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Generalized Blockmodeling
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  • Cited by 23
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    This book has been cited by the following publications. This list is generated based on data provided by CrossRef.

    Diviák, Tomáš 2018. Sinister connections: How to analyse organised crime with social network analysis?. AUC PHILOSOPHICA ET HISTORICA, Vol. 2018, Issue. 2, p. 115.

    Hoffman, Michaela Steinley, Douglas Gates, Kathleen M. Prinstein, Mitchell J. and Brusco, Michael J. 2018. Detecting Clusters/Communities in Social Networks. Multivariate Behavioral Research, Vol. 53, Issue. 1, p. 57.

    Hecking, Tobias Steinert, Laura Masias, Victor H. and Ulrich Hoppe, H. 2018. Complex Networks & Their Applications VI. Vol. 689, Issue. , p. 1002.

    Boroujeni, Mina Shirvani Hecking, Tobias Hoppe, H. Ulrich and Dillenbourg, Pierre 2017. Dynamics of MOOC discussion forums. p. 128.

    Hecking, Tobias Harrer, Andreas and Hoppe, H. Ulrich 2017. Prediction and Inference from Social Networks and Social Media. p. 171.

    Capellari, Saveria and De Stefano, Domenico 2016. Different network typologies in patenting activity of academic inventors through time: the case of Italian chemists in the period 2000–2011. Triple Helix, Vol. 3, Issue. 1,

    Abbe, Emmanuel Bandeira, Afonso S. and Hall, Georgina 2016. Exact Recovery in the Stochastic Block Model. IEEE Transactions on Information Theory, Vol. 62, Issue. 1, p. 471.

    Hecking, Tobias Hoppe, H. Ulrich and Harrer, Andreas 2015. Uncovering the Structure of Knowledge Exchange in a MOOC Discussion Forum. p. 1614.

    Capellari, Saveria and De Stefano, Domenico 2014. University-owned and university-invented patents: a network analysis on two Italian universities. Scientometrics, Vol. 99, Issue. 2, p. 313.

    Abbe, Emmanuel Bandeira, Afonso S. Bracher, Annina and Singer, Amit 2014. Linear inverse problems on Erdős-Rényi graphs: Information-theoretic limits and efficient recovery. p. 1251.

    Baggio, Rodolfo and Del Chiappa, Giacomo 2014. Real and virtual relationships in tourism digital ecosystems. Information Technology & Tourism, Vol. 14, Issue. 1, p. 3.

    Lee, Sang Hoon Cucuringu, Mihai and Porter, Mason A. 2014. Density-based and transport-based core-periphery structures in networks. Physical Review E, Vol. 89, Issue. 3,

    Esmailian, Pouya Abtahi, Seyed Ebrahim and Jalili, Mahdi 2014. Mesoscopic analysis of online social networks: The role of negative ties. Physical Review E, Vol. 90, Issue. 4,

    Silva, Evandro Jose Da Rocha E Ludermir, Teresa Bernarda and Almeida, Leandro Maciel 2013. Clustering and Selection Using Grouping Genetic Algorithms for Blockmodeling to Construct Neural Network Ensembles. p. 420.

    Peixoto, Tiago P. 2012. Entropy of stochastic blockmodel ensembles. Physical Review E, Vol. 85, Issue. 5,

    Coscia, Michele Giannotti, Fosca and Pedreschi, Dino 2011. A classification for community discovery methods in complex networks. Statistical Analysis and Data Mining, Vol. 4, Issue. 5, p. 512.

    Lu, Aidong Wang, Weichao Dnyate, Abhishek and Hu, Xianlin 2011. Sybil Attack Detection through Global Topology Pattern Visualization. Information Visualization, Vol. 10, Issue. 1, p. 32.

    Diaz-Bone, Rainer 2010. Reviews: Collective Action: Matthew O. Jackson, Social and Economic Networks. Princeton, NJ: Princeton University Press, 2008, 520 pp., ISBN 9780691134406, US$65.00/£45.00. International Sociology, Vol. 25, Issue. 5, p. 710.

    MRVAR, ANDREJ and DOREIAN, PATRICK 2009. Partitioning Signed Two-Mode Networks. The Journal of Mathematical Sociology, Vol. 33, Issue. 3, p. 196.

    Ramasco, José J. and Mungan, Muhittin 2008. Inversion method for content-based networks. Physical Review E, Vol. 77, Issue. 3,


Book description

This book provides an integrated treatment of blockmodeling, the most frequently used technique in social network analysis. It secures its mathematical foundations and then generalizes blockmodeling for the analysis of many types of network structures. Examples are used throughout the text and include small group structures, little league baseball teams, intra-organizational networks, inter-organizational networks, baboon grooming networks, marriage ties of noble families, trust networks, signed networks, Supreme Court decisions, journal citation networks, and alliance networks. Also provided is an integrated treatment of algebraic and graph theoretic concepts for network analysis and a broad introduction to cluster analysis. These formal ideas are the foundations for the authors' proposal for direct optimizational approaches to blockmodeling which yield blockmodels that best fit the data, a measure of fit that is integral to the establishment of blockmodels, and creates the potential for many generalizations and a deductive use of blockmodeling.


'This is a clearly presented and insightful book that provides an excellent mix of mathematical rigor and practical application. I would unhesitatingly recommend the book to anyone interested in social network analysis or discrete clustering methods.'

Source: Journal of Classification

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