrossRefGoogle Scholar

Dai, H., Li, H., Tian, T. et al. 2018. Adversarial Attack on Graph Structured Data. Pages 1115–1124 of: Proceedings of the 35th International Conference on Machine Learning. PMLR. (Cited on p. 444.)Google Scholar

Dalton, J. 1808. A New System of Chemical Philosophy. Part I. Cambridge: Cambridge University Press. (Cited on p. 4.)CrossRefGoogle Scholar

Dantzig, G. B., and Fulkerson, D. R. 1955. On the Max Flow Min Cut Theorem of Networks. Santa Monica, CA: RAND corporation. (Cited on pp. 201, 427.)Google Scholar

Dawid, A. P., and Skene, A. M. 1979. Maximum Likelihood Estimation of Observer Error-Rates Using the EM Algorithm. Journal of the Royal Statistical Society: Series C (Applied Statistics), 28(1), 20–28. (Cited on p. 374.)Google Scholar

de Saussure, F. 1959. Course in General Linguistics. New York, NY: Philosophical Library. (Cited on p. 432.)Google Scholar

Dean, J., and Ghemawat, S. 2008. MapReduce: Simplified Data Processing on Large Clusters. Communications of the ACM, 51(1), 107–113. (Cited on pp. 452, 453.)CrossRefGoogle Scholar

Decelle, A., Krzakala, F., Moore, C., and Zdeborová, L. 2011. Asymptotic Analysis of the Stochastic Block Model for Modular Networks and Its Algorithmic Applications. Physical Review E, 84(6), 066106. (Cited on pp. 356, 357.)CrossRefGoogle ScholarPubMed

Decelle, A., Krzakala, F., Moore, C., and Zdeborová, L. 2011. Inference and Phase Transitions in the Detection of Modules in Sparse Networks. Physical Review Letters, 107(6), 065701. (Cited on pp. 356, 357.)CrossRefGoogle ScholarPubMed

Dempster, A. P., Laird, N. M., and Rubin, D. B. 1977. Maximum Likelihood from Incomplete Data Via the EM Algorithm. Journal of the Royal Statistical Society: Series B (Methodological), 39(1), 1–22. (Cited on pp. 134, 360, 362.)Google Scholar

Dessimoz, C., and Škunca, N. (eds.). 2017. The Gene Ontology Handbook. Methods in Molecular Biology, vol. 1446. New York, NY: Springer New York. (Cited on p. 116.)CrossRefGoogle Scholar

Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Pages 4171–4186 of: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Minneapolis, MN: Association for Computational Linguistics. (Cited on p. 261.)Google Scholar

Devroye, L. 1986. Non-Uniform Random Variate Generation. New York, NY: Springer. (Cited on p. 466.)CrossRefGoogle Scholar

Dodds, P. S., Muhamad, R., and Watts, D. J. 2003. An Experimental Study of Search in Global Social Networks. Science, 301(5634), 827–829. (Cited on p. 341.)CrossRefGoogle ScholarPubMed

Donath, W. E., and Hoffman, A. J. 1973. Lower Bounds for the Partitioning of Graphs. IBM Journal of Research and Development, 17(5), 420–425. (Cited on pp. 427, 428.)CrossRefGoogle Scholar

Dong, X., Thanou, D., Rabbat, M., and Frossard, P. 2019. Learning Graphs From Data: A Signal Representation Perspective. IEEE Signal Processing Magazine, 36(3), 44–63. (Cited on p. 238.)CrossRefGoogle Scholar

Doolittle, W. F. 2013. Is Junk DNA Bunk? A Critique of ENCODE. Proceedings of the National Academy of Sciences, 110(14), 5294–5300. (Cited on p. 122.)CrossRefGoogle Scholar

Dunbar, R. I. M. 1993. Coevolution of Neocortical Size, Group Size and Language in Humans. Behavioral and Brain Sciences, 16(4), 681–694. (Cited on p. 181.)CrossRefGoogle Scholar

Dunning, T. 2012. Natural Experiments in the Social Sciences: A Design-Based Approach. Cambridge: Cambridge University Press. (Cited on p. 474.)CrossRefGoogle Scholar

Durand, M., and Flajolet, P. 2003. Loglog Counting of Large Cardinalities. Pages 605–617 of: Di Battista, G., and Zwick, U. (eds.), Algorithms—ESA 2003. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer. (Cited on pp. 455, 469.)Google Scholar

Dwork, C., and Roth, A. 2014. The Algorithmic Foundations of Differential Privacy. Foundations and Trends® in Theoretical Computer Science, 9(3–4), 211–407. (Cited on p. 475.)CrossRefGoogle Scholar

Eades, P. 1984. A Heuristic for Graph Drawing. Congressus Numerantium, vol.42, 149–160. (Cited on p. 221.)Google Scholar

Easley, D., and Kleinberg, J. 2010. Networks, Crowds, and Markets: Reasoning about a Highly Connected World. Cambridge: Cambridge University Press. (Cited on p. 472.)CrossRefGoogle Scholar

Edwards, P. N., Mayernik, M. S., Batcheller, A. L., Bowker, G. C., and Borgman, C. L. 2011. Science Friction: Data, Metadata, and Collaboration. Social Studies of Science, 41(5), 667–690. (Cited on p. 292.)CrossRefGoogle ScholarPubMed

Efron, B., and Hastie, T. 2016. Computer Age Statistical Inference. Cambridge: Cambridge University Press. (Cited on pp. 62, 277, 472.)CrossRefGoogle Scholar

Ehrlich, B. 2022. The Brain in Search of Itself: Santiago Ramón y Cajal and the Story of the Neuron. New York, NY: Farrar, Straus and Giroux. (Cited on p. 16.)Google Scholar

Einstein, A. 1905. Über Die von Der Molekularkinetischen Theorie Der Wärme Geforderte Bewegung von in Ruhenden Flüssigkeiten Suspendierten Teilchen. Annalen der Physik, 322(8), 549–560. (Cited on p. 327.)CrossRefGoogle Scholar

Ellson, J., Gansner, E., Koutsofios, L., North, S. C., and Woodhull, G. 2002. Graphviz—Open Source Graph Drawing Tools. Pages 483–484 of: Mutzel, P., Jünger, M., and Leipert, S. (eds.), Graph Drawing. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer. (Cited on p. 206.)Google Scholar

Emmons, S. W. 2015. The Beginning of Connectomics: A Commentary on White et al. (1986) ‘The Structure of the Nervous System of the Nematode Caenorhabditis Elegans’. Philosophical Transactions of the Royal Society B: Biological Sciences, 370(1666), 20140309. (Cited on p. 82.)Google Scholar

Eom, Y.-H., and Jo, H.-H. 2015. Generalized Friendship Paradox in Complex Networks: The Case of Scientific Collaboration. Scientific Reports, 4(1), 4603. (Cited on p. 324.)CrossRefGoogle Scholar

Erdős, P., and Rényi, A. 1959. On Random Graphs I. Publicationes Mathematicae, 6(1), 290–297. (Cited on pp. 163, 349.)CrossRefGoogle Scholar

Erdős, P., and Rényi, A. 1960. On the Evolution of Random Graphs. Publ. Math. Inst. Hung. Acad. Sci, 5(1), 17–60. (Cited on pp. 163, 349.)Google Scholar

Ester, M., Kriegel, H.-P., Sander, J., Xu, X., et al. 1996. A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Pages 226–231 of: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. KDD ’96, vol. 96. Portland, OR: AAAI Press. (Cited on p. 428.)Google Scholar

Eswaran, D., Faloutsos, C., Guha, S., and Mishra, N. 2018. SpotLight: Detecting Anomalies in Streaming Graphs. Pages 1378–1386 of: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. KDD ‘18. New York, NY: Association for Computing Machinery. (Cited on p. 469.)Google Scholar

