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Bayesian approach to multilayer stochastic blockmodel and network changepoint detection

Published online by Cambridge University Press:  06 June 2017

YUNKYU SOHN  and
JONG HEE PARK
YUNKYU SOHN
Affiliation:
Department of Politics, Princeton University, Princeton, NJ 08540, USA Department of Political Science, University of California San Diego, La Jolla, CA 92093, USA (e-mail: ysohn@princeton.edu)
JONG HEE PARK
Affiliation:
Department of Political Science and International Relations, Seoul National University, Seoul 151-742, Republic of Korea (e-mail: jongheepark@snu.ac.kr)
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Abstract

Network scholars commonly encounter multiple networks, each of which is possibly governed by distinct generation rules while sharing a node group structure. Although the stochastic blockmodeling—detecting such latent group structures with group-specific connection profiles—has been a major topic of recent research, the focus has been given to the assortative group discovery of a single network. Despite its universality, concepts, and techniques for simultaneous characterization of node traits of multilayer networks, constructed by stacking multiple networks into layers, have been limited. Here, we propose a Bayesian multilayer stochastic blockmodeling framework that uncovers layer-common node traits and factors associated with layer-specific network generating functions. Without assuming a priori layer-specific generation rules, our fully Bayesian treatment allows probabilistic inference of latent traits. We extend the approach to detect changes in block structures embedded in temporal layers of network time series. We demonstrate the method using synthetic multilayer networks with assortative, disassortative, core-periphery, and overlapping community structures. Finally, we apply the method to empirical social network datasets, and find that it detects significant latent traits and structural changepoints. In particular, we uncover endogenous historical regimes associated with distinct constellations of states in United States Senate roll call vote similarity patterns.

Keywords

multilayer networkstochastic blockmodelnetwork changepoint detectiontensor decompositionnetwork time serieshidden Markov model
Type
Research Article
Information
Network Science , Volume 5 , Special Issue 2: Modeling, Analysis, and Mining of Multilayer Networks , June 2017 , pp. 164 - 186
Copyright
Copyright © Cambridge University Press 2017 

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