Ahuja, R. K., Magnanti, T. L., & Orlin, J. B. (1993). Network flows: Theory, algorithms, and applications. Prentice Hall.

Akamatsu, T. (1996). Cyclic flows, Markov process and stochastic traffic assignment. Transportation Research B, 30(5), 369386.

Alamgir, M., & von Luxburg, U. (2011). Phase transition in the family of p-resistances. In *Advances in neural information processing systems 24: Proceedings of the NIPS ‘11 conference* (pp. 379–387).

Bacharach, M. (1965) Estimating nonnegative matrices from marginal data. International Economic Review, 6(3), 294–310.

Barabási, A.-L. (2016) Network science. Cambridge University Press.

Bavaud, F., & Guex, G. (2012). Interpolating between random walks and shortest paths: A path functional approach. In *International conference on social informatics* (pp. 68–81).

Brandes, U., & Fleischer, D. (2005). Centrality measures based on current flow. In *Proceedings of the 22*^{nd} annual symposium on theoretical aspects of computer science (STACS ‘05) (pp. 533–544).

Chebotarev, P. (2011). A class of graph-geodetic distances generalizing the shortest-path and the resistance distances. Discrete Applied Mathematics, 159(5), 295–302.

Chebotarev, P. (2012) The walk distances in graphs. Discrete Applied Mathematics, 160(10-11), 1484–1500.

Chebotarev, P. (2013). Studying new classes of graph metrics. In Nielsen, F., and Barbaresco, F. (Eds.), *Proceedings of the 1st international conference on geometric science of information (GSI ‘13)* (vol. 8085, pp. 207–214).

Chung, F. R., & Lu, L. (2006). *Complex graphs and networks*. American Mathematical Society.

Courty, N., Flamary, R., Tuia, D., & Rakotomamonjy, A. (2017). Optimal transport for domain adaptation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(9), 1853–1865.

Cover, T. M., & Thomas, J. A. (2006). Elements of information theory (2nd ed.). John Wiley and Sons.

Cuturi, M. (2013). Sinkhorn distances: Lightspeed computation of optimal transport. In Advances in neural information processing systems 26: Proceedings of the NIPS ‘13 conference (pp. 2292–2300). MIT Press.

Dobrushin, R. L. (1970). Prescribing a system of random variables by conditional distributions. Theory of Probability & Its Applications, 15(3), 458–486.

Doyle, P. G., & Snell, J. L. (1984). *Random walks and electric networks*. The Mathematical Association of America.

Erlander, S., & Stewart, N. (1990). The gravity model in transportation analysis. Theory and extensions. VSP International Science Publishers.

Estrada, E. (2012). The structure of complex networks. Oxford University Press.

Fang, S., Rajasekera, J., & Tsao, H. (1997). Entropy optimization and mathematical programming. Springer.

Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J.-F. (2014) Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853–1882.

Fouss, F., Saerens, M., & Shimbo, M. (2016). Algorithms and models for network data and link analysis. Cambridge University Press.

Françoisse, K., Kivimäki, I., Mantrach, A., Rossi, F., & Saerens, M. (2017). A bag-of-paths framework for network data analysis. Neural Networks, 90, 90–111.

Freeman, L. C. (1977). A set of measures of centrality based on betweenness. Sociometry, 40(1), 35–41.

García-Díez, S., Vandenbussche, E., & Saerens, M. (2011). A continuous-state version of discrete randomized shortest-paths. In *Proceedings of the 50*^{th} IEEE international conference on decision and control (IEEE CDC 2011) (pp. 6570–6577).

Graybill, F. (1983). *Matrices with applications in statistics*. Wadsworth International Group.

Grinstead, C., & Snell, J. L. (1997). *Introduction to probability* (2nd ed.). The Mathematical Association of America.

Griva, I., Nash, S. G., & Sofer, A. (2009). *Linear and nonlinear optimization: Second edition*. Society for Industrial and Applied Mathematics (SIAM).

Guex, G. (2016). Interpolating between random walks and optimal transportation routes: Flow with multiple sources and targets. Physica A: Statistical Mechanics and its Applications, 450, 264–277.