Euzenat, J., and Shvaiko, P. 2013. Ontology Matching. 2nd edn. Berlin, Heidelberg: Springer. (Cited on p. 116.)CrossRefGoogle Scholar

Evans, T. S., and Lambiotte, R. 2009. Line Graphs, Link Partitions, and Overlapping Communities. Physical Review E, 80(1), 016105. (Cited on pp. 184, 201.)CrossRefGoogle ScholarPubMed

Evtushenko, A., and Kleinberg, J. 2023. Node-Based Generalized Friendship Paradox Fails. Scientific Reports, 13(1), 2074. (Cited on p. 324.)CrossRefGoogle ScholarPubMed

Fan, J., Han, F., and Liu, H. 2014. Challenges of Big Data Analysis. National Science Review, 1(2), 293–314. (Cited on p. 469.)CrossRefGoogle ScholarPubMed

Feld, S. L. 1991. Why Your Friends Have More Friends Than You Do. American Journal of Sociology, 96(6), 1464–1477. (Cited on p. 324.)CrossRefGoogle Scholar

Ferguson, N. 2019. The Square and the Tower: Networks and Power, from the Freemasons to Facebook. New York, NY: Penguin. (Cited on p. 16.)Google Scholar

Fiedler, M. 1973. Algebraic Connectivity of Graphs. Czechoslovak Mathematical Journal, 23(2), 298–305. (Cited on pp. 412, 427, 428.)CrossRefGoogle Scholar

Firth, J. R. 1957. Studies in Linguistic Analysis. Publications of the Philological Society. Oxford: Blackwell. (Cited on pp. 272, 432.)Google Scholar

Flajolet, P., and Nigel Martin, G. 1985. Probabilistic Counting Algorithms for Data Base Applications. Journal of Computer and System Sciences, 31(2), 182–209. (Cited on pp. 455, 469.)CrossRefGoogle Scholar

Flajolet, P., Fusy, É., Gandouet, O., and Meunier, F. 2007. HyperLogLog: The Analysis of a near-Optimal Cardinality Estimation Algorithm. Pages 137–156 of: Discrete Mathematics and Theoretical Computer Science, vol. AH. Nancy, France: Discrete Mathematics and Theoretical Computer Science. (Cited on pp. 455, 456, 469.)CrossRefGoogle Scholar

Ford, L. R., and Fulkerson, D. R. 1956. Maximal Flow Through a Network. Canadian Journal of Mathematics, 8, 399–404. (Cited on p. 201.)CrossRefGoogle Scholar

Ford, L. R., and Fulkerson, D. R. 1962. Flows in Networks. Princeton, NJ: Princeton University Press. (Cited on p. 201.)Google Scholar

Fortunato, S. 2010. Community Detection in Graphs. Physics Reports, 486(3), 75–174. (Cited on p. 201.)CrossRefGoogle Scholar

Fortunato, S., and Barthélemy, M. 2007. Resolution Limit in Community Detection. Proceedings of the National Academy of Sciences, 104(1), 36–41. (Cited on p. 201.)CrossRefGoogle Scholar

Fortunato, S., and Newman, M. E. J. 2022. 20 Years of Network Community Detection. Nature Physics, 18(8), 848–850. (Cited on p. 201.)CrossRefGoogle Scholar

Fosdick, B. K., Larremore, D. B., Nishimura, J., and Ugander, J. 2018. Configuring Random Graph Models with Fixed Degree Sequences. SIAM Review, 60(2), 315–355. (Cited on p. 163.)CrossRefGoogle Scholar

Frank, O., and Strauss, D. 1986. Markov Graphs. Journal of the American Statistical Association, 81(395), 832–842. (Cited on pp. 163, 367.)Google Scholar

Frantz, T. L., Cataldo, M., and Carley, K. M. 2009. Robustness of Centrality Measures under Uncertainty: Examining the Role of Network Topology. Computational and Mathematical Organization Theory, 15(4), 303–328. (Cited on pp. 377, 395.)CrossRefGoogle Scholar

Friedman, J., Hastie, T., and Tibshirani, R. 2008. Sparse Inverse Covariance Estimation with the Graphical Lasso. Biostatistics, 9(3), 432–441. (Cited on pp. 373, 374.)CrossRefGoogle ScholarPubMed

Fruchterman, T. M. J., and Reingold, E. M. 1991. Graph Drawing by Force-Directed Placement. Software: Practice and Experience, 21(11), 1129–1164. (Cited on p. 221.)Google Scholar

Gao, X., Xiao, B., Tao, D., and Li, X. 2010. A Survey of Graph Edit Distance. Pattern Analysis and Applications, 13(1), 113–129. (Cited on p. 232.)CrossRefGoogle Scholar

Garcia, D. 2017. Leaking Privacy and Shadow Profiles in Online Social Networks. Science Advances, 3(8), e1701172. (Cited on p. 37.)CrossRefGoogle ScholarPubMed

Garey, M., Johnson, D., and Stockmeyer, L. 1976. Some Simplified NP-complete Graph Problems. Theoretical Computer Science, 1(3), 237–267. (Cited on p. 410.)CrossRefGoogle Scholar

Gauvin, L., Génois, M., Karsai, M. et al. 2022. Randomized Reference Models for Temporal Networks. SIAM Review, 64(4), 763–830. (Cited on p. 243.)CrossRefGoogle Scholar

Gawande, A. 2010. The Checklist Manifesto: How to Get Things Right. New York, NY: Metropolitan Books. (Cited on p. 285.)Google Scholar

Gebru, T., Morgenstern, J., Vecchione, B. et al. 2020. Datasheets for Datasets. arXiv:1803.09010 [cs], Mar. (Cited on pp. 31, 474.)Google Scholar

Gelman, A., and Hill, J. 2007. Data Analysis Using Regression and Multi-level/Hierarchical Models. Analytical Methods for Social Research. Cambridge; New York: Cambridge University Press. (Cited on pp. 116, 134.)Google Scholar

Gelman, A., Carlin, Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. 2014. Bayesian Data Analysis. 3rd edn. Chapman & Hall/CRC Texts in Statistical Science. Boca Raton, FL: CRC Press. (Cited on pp. 62, 375, 472.)Google Scholar

Gertner, J. 2013. The Idea Factory: Bell Labs and the Great Age of American Innovation. London: Penguin Books. (Cited on p. 313.)Google Scholar

Ghosh, S., Das, N., Gonçalves, T., Quaresma, P., and Kundu, M. 2018. The Journey of Graph Kernels through Two Decades. Computer Science Review, 27(Feb.), 88–111. (Cited on p. 232.)CrossRefGoogle Scholar

Gilbert, E. N. 1959. Random Graphs. The Annals of Mathematical Statistics, 30(4), 1141–1144. (Cited on pp. 163, 349.)CrossRefGoogle Scholar

Girvan, M., and Newman, M. E. J. 2002. Community Structure in Social and Biological Networks. Proceedings of the National Academy of Sciences, 99(12), 7821–7826. (Cited on pp. 185, 201.)CrossRefGoogle Scholar

Golub, G. H., and Van Loan, C. F. 2013. Matrix Computations. 4th edn. Johns Hopkins Studies in the Mathematical Sciences. Baltimore, MD: The Johns Hopkins University Press. (Cited on pp. 400, 427.)Google Scholar

Gonzalez, J. E., Low, Y., Gu, H., Bickson, D., and Guestrin, C. 2012. Power-Graph: Distributed Graph-Parallel Computation on Natural Graphs. Pages 17–30 of: Proceedings of the 10th USENIX Conference on Operating Systems Design and Implementation. OSDI’12. Hollywood, CA: USENIX Association. (Cited on p. 453.)Google Scholar

