Multiscale Community Blockmodel for Network Exploration

Qirong Ho, Ankur Parikh, Le Song, Eric Xing
Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, PMLR 15:333-341, 2011.

Abstract

Real world networks exhibit a complex set of phenomena such as underlying hierarchical organization, multiscale interaction, and varying topologies of communities. Most existing methods do not adequately capture the intrinsic interplay among such phenomena. We propose a nonparametric Multiscale Community Blockmodel (MSCB) to model the generation of hierarchies in social communities, selective membership of actors to subsets of these communities, and the resultant networks due to within- and cross- community interactions. By using the nested Chinese Restaurant Process, our model automatically infers the hierarchy structure from the data. We develop a collapsed Gibbs sampling algorithm for posterior inference, conduct extensive validation using synthetic networks, and demonstrate the utility of our model in real-world datasets such as predator-prey networks and citation networks.

Cite this Paper

BibTeX
@InProceedings{pmlr-v15-ho11a, title = {Multiscale Community Blockmodel for Network Exploration}, author = {Ho, Qirong and Parikh, Ankur and Song, Le and Xing, Eric}, booktitle = {Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics}, pages = {333--341}, year = {2011}, editor = {Gordon, Geoffrey and Dunson, David and Dudík, Miroslav}, volume = {15}, series = {Proceedings of Machine Learning Research}, address = {Fort Lauderdale, FL, USA}, month = {11--13 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v15/ho11a/ho11a.pdf}, url = {https://proceedings.mlr.press/v15/ho11a.html}, abstract = {Real world networks exhibit a complex set of phenomena such as underlying hierarchical organization, multiscale interaction, and varying topologies of communities. Most existing methods do not adequately capture the intrinsic interplay among such phenomena. We propose a nonparametric Multiscale Community Blockmodel (MSCB) to model the generation of hierarchies in social communities, selective membership of actors to subsets of these communities, and the resultant networks due to within- and cross- community interactions. By using the nested Chinese Restaurant Process, our model automatically infers the hierarchy structure from the data. We develop a collapsed Gibbs sampling algorithm for posterior inference, conduct extensive validation using synthetic networks, and demonstrate the utility of our model in real-world datasets such as predator-prey networks and citation networks.} }
Endnote
%0 Conference Paper %T Multiscale Community Blockmodel for Network Exploration %A Qirong Ho %A Ankur Parikh %A Le Song %A Eric Xing %B Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2011 %E Geoffrey Gordon %E David Dunson %E Miroslav Dudík %F pmlr-v15-ho11a %I PMLR %P 333--341 %U https://proceedings.mlr.press/v15/ho11a.html %V 15 %X Real world networks exhibit a complex set of phenomena such as underlying hierarchical organization, multiscale interaction, and varying topologies of communities. Most existing methods do not adequately capture the intrinsic interplay among such phenomena. We propose a nonparametric Multiscale Community Blockmodel (MSCB) to model the generation of hierarchies in social communities, selective membership of actors to subsets of these communities, and the resultant networks due to within- and cross- community interactions. By using the nested Chinese Restaurant Process, our model automatically infers the hierarchy structure from the data. We develop a collapsed Gibbs sampling algorithm for posterior inference, conduct extensive validation using synthetic networks, and demonstrate the utility of our model in real-world datasets such as predator-prey networks and citation networks.
RIS
TY - CPAPER TI - Multiscale Community Blockmodel for Network Exploration AU - Qirong Ho AU - Ankur Parikh AU - Le Song AU - Eric Xing BT - Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics DA - 2011/06/14 ED - Geoffrey Gordon ED - David Dunson ED - Miroslav Dudík ID - pmlr-v15-ho11a PB - PMLR DP - Proceedings of Machine Learning Research VL - 15 SP - 333 EP - 341 L1 - http://proceedings.mlr.press/v15/ho11a/ho11a.pdf UR - https://proceedings.mlr.press/v15/ho11a.html AB - Real world networks exhibit a complex set of phenomena such as underlying hierarchical organization, multiscale interaction, and varying topologies of communities. Most existing methods do not adequately capture the intrinsic interplay among such phenomena. We propose a nonparametric Multiscale Community Blockmodel (MSCB) to model the generation of hierarchies in social communities, selective membership of actors to subsets of these communities, and the resultant networks due to within- and cross- community interactions. By using the nested Chinese Restaurant Process, our model automatically infers the hierarchy structure from the data. We develop a collapsed Gibbs sampling algorithm for posterior inference, conduct extensive validation using synthetic networks, and demonstrate the utility of our model in real-world datasets such as predator-prey networks and citation networks. ER -
APA
Ho, Q., Parikh, A., Song, L. & Xing, E.. (2011). Multiscale Community Blockmodel for Network Exploration. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 15:333-341 Available from https://proceedings.mlr.press/v15/ho11a.html.

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