Guex, G., & Bavaud, F. (2015). Flow-based dissimilarities: shortest path, commute time, max-flow and free energy. In Lausen, B., Krolak-Schwerdt, S., and Bohmer, M. (Eds.), Data science, learning by latent structures, and knowledge discovery (vol. 1564, pp. 101–111). Springer.

Guex, G., Emmanouilidis, T., & Bavaud, F. (2017). *Transportation clustering: A regularized version of the optimal transportation problem*. (Submitted for publication)

Hara, K., Suzuki, I., Shimbo, M., Kobayashi, K., Fukumizu, K., & Radovanovic, M. (2015). Localized centering: Reducing hubness in large-sample data. In *Proceedings of the 29*^{th} AAAI conference on artificial intelligence (AAAI ‘15) (pp. 26452651).

Hashimoto, T., Sun, Y., & Jaakkola, T. (2015). From random walks to distances on unweighted graphs. In Advances in neural information processing systems 28: Proceedings of the NIPS ‘15 conference (pp. 3429–3437). MIT Press.

Herbster, M., & Lever, G. (2009). Predicting the labelling of a graph via minimum p-seminorm interpolation. In *Proceedings of the 22nd conference on learning theory (COLT ‘09)* (pp. 18–21).

Jaynes, E. T. (1957). Information theory and statistical mechanics. Physical Review, 106, 620–630.

Kantorovich, L. V. (1942). On the translocation of masses. Doklady Akademii Nauk SSSR, 37(7-8), 227–229.

Kapur, J. N. (1989). Maximum-entropy models in science and engineering. Wiley.

Kapur, J. N., & Kesavan, H. K. (1992). Entropy optimization principles with applications. Academic Press.

Kivimäki, I., Lebichot, B., Saramäki, J., & Saerens, M. (2016). Two betweenness centrality measures based on randomized shortest paths (Scientific Reports, 6, srep19668).

Kivimäki, I., Shimbo, M., & Saerens, M. (2014). Developments in the theory of randomized shortest paths with a comparison of graph node distances. Physica A: Statistical Mechanics and its Applications, 393, 600–616.

Klein, D. J., & Randic, M. (1993). Resistance distance. Journal of Mathematical Chemistry, 12(1), 81–95.

Kolaczyk, E. D. (2009). Statistical analysis of network data: Methods and models. Springer.

Kurras, S. (2015). Symmetric iterative proportional fitting. In *Proceedings of the 18th international conference on artificial intelligence and statistics (AISTATS)* (vol. 38, pp. 526–534).

Lebichot, B., Kivimäki, I., Françoisse, K., & Saerens, M. (2014). Semi-supervised classification through the bag-of-paths group betweenness. IEEE Transactions on Neural Networks and Learning Systems, 25, 1173–1186.

Lebichot, B., & Saerens, M. (2018). A bag-of-paths node criticality measure. Neurocomputing, 275, 224–236.

Lewis, T. G. (2009). Network science. Wiley.

Li, Y., Zhang, Z.-L., & Boley, D. (2013). From shortest-path to all-path: The routing continuum theory and its applications. IEEE Transactions on Parallel and Distributed Systems, 25(7), 1745–1755.

Lougee-Heimer, R. (2003). The Common Optimization INterface for Operations Research: Promoting open-source software in the operations research community. IBM Journal of Research and Development, 47(1), 57–66. doi: 10.1147/rd.471.0057.

Lü, L., &Zhou, T. (2011). Link prediction in complex networks: A survey. PhysicaA: Statistical Mechanics and its Applications, 390, 1150–1170.

Mantrach, A., Yen, L., Callut, J., Françoisse, K., Shimbo, M., & Saerens, M. (2010). The sum-over-paths covariance kernel: A novel covariance between nodes of a directed graph. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(6), 1112–1126.

Mantrach, A., Zeebroeck, N. V., Francq, P., Shimbo, M., Bersini, H., & Saerens, M. (2011). Semi-supervised classification and betweenness computation on large, sparse, directed graphs. Pattern Recognition, 44(6), 1212–1224.

Newman, M. E. (2005). A measure of betweenness centrality based on random walks. Social Networks, 27(1), 39–54.

Newman, M. E. (2010). Networks: An introduction. Oxford University Press.