Goodfellow, I., Bengio, Y., and Courville, A. 2016. Deep Learning. Cambridge, MA: MIT Press. (Cited on pp. 277, 472.)Google Scholar

Gower, J. C. 1975. Generalized Procrustes Analysis. Psychometrika, 40(1), 33–51. (Cited on p. 371.)CrossRefGoogle Scholar

Goyal, P., and Ferrara, E. 2018. Graph Embedding Techniques, Applications, and Performance: A Survey. Knowledge-Based Systems, 151(July), 78–94. (Cited on p. 445.)CrossRefGoogle Scholar

Granovetter, M. 1976. Network Sampling: Some First Steps. American Journal of Sociology, 81(6), 1287–1303. (Cited on p. 395.)CrossRefGoogle Scholar

Granovetter, M. S. 1973. The Strength of Weak Ties. American Journal of Sociology, 78(6), 1360–1380. (Cited on pp. 21, 343.)CrossRefGoogle Scholar

Gray, J., Liu, D. T., Nieto-Santisteban, M., Szalay, A., DeWitt, D. J., and Heber, G. 2005. Scientific Data Management in the Coming Decade. ACM SIGMOD Record, 34(4), 34–41. (Cited on p. 292.)CrossRefGoogle Scholar

Grohe, M., and Schweitzer, P. 2020. The Graph Isomorphism Problem. Com-munications of the ACM, 63(11), 128–134. (Cited on p. 445.)CrossRefGoogle Scholar

Grover, A., and Leskovec, J. 2016. Node2vec: Scalable Feature Learning for Networks. Pages 855–864 of: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16. New York, NY: Association for Computing Machinery. (Cited on p. 435.)Google Scholar

Groves, R. M. 2011. Three Eras of Survey Research. Public Opinion Quarterly, 75(5), 861–871. (Cited on pp. 65, 68, 71.)CrossRefGoogle Scholar

Guimerà, R., and Sales-Pardo, M. 2009. Missing and Spurious Interactions and the Reconstruction of Complex Networks. Proceedings of the National Academy of Sciences, 106(52), 22073–22078. (Cited on p. 134.)CrossRefGoogle Scholar

Guimerà, R., Sales-Pardo, M., and Amaral, L. A. N. 2004. Modularity from Fluctuations in Random Graphs and Complex Networks. Physical Review E, 70(2), 025101. (Cited on p. 179.)CrossRefGoogle ScholarPubMed

Gutmann, M., and Hyvärinen, A. 2010. Noise-Contrastive Estimation: A New Estimation Principle for Unnormalized Statistical Models. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research. Sardinia, Italy: PMLR. (Cited on p. 435.)Google Scholar

Hagberg, A., and Lemons, N. 2015. Fast Generation of Sparse Random Kernel Graphs. PLOS ONE, 10(9), e0135177. (Cited on p. 469.)CrossRefGoogle ScholarPubMed

Hall, K. M. 1970. An R-Dimensional Quadratic Placement Algorithm. Management Science, 17(3), 219–229. (Cited on p. 221.)CrossRefGoogle Scholar

Hamilton, W., Ying, Z., and Leskovec, J. 2017. Inductive Representation Learning on Large Graphs. In: Guyon, I., Luxburg, U. V., Bengio, S. et al. (eds.), Advances in Neural Information Processing Systems, vol. 30. Long Beach, CA: Curran Associates, Inc. (Cited on pp. 273, 442.)Google Scholar

Hanel, R., Corominas-Murtra, B., Liu, B., and Thurner, S. 2017. Fitting Power-Laws in Empirical Data with Estimators That Work for All Exponents. PLOS ONE, 12(2), e0170920. (Cited on p. 163.)CrossRefGoogle ScholarPubMed

Harris, Z. S. 1954. Distributional Structure. WORD, 10(2-3), 146–162. (Cited on pp. 272, 432.)CrossRefGoogle Scholar

Hartle, H., Klein, B., McCabe, S. et al. 2020. Network Comparison and the Within-Ensemble Graph Distance. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 476(2243), 20190744. (Cited on p. 232.)CrossRefGoogle Scholar

Hashimoto, K.-i. 1989. Zeta Functions of Finite Graphs and Representations of P-Adic Groups. Pages 211–280 of: Automorphic Forms and Geometry of Arithmetic Varieties. Advanced Studies in Pure Mathematics, vol. 15. Cambridge, MA: Academic Press. (Cited on pp. 357, 403.)CrossRefGoogle Scholar

Hastie, T., Tibshirani, R., and Friedman, J. H. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction. 2nd edn. Springer Series in Statistics. New York, NY: Springer. (Cited on pp. 277, 428, 472.)Google Scholar

Hastie, T., Tibshirani, R., and Wainwright, M. 2015. Statistical Learning with Sparsity: The Lasso and Generalizations. New York, NY: Taylor & Francis. (Cited on pp. 277, 375.)CrossRefGoogle Scholar

Head, M. L., Holman, L., Lanfear, R., Kahn, A. T., and Jennions, M. D. 2015. The Extent and Consequences of P-Hacking in Science. PLOS Biology, 13(3), e1002106. (Cited on pp. 35, 71.)CrossRefGoogle ScholarPubMed

Healy, K. 2018. Data Visualization: A Practical Introduction. Princeton, NJ: Princeton University Press. (Cited on p. 473.)Google Scholar

Heinrich, J. 2001. Drug Safety: Most Drugs Withdrawn in Recent Years Had Greater Health Risks for Women. Tech. rept. GAO-01-286R. US Government Printing Office, Washington, DC. (Cited on p. 28.)Google Scholar

Hellman, M. 2002. An Overview of Public Key Cryptography. IEEE Communications Magazine, 40(5), 42–49. (Cited on p. 313.)CrossRefGoogle Scholar

Hennig, C., Meila, M., Murtagh, F., and Rocci, R. (eds.). 2016. Handbook of Cluster Analysis. Chapman & Hall/CRC Handbooks of Modern Statistical Methods. Boca Raton, FL; London; New York: CRC Press, Taylor & Francis Group. (Cited on p. 428.)Google Scholar

Heule, S., Nunkesser, M., and Hall, A. 2013. HyperLogLog in Practice: Algorithmic Engineering of a State of the Art Cardinality Estimation Algorithm. Pages 683–692 of: Proceedings of the 16th International Conference on Extending Database Technology. EDBT ’13. New York, NY: Association for Computing Machinery. (Cited on p. 469.)Google Scholar

Hočevar, T., and Demšar, J. 2014. A Combinatorial Approach to Graphlet Counting. Bioinformatics, 30(4), 559–565. (Cited on p. 175.)CrossRefGoogle ScholarPubMed

Hodas, N., Kooti, F., and Lerman, K. 2013. Friendship Paradox Redux: Your Friends Are More Interesting Than You. Proceedings of the International AAAI Conference on Web and Social Media, 7(1), 225–233. (Cited on p. 324.)Google Scholar

Hofer, M., Hellmann, S., Dojchinovski, M., and Frey, J. 2020. The New DBpedia Release Cycle: Increasing Agility and Efficiency in Knowledge Extraction Workflows. Pages 1–18 of: Blomqvist, E., Groth, P., de Boer, V. et al. (eds.), Semantic Systems. In the Era of Knowledge Graphs. Lecture Notes in Computer Science. Cham, Switzerland: Springer. (Cited on p. 449.)Google Scholar

Hoff, P. D., Raftery, A. E., and Handcock, M. S. 2002. Latent Space Approaches to Social Network Analysis. Journal of the American Statistical Association, 97(460), 1090–1098. (Cited on pp. 369, 370, 371, 374.)CrossRefGoogle Scholar

Hofman, J. M., Watts, D. J., Athey, S. et al. 2021. Integrating Explanation and Prediction in Computational Social Science. Nature, 595(7866), 181–188. (Cited on p. 474.)CrossRefGoogle ScholarPubMed

