Everitt, B., Landau, S., Leese, M., & Stahl, D. (2011). Cluster analysis. Wiley Series in Probability and Statistics. Hoboken: John Wiley & Sons.
Faust, K., & Skvoretz, J. (2002). Comparing networks across space and time, size and species. Sociological Methodology, 32, 267–299.
Fraley, C., & Raftery, A. E. (2002). Model-based clustering, discriminant analysis, and density estimation. Journal of the American statistical Association, 97, 611–631.
Frank, K. A., Lo, Y.-J., & Sun, M. (2014). Social network analysis of the influences of educational reforms on teachers practices and interactions. Zeitschrift für Erziehungswissenschaft, 17, 117–134.
Frank, K. A., Zhao, Y., & Borman, K. (2004). Social capital and the diffusion of innovations within organizations: The case of computer technology in schools. Sociology of Education, 77, 148–171.
Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning (vol. 1). New York: Springer.
Gest, S. D., & Rodkin, P. C. (2011). Teaching practices and elementary classroom peer ecologies. Journal of Applied Developmental Psychology, 32, 288–296.
Harris, A. (2009). Distributed leadership: What we know. In Distributed leadership (pp. 11–21). Dordrecht: Springer.
Harris, A., & Spillane, J. (2008). Distributed leadership through the looking glass. Management in Education, 22, 31–34.
Harris, K., Halpern, C., Whitsel, E., Hussey, J., Tabor, J., Entzel, P., & Udry, J. (2009). The national longitudinal study of adolescent health. Research design. Retrieved from http://www.cpc.unc.edu/projects/addhealth/design (September 2011).
Hashimoto, K.-i. (1989). Zeta functions of finite graphs and representations of p-adic groups. Automorphic Forms and Geometry of Arithmetic Varieties, 15, 211–280.
Holland, P., Laskey, K., & Leinhardt, S. (1983). Stochastic blockmodels: First steps. Social Networks, 5, 109–137.
Hopkins, M., Lowenhaupt, R., & Sweet, T. M. (2015). Organizing instruction in new immigrant destinations: District infrastructure and subject-specific school practice. American Educational Research Journal, 52, 408–439.
Hubert, L. & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2, 193–218.
Kashima, H., Tsuda, K., & Inokuchi, A. (2003). Menlo Park: AAAI Press. Marginalized kernels between labeled graphs. In Proceedings of the 20th international conference on machine learning (ICML-03) (pp. 321–328).
Krzakala, F., Moore, C., Mossel, E., Neeman, J., Sly, A., Zdeborová, L., & Zhang, P. (2013). Spectral redemption in clustering sparse networks. Proceedings of the National Academy of Sciences, 110, 20935–20940.
Lazega, E., & Snijders, T. A. (2015). Multilevel network analysis for the social sciences: Theory, methods and applications (vol. 12). Berlin, Germany: Springer.
MacQueen, J., (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the fifth Berkeley symposium on mathematical statistics and probability, Oakland, CA, USA (vol. 1, pp. 281–297). Berkeley: University of California Press.
Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M., & Hornik, K. (2018). cluster: Cluster analysis basics and extensions.
Martin, T., Zhang, X., & Newman, M. (2014). Localization and centrality in networks. Physical Review E, 90, 052808.
Paluck, E. L., & Shepherd, H. (2012). The salience of social referents: A field experiment of collective norms and harassment behavior in a school social network. Journal of Personality and Social Psychology, 103, 899–915.
Ralaivola, L., Swamidass, S. J., Saigo, H., & Baldi, P. (2005). Graph kernels for chemical informatics. Neural Networks, 18, 1093–1110.
Saigo, H., Nowozin, S., Kadowaki, T., Kudo, T., & Tsuda, K. (2009). gBoost: A mathematical programming approach to graph classification and regression. Machine Learning, 75, 69–89.
Sarkar, A., Fienberg, S., & Krackhardt, D. (2010). Predicting profitability using advice branch bank networks. Statistical Methodology, 7, 429–444.
Sinani, E., Stafsudd, A., Thomsen, S., Edling, C., & Randøy, T. (2008). Corporate governance in Scandinavia: Comparing networks and formal institutions. European Management Review, 5, 27–40.
Snijders, T., & Kenny, D. (1999). The social relations model for family data: A multilevel approach. Personal Relationships, 6, 471–486.
Snijders, T. A., & Baerveldt, C. (2003). A multilevel network study of the effects of delinquent behavior on friendship evolution. Journal of Mathematical Sociology, 27, 123–151.
Snijders, T. A., Steglich, C. E., Schweinberger, M., & Huisman, M. (2008). Manual for SIENA version 3.2. Department of Sociology, ICS, University of Groningen, Groningen, The Netherlands.
Spillane, J., Hopkins, M., & Sweet, T. (2015). Intra- and inter-school instructional interactions: Exploring conditions for instructional knowledge production within and between schools. American Journal of Education, 122, 71–110.
Spillane, J. P. (2012). Distributed leadership (vol. 4). San Francisco: John Wiley & Sons.
Spillane, J. P., Halverson, R., & Diamond, J. B. (2001). Investigating school leadership practice: A distributed perspective. Educational Researcher, 30, 23–28.
Spillane, J. P., & Hopkins, M. (2013). Organizing for instruction in education systems and school organizations: How the subject matters. Journal of Curriculum Studies, 45, 721–747.
Spillane, J. P., Hopkins, M., & Sweet, T. M. (2016). Exploring the relationship between teachers’ instructional ties and teachers’ instructional beliefs: Trying not to ‘put the cart before the horse’. American Journal of Education, 122, 71–110.
Spillane, J. P., Shirrell, M., & Sweet, T. M. (2017). The elephant in the schoolhouse: The role of propinquity in school staff interactions about teaching. Sociology of Education, 90, 149–171.
Sweet, T., & Zheng, Q. (2017). A mixed membership model-based measure for subgroup integration in social networks. Social Networks, 48, 169–180.
Sweet, T. M., Thomas, A. C., & Junker, B. W. (2013). Hierarchical network models for education research: Hierarchical latent space models. Journal of Educational and Behavioral Statistics, 38, 295–318.
Tibshirani, R., Walther, G., & Hastie, T. (2001). Estimating the number of clusters in a data set via the gap statistic. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 63, 411–423.
Traud, A. L., Kelsic, E. D., Mucha, P. J., & Porter, M. A. (2011). Comparing community structure to characteristics in online collegiate social networks. SIAM Review, 53, 526–543.
Vishwanathan, S. V. N., Schraudolph, N. N., Kondor, R., & Borgwardt, K. M. (2010). Graph kernels. Journal of Machine Learning Research, 11, 1201–1242.
Vogelstein, J. T., Roncal, W. G., Vogelstein, R. J., & Priebe, C. E. (2013). Graph classification using signal-subgraphs: Applications in statistical connectomics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35, 1539–1551.
Ward, J. H. Jr. (1963). Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association, 58, 236–244.
Wolfe, J. H. (1963). Object cluster analysis of social areas, Ph.D. thesis, University of California.
Zijlstra, B., van Duijn, M., & Snijders, T. (2006). The multilevel p2 model. Methodology: European Journal of Research Methods for the Behavioral and Social Sciences, 2, 42–47.
Žnidaršič, A., Ferligoj, A., & Doreian, P. (2017). Actor non-response in valued social networks: The impact of different non-response treatments on the stability of blockmodels. Social Networks, 48, 46–56.