Nguyen, C. H., & Mamitsuka, H. (2016). New resistance distances with global information on large graphs. In *Proceedings of the 19th international conference on artificial intelligence and statistics (AISTATS)* (pp. 639–647).

Osborne, M. J. (2004). An introduction to game theory. Oxford University Press.

Pukelsheim, F. (2014). Biproportional scaling of matrices and the iterative proportional fitting procedure. Annals of Operations Research, 215(1), 269–283.

Radovanovic, M., Nanopoulos, A., & Ivanovic, M. (2010a). Hubs in space: Popular nearest neighbors in high-dimensional data. Journal of Machine Learning Research, 11, 2487–2531.

Radovanovic, M., Nanopoulos, A., & Ivanovic, M. (2010b). On the existence of obstinate results in vector space models. In *Proceedings of the 33rd annual international ACM SIGIR conference on research and development in information retrieval (SIGIR ‘10)* (pp. 186–193).

Saerens, M., Achbany, Y., Fouss, F., & Yen, L. (2009). Randomized shortest-path problems: Two related models. Neural Computation, 21(8), 2363–2404.

Silva, T., & Zhao, L. (2016). Machine learning in complex networks. Springer.

Sinkhorn, R. (1967). Diagonal equivalence to matrices with prescribed row and column sums. The American Mathematical Monthly, 74(4), 402–405.

Solomon, J., Rustamov, R., Guibas, L., & Butscher, A. (2014). Wasserstein propagation for semi-supervised learning. In *Proceedings of the 31 international conference on machine learning (ICML ‘14)* (pp. 306–314).

Sommer, F., Fouss, F., & Saerens, M. (2016). Comparison of graph node distances on clustering tasks. In *Proceedings of the 25th international conference on artificial neural networks (ICANN2016)* (vol. 9886, pp. 192–201).

Sommer, F., Fouss, F., & Saerens, M. (2017). Modularity-driven kernel k-means for community detection. In *Proceedings of the 26th international conference on artificial neural networks (ICANN2017)* (vol. 10614, pp. 423–433).

Suzuki, I., Hara, K., Shimbo, M., Matsumoto, Y., & Saerens, M. (2012). Investigating the effectiveness of Laplacian-based kernels in hub reduction. In *Proceedings of the 26th AAAI conference on artificial intelligence (AAAI ‘12)* (pp. 1112–1118).

Suzuki, I., Hara, K., Shimbo, M., Saerens, M., & Fukumizu, K. (2013). Centering similarity measures to reduce hubs. In *Proceedings of the international conference on empirical methods in natural language processing (EMNLP 2013)* (pp. 613–623).

Thelwall, M. (2004). Link analysis: An information science approach. Elsevier.

Tomasev, N., Radovanovic, M., Mladenic, D., & Ivanovic, M. (2014). The role of hubness in clustering high-dimensional data. IEEE Transactions on Knowledge and Data Engineering, 26(3), 739–751.

Villani, C. (2003). *Topics in optimal transportation*. American Mathematical Society.

Villani, C. (2008). Optimal transport: Old and new. Springer.

von Luxburg, U., Radl, A., & Hein, M. (2010). Getting lost in space: Large sample analysis of the commute distance. In Advances in neural information processing systems 23: Proceedings of the NIPS ‘10 conference (pp. 2622–2630). MIT Press.

von Luxburg, U., Radl, A., & Hein, M. (2014). Hitting and commute times in large random neighborhood graphs. Journal of Machine Learning Research, 15(1), 1751–1798.

Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge University Press.

Wilson, A. (1970). Entropy in urban and regional modelling. Routledge.

Yen, L., Mantrach, A., Shimbo, M., & Saerens, M. (2008). A family ofdissimilarity measures between nodes generalizing both the shortest-path and the commute-time distances. In *Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining (KDD ‘08)* (pp. 785–793).

Zhang, W., Zhao, D., & Wang, X. (2013). Agglomerative clustering via maximum incremental path integral. Pattern Recognition, 46(11), 3056–3065.

Zolotarev, V. M. (1983). Probability metrics. Teoriya Veroyatnostei i ee Primeneniya, 28(2), 264–287.