Holland, P. W., and Leinhardt, S. 1981. An Exponential Family of Probability Distributions for Directed Graphs. Journal of the American Statistical Association, 76(373), 33–50. (Cited on p. 163.)CrossRefGoogle Scholar

Holland, P. W., Laskey, K. B., and Leinhardt, S. 1983. Stochastic Blockmodels: First Steps. Social Networks, 5(2), 109–137. (Cited on pp. 163, 374.)CrossRefGoogle Scholar

Holme, P. 2019. Rare and Everywhere: Perspectives on Scale-Free Networks. Nature Communications, 10(1), 1016. (Cited on p. 346.)Google Scholar

Holme, P., and Saramäki, J. 2012. Temporal Networks. Physics Reports, 519(3), 97–125. (Cited on pp. 249, 472.)CrossRefGoogle Scholar

Holme, P., and Saramäki, J. (eds.). 2019. Temporal Network Theory. Computational Social Science. Cham, Switzerland: Springer Naure. (Cited on p. 249.)Google Scholar

Holten, D., and Van Wijk, J. J. 2009. Force-Directed Edge Bundling for Graph Visualization. Computer Graphics Forum, 28(3), 983–990. (Cited on p. 213.)CrossRefGoogle Scholar

Hosmer, D. W., Lemeshow, S., and Sturdivant, R. X. 2013. Applied Logistic Regression. Hoboken, NJ: John Wiley & Sons. (Cited on p. 256.)CrossRefGoogle Scholar

Howe, J., et al. 2006. The Rise of Crowdsourcing. Wired magazine, 14(6), 1–4. (Cited on p. 374.)Google Scholar

Humphreys, I. R., Pei, J., Baek, M. et al. 2021. Computed Structures of Core Eukaryotic Protein Complexes. Science, 374(6573), eabm4805. (Cited on p. 17.)CrossRefGoogle ScholarPubMed

Hyndman, R. J., and Athanasopoulos, G. 2021. Forecasting: Principles and Practice. 3rd edn. Melbourne, Australia: Otexts, Online Open-Access Textbooks. (Cited on p. 249.)Google Scholar

Israel, M., and Hay, I. 2006. Research Ethics for Social Scientists. Thousand Oaks, CA: SAGE. (Cited on p. 37.)CrossRefGoogle Scholar

Jackson, M. O. 2019. The Friendship Paradox and Systematic Biases in Perceptions and Social Norms. Journal of Political Economy, 127(2), 777–818. (Cited on p. 325.)CrossRefGoogle Scholar

James, G., Witten, D., Hastie, T., and Tibshirani, R. 2021. An Introduction to Statistical Learning: With Applications in R. 2nd edn. Springer Texts in Statistics. New York, NY: Springer. (Cited on pp. 277, 472.)Google Scholar

Jha, M., Seshadhri, C., and Pinar, A. 2015. A Space-Efficient Streaming Algorithm for Estimating Transitivity and Triangle Counts Using the Birthday Paradox. ACM Transactions on Knowledge Discovery from Data, 9(3), 15:1–15:21. (Cited on p. 454.)CrossRefGoogle Scholar

Kanare, H. M. 1985. Writing the Laboratory Notebook. Washington, DC: American Chemical Society. (Cited on pp. 283, 287.)Google Scholar

Kanungo, T., Mount, D., Netanyahu, N., Piatko, C., Silverman, R., and Wu, A. 2002. An Efficient K-Means Clustering Algorithm: Analysis and Implementation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7), 881–892. (Cited on p. 426.)CrossRefGoogle Scholar

Karger, D., Oh, S., and Shah, D. 2011. Iterative Learning for Reliable Crowd-sourcing Systems. In: Advances in Neural Information Processing Systems, vol. 24. Granada, Spain: Curran Associates, Inc. (Cited on p. 374.)Google Scholar

Karinthy, F. 1929. Chain-Links. Everything is different, 21–26. (Cited on p. 15.)CrossRefGoogle Scholar

Karrer, B., and Newman, M. E. J. 2011. Stochastic Blockmodels and Community Structure in Networks. Physical Review E, 83(1), 016107. (Cited on p. 355.)CrossRefGoogle ScholarPubMed

Katz, L. 1953. A New Status Index Derived from Sociometric Analysis. Psy-chometrika, 18(1), 39–43. (Cited on pp. 189, 408, 427.)CrossRefGoogle Scholar

Kaufman, L., and Rousseeuw, P. J. 2009. Finding Groups in Data: An Introduction to Cluster Analysis. New York, NY: John Wiley & Sons. (Cited on pp. 232, 428.)Google Scholar

Kernighan, B. W., and Lin, S. 1970. An Efficient Heuristic Procedure for Partitioning Graphs. The Bell System Technical Journal, 49(2), 291–307. (Cited on pp. 201, 427.)CrossRefGoogle Scholar

Kernighan, B. W. 2020. UNIX: A History and a Memoir. Seattle, WA: Kindle Direct Publishing. (Cited on p. 313.)Google Scholar

Kessler, M. D., Yerges-Armstrong, L., Taub, M. A. et al. 2016. Challenges and Disparities in the Application of Personalized Genomic Medicine to Populations with African Ancestry. Nature Communications, 7(1), 12521. (Cited on p. 28.)CrossRefGoogle ScholarPubMed

Kipf, T. N., and Welling, M. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In: International Conference on Learning Representations. Toulon, France: OpenReview.net. (Cited on pp. 273, 442.)Google Scholar

Kirchgässner, G., Wolters, J., and Hassler, U. 2013. Introduction to Modern Time Series Analysis. 2nd edn. Springer Texts in Business and Economics. Berlin, Heidelberg: Springer. (Cited on p. 249.)Google Scholar

Kleinberg, J. M. 1999. Authoritative Sources in a Hyperlinked Environment. Journal of the ACM, 46(5), 604–632. (Cited on p. 189.)CrossRefGoogle Scholar

Kleinberg, J. M. 2000. Navigation in a Small World. Nature, 406(6798), 845–845. (Cited on p. 344.)Google Scholar

Kleinberg, J. M., Kumar, R., Raghavan, P., Rajagopalan, S., and Tomkins, A. S. 1999. The Web as a Graph: Measurements, Models, and Methods. Pages 1–17 of: Goos, G., Hartmanis, J., van Leeuwen, J. et al. (eds.), Computing and Combinatorics, vol. 1627. Berlin, Heidelberg: Springer. (Cited on pp. 346, 349.)Google Scholar

Knuth, D. E. 1984. Literate Programming. The Computer Journal, 27(2), 97–111. (Cited on p. 301.)CrossRefGoogle Scholar

Kojaku, S., and Masuda, N. 2017. Finding Multiple Core-Periphery Pairs in Networks. Physical Review E, 96(5), 052313. (Cited on p. 195.)CrossRefGoogle ScholarPubMed

Kojaku, S., Yoon, J., Constantino, I., and Ahn, Y.-Y. 2021. Residual2Vec: Debi-asing Graph Embedding with Random Graphs. Pages 24150–24163 of: Advances in Neural Information Processing Systems, vol. 34. Online: Curran Associates, Inc. (Cited on pp. 435, 445.)Google Scholar

Koponen, J., and Hildén, J. 2019. Data Visualization Handbook. Aalto, Finland: Aalto University. (Cited on p. 473.)Google Scholar

Kosinski, M., Stillwell, D., and Graepel, T. 2013. Private Traits and Attributes Are Predictable from Digital Records of Human Behavior. Proceedings of the National Academy of Sciences, 110(15), 5802–5805. (Cited on p. 32.)CrossRefGoogle Scholar

