A state-space mixed membership blockmodel for dynamic network tomography

By Eric P. Xing, Wenjie Fu, Le Song. 2008

249 citations recorded by the source corpus.

In a dynamic social or biological environment, the interactions between the actors can undergo large and systematic changes. In this paper we propose a model-based approach to analyze what we will refer to as the dynamic tomography of such time-evolving networks. Our approach offers an intuitive but powerful tool to infer the semantic underpinnings of each actor, such as its social roles or biological functions, underlying the observed network topologies. Our model builds on earlier work on a mixed membership stochastic blockmodel for static networks, and the state-space model for tracking object trajectory. It overcomes a major limitation of many current network inference techniques, which assume that each actor plays a unique and invariant role that accounts for all its interactions with other actors; instead, our method models the role of each actor as a time-evolving mixed membership vector that allows actors to behave differently over time and carry out different roles/functions when interacting with different peers, which is closer to reality. We present an efficient algorithm for approximate inference and learning using our model; and we applied our model to analyze a social network between monks (i.e., the Sampson's network), a dynamic email communication network between the Enron employees, and a rewiring gene interaction network of fruit fly collected during its full life cycle. In all cases, our model reveals interesting patterns of the dynamic roles of the actors.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics

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Citations in the corpus

Causal Discovery from Heterogeneous/Nonstationary Data (2019, methodology): “Several methods aim to model time-varying time-delayed causal relations (Xing et al., 2010; Song et al., 2009), which can be reduced to online parameter learning because the direction of causal relations is given (i.”
Causal Discovery from Nonstationary/Heterogeneous Data: Skeleton Estimation and Orientation Determination (2017, methodology): “Several methods aim to model time-varying time-delayed causal relations [Xing et al. , 2010; Song et al. , 2009], which can be reduced to online parameter learning because the direction of the causal relations is given (i.e., the past inﬂuences the future).”
Discovery and Visualization of Nonstationary Causal Models (2015, methodology): “Several methods aim to model time-varying time-delayed causal relations [16, 17], which can be reduced to online parameter learning because the direction of the causal relations is given (i.”
A Survey of Community Detection Approaches: From Statistical Modeling to Deep Learning (2021, background): “[54] propose a variant MMSB model for dynamic networks.”
A tensor approach to learning mixed membership community models (2013, unknown): “L G ] Keywords: Community detection, spectral methods, tensor methods, moment-based estimation, mixed membership models.”
Statistical clustering of temporal networks through a dynamic stochastic block model (2015, methodology): “The work by Xing et al. (2010) relies on a state space model for the evolution of the parameters of the priors of both the mixed membership vector of a node and the connectivity behavior.”
Multiscale Community Blockmodel for Network Exploration (2011, unknown): “The literature contains many examples of variational approximations being applied to variants of the stochastic blockmodel (Airoldi et al. 2008; Xing, Fu and Song 2010; Nallap-ati, Ahmed, Xing and Cohen 2008).”