Köster, J., and Rahmann, S. 2012. Snakemake—a Scalable Bioinformatics Workflow Engine. Bioinformatics, 28(19), 2520–2522. (Cited on p. 302.)CrossRefGoogle Scholar

Krapivsky, P. L., and Redner, S. 2001. Organization of Growing Random Networks. Physical Review E, 63(6), 066123. (Cited on pp. 345, 346, 348, 349.)CrossRefGoogle Scholar

Krioukov, D., Papadopoulos, F., Kitsak, M., Vahdat, A., and Boguñá, M. 2010. Hyperbolic Geometry of Complex Networks. Physical Review E, 82(3), 036106. (Cited on p. 438.)CrossRefGoogle ScholarPubMed

Kruja, E., Marks, J., Blair, A., and Waters, R. 2002. A Short Note on the History of Graph Drawing. Pages 272–286 of: Mutzel, P., Jünger, M., and Leipert, S. (eds.), Graph Drawing. Lecture Notes in Computer Science. Berlin, Heidelberg: Springer. (Cited on p. 221.)Google Scholar

Kruschke, J. K. 2015. Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan. 2nd edn. Boston, MA: Academic Press. (Cited on pp. 62, 472.)Google Scholar

Kruskal, J. B., and Wish, M. 1978. Multidimensional Scaling. Sage University Papers Quantitative Applications in the Social Sciences, no. 11. Newbury Park, CA: SAGE. (Cited on p. 370.)Google Scholar

Kryshtafovych, A., Schwede, T., Topf, M., Fidelis, K., and Moult, J. 2021. Critical Assessment of Methods of Protein Structure Prediction (CASP)—Round XIV. Proteins: Structure, Function, and Bioinformatics, 89(12), 1607–1617. (Cited on p. 17.)CrossRefGoogle ScholarPubMed

Krzakala, F., Moore, C., Mossel, E. et al. 2013. Spectral Redemption in Clustering Sparse Networks. Proceedings of the National Academy of Sciences, 110(52), 20935–20940. (Cited on pp. 356, 357, 421.)CrossRefGoogle Scholar

Kumar, S., Morstatter, F., and Liu, H. 2014. Twitter Data Analytics. Springer-Briefs in Computer Science. New York, NY: Springer. (Cited on p. 71.)Google Scholar

Landauer, T. K., Foltz, P. W., and Laham, D. 1998. An Introduction to Latent Semantic Analysis. Discourse Processes, 25(2-3), 259–284. (Cited on pp. 438, 445.)CrossRefGoogle Scholar

Lang, T. A., and Altman, D. G. 2015. Basic Statistical Reporting for Articles Published in Biomedical Journals: The “Statistical Analyses and Methods in the Published Literature” or the SAMPL Guidelines. International Journal of Nursing Studies, 52(1), 5–9. (Cited on p. 163.)CrossRefGoogle ScholarPubMed

Latapy, M., Viard, T., and Magnien, C. 2018. Stream Graphs and Link Streams for the Modeling of Interactions over Time. Social Network Analysis and Mining, 8(1), 61. (Cited on p. 240.)CrossRefGoogle Scholar

Lazer, D., Pentland, A., Adamic, L. et al. 2009. Computational Social Science. Science, 323(5915), 721–723. (Cited on p. 25.)CrossRefGoogle ScholarPubMed

Lazer, D. M. J., Pentland, A., Watts, D. J. et al. 2020. Computational Social Science: Obstacles and Opportunities. Science, 369(6507), 1060–1062. (Cited on p. 474.)CrossRefGoogle ScholarPubMed

Lee, C., and Wilkinson, D. J. 2019. A Review of Stochastic Block Models and Extensions for Graph Clustering. Applied Network Science, 4(1), 1–50. (Cited on pp. 353, 374.)CrossRefGoogle Scholar

Lee, J. R., Gharan, S. O., and Trevisan, L. 2014. Multiway Spectral Partitioning and Higher-Order Cheeger Inequalities. Journal of the ACM, 61(6), 1–30. (Cited on pp. 417, 427.)CrossRefGoogle Scholar

Lehmann, S. 2019. Fundamental Structures in Temporal Communication Networks. Pages 25–48 of: Holme, P., and Saramäki, J. (eds.), Temporal Network Theory. Computational Social Sciences. Cham, Switzerland: Springer. (Cited on p. 473.)Google Scholar

Levy, O., and Goldberg, Y. 2014. Neural Word Embedding as Implicit Matrix Factorization. In: Proceedings of the 27th International Conference on Neural Information Processing Systems, vol. 2. Montreal, Canada: Curran Associates, Inc. (Cited on pp. 272, 438, 439, 440, 445.)Google Scholar

Levy, O., Goldberg, Y., and Dagan, I. 2015. Improving Distributional Similarity with Lessons Learned from Word Embeddings. Transactions of the Association for Computational Linguistics, 3(Dec.), 211–225. (Cited on p. 272.)CrossRefGoogle Scholar

Lewandowsky, S., Mann, M. E., Bauld, L., Hastings, G., and Loftus, a. E. F. 2013. The Subterranean War on Science. APS Observer, 26. (Cited on p. 284.)Google Scholar

Lewis, S. 2019. The Racial Bias Built into Photography. The New York Times, Apr. (Cited on p. 29.)Google Scholar

Liben-Nowell, D., and Kleinberg, J. 2007. The Link-Prediction Problem for Social Networks. Journal of the American Society for Information Science and Technology, 58(7), 1019–1031. (Cited on p. 134.)CrossRefGoogle Scholar

Liggett, T. M. 1999. Stochastic Interacting Systems: Contact, Voter, and Exclusion Processes. Grundlehren Der Mathematischen Wissenschaften, no. 324. Berlin; New York: Springer. (Cited on p. 235.)Google Scholar

Liggett, T. M. 2005. Interacting Particle Systems. Classics in Mathematics. Berlin; New York: Springer. (Cited on p. 235.)Google Scholar

Lin, J. W.-B., Aizenman, H., Espinel, E. M. C., Gunnerson, K. N., and Liu, J. 2022. An Introduction to Python Programming for Scientists and Engineers. Cambridge; New York, NY: Cambridge University Press. (Cited on p. 61.)Google Scholar

Lin, T., Wang, Y., Liu, X., and Qiu, X. 2022. A Survey of Transformers. AI Open, 3(Jan.), 111–132. (Cited on p. 433.)CrossRefGoogle Scholar

Liu, Y., Safavi, T., Dighe, A., and Koutra, D. 2018. Graph Summarization Methods and Applications: A Survey. ACM Computing Surveys, 51(3), 62:1–62:34. (Cited on pp. 232, 452, 469.)Google Scholar

Louis, A., Raghavendra, P., Tetali, P., and Vempala, S. 2012. Many Sparse Cuts via Higher Eigenvalues. Pages 1131–1140 of: Proceedings of the Forty-Fourth Annual ACM Symposium on Theory of Computing. New York, NY: Association for Computing Machinery. (Cited on pp. 417, 427.)Google Scholar

Low, Y., Gonzalez, J., Kyrola, A., Bickson, D., Guestrin, C., and Hellerstein, J. 2010. GraphLab: A New Framework for Parallel Machine Learning. Pages 340–349 of: Proceedings of the Twenty-Sixth Conference on Uncertainty in Artificial Intelligence. UAI’10. Arlington, VA: AUAI Press. (Cited on p. 453.)Google Scholar

Low, Y., Bickson, D., Gonzalez, J., Guestrin, C., Kyrola, A., and Hellerstein, J. M. 2012. Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud. Proceedings of the VLDB Endowment, 5(8), 716–727. (Cited on p. 453.)CrossRefGoogle Scholar

Lü, L., and Zhou, T. 2011. Link Prediction in Complex Networks: A Survey. Physica A: Statistical Mechanics and its Applications, 390(6), 1150–1170. (Cited on p. 134.)CrossRefGoogle Scholar

Luck, K., Kim, D.-K., Lambourne, L. et al. 2020. A Reference Map of the Human Binary Protein Interactome. Nature, 580(7803), 402–408. (Cited on pp. 24, 84, 130, 389, 390.)Google Scholar

Lusher, D., Koskinen, J., and Robins, G. (eds.). 2013. Exponential Random Graph Models for Social Networks: Theory, Methods, and Applications. Structural Analysis in the Social Sciences, no. 32. Cambridge: Cambridge University Press. (Cited on p. 163.)Google Scholar

Macleod, M., Collings, A. M., Graf, C. et al. 2021. The MDAR (Materials Design Analysis Reporting) Framework for Transparent Reporting in the Life Sciences. Proceedings of the National Academy of Sciences, 118(17), e2103238118. (Cited on p. 163.)CrossRefGoogle Scholar

Madden, S. 2012. From Databases to Big Data. IEEE Internet Computing, 16(3), 4–6. (Cited on p. 469.)CrossRefGoogle Scholar

Makalowski, W. 2003. Not Junk After All. Science, 300(5623), 1246–1247. (Cited on p. 122.)Google Scholar

Makin, T. R., and Orban de Xivry, J.-J. 2019. Ten Common Statistical Mistakes to Watch out for When Writing or Reviewing a Manuscript. eLife, 8(Oct.), e48175. (Cited on p. 163.)Google ScholarPubMed

Malewicz, G., Austern, M. H., Bik, A. J. et al. 2010. Pregel: A System for Large-Scale Graph Processing. Pages 135–146 of: Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data. SIGMOD ’10. New York, NY: Association for Computing Machinery. (Cited on p. 453.)Google Scholar

Manzoor, E., Milajerdi, S. M., and Akoglu, L. 2016. Fast Memory-efficient Anomaly Detection in Streaming Heterogeneous Graphs. Pages 1035–1044 of: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’16. New York, NY: Association for Computing Machinery. (Cited on p. 469.)Google Scholar

Marai, G. E., Pinaud, B., Bühler, K., Lex, A., and Morris, J. H. 2019. Ten Simple Rules to Create Biological Network Figures for Communication. PLOS Computational Biology, 15(9), e1007244. (Cited on p. 221.)CrossRefGoogle ScholarPubMed

Mariani, M. S., Ren, Z.-M., Bascompte, J., and Tessone, C. J. 2019. Nestedness in Complex Networks: Observation, Emergence, and Implications. Physics Reports, 813(June), 1–90. (Cited on pp. 197, 198, 202.)CrossRefGoogle Scholar

Maron, H., Ben-Hamu, H., Shamir, N., and Lipman, Y. 2019. Invariant and Equivariant Graph Networks. In: International Conference on Learning Representations. New Orleans, LA: OpenReview.net. (Cited on p. 443.)Google Scholar

Martin, C., and Niemeyer, P. 2019. Influence of Measurement Errors on Networks: Estimating the Robustness of Centrality Measures. Network Science, 7(2), 180–195. (Cited on pp. 377, 395.)CrossRefGoogle Scholar

Martin, T., Ball, B., and Newman, M. E. J. 2016. Structural Inference for Uncertain Networks. Physical Review E, 93(1), 012306. (Cited on p. 134.)CrossRefGoogle ScholarPubMed

Marx, V. 2013. The Big Challenges of Big Data. Nature, 498(7453), 255–260. (Cited on p. 469.)Google Scholar

Masuda, N., and Lambiotte, R. 2020. A Guide to Temporal Networks. 2nd edn. Series on Complexity Science, vol. 06. London: World Scientific. (Cited on pp. 238, 240, 241, 242, 249, 472.)Google Scholar

Masuda, N., Porter, M. A., and Lambiotte, R. 2017. Random Walks and Diffusion on Networks. Physics Reports, 716–717(Nov.), 1–58. (Cited on p. 428.)CrossRefGoogle Scholar

Matejka, J., and Fitzmaurice, G. 2017. Same Stats, Different Graphs: Generating Datasets with Varied Appearance and Identical Statistics through Simulated Annealing. Pages 1290–1294 of: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems. Denver, CO: Association for Computing Machinery. (Cited on p. 203.)Google Scholar

McGregor, A. 2014. Graph Stream Algorithms: A Survey. ACM SIGMOD Record, 43(1), 9–20. (Cited on pp. 453, 454, 469.)CrossRefGoogle Scholar

McGregor, A., Vorotnikova, S., and Vu, H. T. 2016. Better Algorithms for Counting Triangles in Data Streams. Pages 401–411 of: Proceedings of the 35th ACM SIGMOD-SIGACT-SIGAI Symposium on Principles of Database Systems. San Francisco, CA: Association for Computing Machinery. (Cited on pp. 454, 469.)Google Scholar

McInnes, L., Healy, J., and Melville, J. 2018. UMAP: Uniform Manifold Approx-imation and Projection for Dimension Reduction. arXiv:1802.03426. (Cited on p. 259.)Google Scholar

Mead, C., and Conway, L. 1980. Introduction to VLSI Systems. Reading, MA: Addison-Wesley. (Cited on p. 26.)Google Scholar

Meinshausen, N., and Bühlmann, P. 2006. High-Dimensional Graphs and Variable Selection with the Lasso. The Annals of Statistics, 34(3). (Cited on p. 371.)CrossRefGoogle Scholar

Menczer, F., Fortunato, S., and Davis, C. A. 2020. A First Course in Network Science. Cambridge: Cambridge University Press. (Cited on pp. 16, 472.)CrossRefGoogle Scholar

Meusel, R., Vigna, S., Lehmberg, O., and Bizer, C. 2014. Graph Structure in the Web—Revisited: A Trick of the Heavy Tail. Pages 427–432 of: Proceedings of the 23rd International Conference on World Wide Web. WWW ’14 Companion. New York, NY: Association for Computing Machinery. (Cited on p. 448.)Google Scholar

Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., and Dean, J. 2013. Distributed Representations of Words and Phrases and Their Compositionality. In: Advances in Neural Information Processing Systems, vol. 26. Lake Tahoe, NV: Curran Associates, Inc. (Cited on pp. 272, 431, 434, 445.)Google Scholar

Milgram, S. 1963. Behavioral Study of Obedience. The Journal of Abnormal and Social Psychology, 67, 371–378. (Cited on p. 341.)CrossRefGoogle ScholarPubMed

Milgram, S. 1967. The Small-World Problem. Psychology Today, 1, 61. (Cited on pp. 15, 192, 458.)Google Scholar

Miller, J. C., and Hagberg, A. 2011. Efficient Generation of Networks with Given Expected Degrees. Pages 115–126 of: Frieze, A., Horn, P., and Prałat, P. (eds.), Algorithms and Models for the Web Graph, vol. 6732. Berlin, Heidelberg: Springer. (Cited on pp. 468, 469.)Google Scholar

Milo, R., Shen-Orr, S., Itzkovitz, S., Kashtan, N., Chklovskii, D., and Alon, U. 2002. Network Motifs: Simple Building Blocks of Complex Networks. Science, 298(5594), 824–827. (Cited on p. 201.)CrossRefGoogle ScholarPubMed

Mitchell, T. M. 1997. Machine Learning. New York, NY: McGraw-Hill. (Cited on pp. 254, 472.)Google Scholar

Molloy, M., and Reed, B. 1995. A Critical Point for Random Graphs With a Given Degree Sequence. Random Structures & Algorithms, 6(2-3), 161–180. (Cited on pp. 163, 333.)CrossRefGoogle Scholar

Molloy, M., and Reed, B. 1998. The Size of the Giant Component of a Random Graph with a Given Degree Sequence. Combinatorics, Probability and Computing, 7(3), 295–305. (Cited on pp. 163, 333.)CrossRefGoogle Scholar

Molnar, C. 2020. Interpretable Machine Learning. Morrisville, NC: Lulu Press, Inc. (Cited on p. 275.)Google Scholar

Moore, P. B., Hendrickson, W. A., Henderson, R., and Brunger, A. T. 2022. The Protein-Folding Problem: Not Yet Solved. Science, 375(6580), 507–507. (Cited on p. 17.)CrossRefGoogle ScholarPubMed

Moreno, J. L. 1934. Who Shall Survive?: A New Approach to the Problem of Human Interrelations. Washington, DC: Nervous and Mental Disease Publishing Co. (Cited on pp. 7, 16, 25, 78, 221.)CrossRefGoogle Scholar

Moreno, J. L. 1953. Who Shall Survive? Foundations of Sociometry, Group Psychotherapy and Socio-Drama. 2nd edn. Oxford: Beacon House. (Cited on p. 7.)Google Scholar

Moreno, R. 2021. Words of the Daughter: A Memoir. Morrisville, NC: Lulu Press, Inc. (Cited on p. 16.)Google Scholar

Morris, C., Ritzert, M., Fey, M. et al. 2019. Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 4602–4609. (Cited on p. 443.)CrossRefGoogle Scholar

Morris, R. 1978. Counting Large Numbers of Events in Small Registers. Com-munications of the ACM, 21(10), 840–842. (Cited on p. 469.)CrossRefGoogle Scholar

Mucha, P. J., Richardson, T., Macon, K., Porter, M. A., and Onnela, J.-P. 2010. Community Structure in Time-Dependent, Multiscale, and Multiplex Networks. Science, 328(5980), 876–878. (Cited on pp. 181, 201.)CrossRefGoogle ScholarPubMed

Munafò, M. R., Nosek, B. A., Bishop, D. V. M. et al. 2017. A Manifesto for Reproducible Science. Nature Human Behaviour, 1(1), 0021. (Cited on p. 163.)CrossRefGoogle ScholarPubMed

Murphy, K. P. 2022. Probabilistic Machine Learning: An Introduction. Cambridge, MA: MIT Press. (Cited on p. 473.)Google Scholar

Murray, D., Yoon, J., Kojaku, S. et al. 2021 (June). Unsupervised Embedding of Trajectories Captures the Latent Structure of Mobility. arXiv:2012.02785. (Cited on p. 435.)CrossRefGoogle Scholar

Nadakuditi, R. R., and Newman, M. E. J. 2012. Graph Spectra and the Detectabil-ity of Community Structure in Networks. Physical Review Letters, 108(18), 188701. (Cited on p. 357.)CrossRefGoogle Scholar

Narayanan, A., Shi, E., and Rubinstein, B. I. P. 2011. Link Prediction by De-Anonymization: How We Won the Kaggle Social Network Challenge. Pages 1825–1834 of: The 2011 International Joint Conference on Neural Networks. San Jose, CA: IEEE. (Cited on p. 33.)Google Scholar

National Academies of Sciences, Engineering, and Medicine. 2019. Reproducibility and Replicability in Science. Washington, DC: National Academies Press. (Cited on p. 474.)Google Scholar

Nemeth, R. J., and Smith, D. A. 1985. International Trade and World-System Structure: A Multiple Network Analysis. Review (Fernand Braudel Center), 8(4), 517–560. (Cited on p. 194.)Google Scholar

Newman, E. A., Araque, A., Dubinsky, J. M., Swanson, L. W., King, L. S., and Himmel, E. (eds.). 2017. The Beautiful Brain: The Drawings of Santiago Ramón y Cajal. New York, NY: Abrams. (Cited on p. 16.)Google Scholar

Newman, M. E. J. 2002. Assortative Mixing in Networks. Physical Review Letters, 89(20), 208701. (Cited on pp. 111, 201.)CrossRefGoogle Scholar

Newman, M. E. J. 2003. Mixing Patterns in Networks. Physical Review E, 67(2), 026126. (Cited on pp. 111, 116, 163, 173, 201.)CrossRefGoogle Scholar

Newman, M. E. J. 2006. Finding Community Structure in Networks Using the Eigenvectors of Matrices. Physical Review E, 74(3), 036104. (Cited on pp. 421, 427.)CrossRefGoogle ScholarPubMed

Newman, M. E. J. 2006. Modularity and Community Structure in Networks. Proceedings of the National Academy of Sciences, 103(23), 8577–8582. (Cited on pp. 201, 417, 418, 427.)CrossRefGoogle Scholar

Newman, M. E. J. 2012. Communities, Modules and Large-Scale Structure in Networks. Nature Physics, 8(1), 25–31. (Cited on p. 201.)CrossRefGoogle Scholar

Newman, M. E. J. 2013. Spectral Methods for Community Detection and Graph Partitioning. Physical Review E, 88(4), 042822. (Cited on p. 427.)CrossRefGoogle ScholarPubMed

Newman, M. E. J. 2018. Network Structure from Rich but Noisy Data. Nature Physics, 14(6), 542–545. (Cited on pp. 134, 365, 374.)CrossRefGoogle Scholar

Newman, M. E. J., and Girvan, M. 2004. Finding and Evaluating Community Structure in Networks. Physical Review E, 69(2), 026113. (Cited on p. 201.)CrossRefGoogle ScholarPubMed

Newman, M. E. J., and Park, J. 2003. Why Social Networks Are Different from Other Types of Networks. Physical Review E, 68(3), 036122. (Cited on pp. 380, 395.)CrossRefGoogle ScholarPubMed

Newman, M. E. J., Strogatz, S. H., and Watts, D. J. 2001. Random Graphs with Arbitrary Degree Distributions and Their Applications. Physical Review E, 64(2), 026118. (Cited on p. 340.)CrossRefGoogle ScholarPubMed

Newman, M. E. J., Watts, D. J., and Strogatz, S. H. 2002. Random Graph Models of Social Networks. Proceedings of the National Academy of Sciences, 99(suppl. 1), 2566–2572. (Cited on p. 380.)CrossRefGoogle Scholar

Newman, M. 2018. Networks. Oxford: Oxford University Press. (Cited on pp. 16, 172, 472.)CrossRefGoogle Scholar

Newman, M. 2005. Power Laws, Pareto Distributions and Zipf's Law. Contemporary Physics, 46(5), 323–351. (Cited on p. 163.)CrossRefGoogle Scholar

Ng, A., Jordan, M., and Weiss, Y. 2001. On Spectral Clustering: Analysis and an Algorithm. Advances in Neural Information Processing Systems, 14. (Cited on p. 428.)Google Scholar

Nica, B. 2018. A Brief Introduction to Spectral Graph Theory. EMS Textbooks in Mathematics. Zürich, Switzerland: European Mathematical Society. (Cited on p. 427.)Google Scholar

Nickel, M., Tresp, V., and Kriegel, H.-P. 2011. A Three-Way Model for Collective Learning on Multi-Relational Data. Pages 809–816 of: Proceedings of the 28th International Conference on International Conference on Machine Learning. ICML’11. Madison, WI: Omnipress. (Cited on p. 437.)Google Scholar

Nickel, M., and Kiela, D. 2017. Poincaré Embeddings for Learning Hierarchical Representations. In: Advances in Neural Information Processing Systems, vol. 30. Long Beach, CA: Curran Associates, Inc. (Cited on p. 438.)Google Scholar

Nilsson, N. J. 2009. The Quest for Artificial Intelligence. Cambridge: Cambridge University Press. (Cited on p. 276.)CrossRefGoogle Scholar

Nolte, D. D. 2019. Introduction to Modern Dynamics: Chaos, Networks, Space and Time. 2nd edn. Oxford; New York, NY: Oxford University Press. (Cited on p. 428.)CrossRefGoogle Scholar

Nowicki, K., and Snijders, T. A. B. 2001. Estimation and Prediction for Stochastic Blockstructures. Journal of the American Statistical Association, 96(455), 1077–1087. (Cited on p. 374.)CrossRefGoogle Scholar

Obermeyer, Z., Powers, B., Vogeli, C., and Mullainathan, S. 2019. Dissecting Racial Bias in an Algorithm Used to Manage the Health of Populations. Science, 366(6464), 447–453. (Cited on pp. 30, 31.)CrossRefGoogle Scholar

O’Neil, C. 2017. Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy. New York, NY: Crown. (Cited on pp. 37, 277.)Google Scholar

Ozella, L., Paolotti, D., Lichand, G. et al. 2021. Using Wearable Proximity Sensors to Characterize Social Contact Patterns in a Village of Rural Malawi. EPJ Data Science, 10(1), 46. (Cited on pp. 25, 37, 246.)CrossRefGoogle Scholar

Palla, G., Derényi, I., Farkas, I., and Vicsek, T. 2005. Uncovering the Overlapping Community Structure of Complex Networks in Nature and Society. Nature, 435(7043), 814–818. (Cited on pp. 181, 201.)CrossRefGoogle ScholarPubMed

Palmer, C. R., Gibbons, P. B., and Faloutsos, C. 2002. ANF: A Fast and Scalable Tool for Data Mining in Massive Graphs. Pages 81–90 of: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD ’02. New York, NY: Association for Computing Machinery. (Cited on pp. 456, 457, 469.)Google Scholar

Panko, R. R. 1998. What We Know About Spreadsheet Errors. Journal of Organizational and End User Computing, 10(2), 15–21. (Cited on p. 36.)CrossRefGoogle Scholar

Pantel, P. 2005. Inducing Ontological Co-Occurrence Vectors. Pages 125–132 of: Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics. ACL ’05. Ann Arbor, MI: Association for Computational Linguistics. (Cited on pp. 432, 445.)Google Scholar

Park, J., Wood, I. B., Jing, E. et al. 2019. Global Labor Flow Network Reveals the Hierarchical Organization and Dynamics of Geo-Industrial Clusters. Nature Communications, 10(1), 3449. (Cited on p. 21.)CrossRefGoogle ScholarPubMed

Park, J., and Newman, M. E. J. 2004. Solution of the Two-Star Model of a Network. Physical Review E, 70(6), 066146. (Cited on p. 369.)CrossRefGoogle ScholarPubMed

Park, J., and Newman, M. E. J. 2005. A Network-Based Ranking System for US College Football. Journal of Statistical Mechanics: Theory and Experiment, 2005(10), P10014–P10014. (Cited on p. 216.)CrossRefGoogle Scholar

Pascual, M., and Dunne, J. A. 2005. Ecological Networks: Linking Structure to Dynamics in Food Webs. Oxford: Oxford University Press. (Cited on p. 25.)CrossRefGoogle Scholar

Pastor-Satorras, R., and Vespignani, A. 2001. Epidemic Spreading in Scale-Free Networks. Physical Review Letters, 86(14), 3200–3203. (Cited on p. 333.)CrossRefGoogle ScholarPubMed

Patterson, B. D., and Atmar, W. 1986. Nested Subsets and the Structure of Insular Mammalian Faunas and Archipelagos. Biological Journal of the Linnean Society, 28(1–2), 65–82. (Cited on p. 202.)CrossRefGoogle Scholar

Peel, L., Larremore, D. B., and Clauset, A. 2017. The Ground Truth about Meta-data and Community Detection in Networks. Science Advances, 3(5), e1602548. (Cited on p. 107.)CrossRefGoogle Scholar

Peixoto, T. P. 2013. Parsimonious Module Inference in Large Networks. Physical Review Letters, 110(14), 148701. (Cited on p. 354.)CrossRefGoogle ScholarPubMed

Peixoto, T. P. 2014. Hierarchical Block Structures and High-Resolution Model Selection in Large Networks. Physical Review X, 4(1), 011047. (Cited on p. 358.)CrossRefGoogle Scholar

Pelleg, D., and Moore, A. W. 2000. X-Means: Extending K-means with Efficient Estimation of the Number of Clusters. Pages 727–734 of: Proceedings of the Seventeenth International Conference on Machine Learning. ICML ’00. San Francisco, CA: Morgan Kaufmann. (Cited on p. 426.)Google Scholar

Pereira, D. A., and Williams, J. A. 2007. Origin and Evolution of High Throughput Screening. British Journal of Pharmacology, 152(1), 53–61. (Cited on p. 82.)CrossRefGoogle ScholarPubMed

Perozzi, B., Al-Rfou, R., and Skiena, S. 2014. DeepWalk: Online Learning of Social Representations. Pages 701–710 of: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, NY: Association for Computing Machinery. (Cited on pp. 435, 445.)Google Scholar

Perry, B. L., Pescosolido, B. A., and Borgatti, S. P. 2018. Egocentric Network Analysis: Foundations, Methods, and Models. Cambridge: Cambridge University Press. (Cited on p. 201.)CrossRefGoogle Scholar

Pfeiffer, J., and Neville, J. 2021. Methods to Determine Node Centrality and Clustering in Graphs with Uncertain Structure. Proceedings of the International AAAI Conference on Web and Social Media, 5(1), 590–593. (Cited on pp. 387, 395.)CrossRefGoogle Scholar

Phan, T. Q., and Airoldi, E. M. 2015. A Natural Experiment of Social Network Formation and Dynamics. Proceedings of the National Academy of Sciences, 112(21), 6595–6600. (Cited on p. 474.)CrossRefGoogle Scholar

Pilgrim, C., and Hills, T. T. 2021. Bias in Zipf's Law Estimators. Scientific Reports, 11(1), 17309. (Cited on p. 163.)CrossRefGoogle ScholarPubMed

Pimentel, J. F., Murta, L., Braganholo, V., and Freire, J. 2019. A Large-Scale Study About Quality and Reproducibility of Jupyter Notebooks. Pages 507–517 of: 2019 IEEE/ACM 16th International Conference on Mining Software Repositories (MSR). Montreal, Canada: IEEE. (Cited on p. 302.)Google Scholar

Porter, M., and Gleeson, J. 2016. Dynamical Systems on Networks: A Tutorial. Frontiers in Applied Dynamical Systems: Reviews and Tutorials, no. 4. Cham, Switzerland: Springer. (Cited on pp. 235, 428.)Google Scholar

Porter, M. A., Onnela, J.-P., Mucha, P. J., et al. 2009. Communities in Networks. Notices of the AMS, 56(9), 1082–1097. (Cited on p. 201.)Google Scholar

Pothen, A., Simon, H. D., and Liou, K.-P. 1990. Partitioning Sparse Matrices with Eigenvectors of Graphs. SIAM Journal on Matrix Analysis and Applications, 11(3), 430–452. (Cited on pp. 412, 427.)CrossRefGoogle Scholar

Price, D. D. S. 1976. A General Theory of Bibliometric and Other Cumulative Advantage Processes. Journal of the American Society for Information Science, 27(5), 292–306. (Cited on p. 349.)CrossRefGoogle Scholar

Price, D. J. d. S. 1965. Networks of Scientific Papers: The Pattern of Bibliographic References Indicates the Nature of the Scientific Research Front. Science, 149(3683), 510–515. (Cited on p. 349.)CrossRefGoogle Scholar

Proulx, S. R., Promislow, D. E. L., and Phillips, P. C. 2005. Network Thinking in Ecology and Evolution. Trends in Ecology & Evolution, 20(6), 345–353. (Cited on p. 25.)CrossRefGoogle ScholarPubMed