2009/01/31 by Eric P. Xing, Wenjie Fu, Le Song · 3 citations
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Physics and Astronomy · #Advanced Graph Neural Networks #Bayesian Modeling and Causal Inference #Complex Network Analysis Techniques #Dynamic Bayesian network #Dynamic network analysis #Inference #Network analysis #Network model #Object (grammar) #Social network (sociolinguistics) #Social network analysis #q-bio.MN #q-bio.QM #stat.AP #stat.ME #stat.ML
paper · pdf · doi:10.1214/09-aoas311
published as Annals of Applied Statistics 2010, Vol. 4, No. 2, 535-566 · Published in at http://dx.doi.org/10.1214/09-AOAS311 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org)
openalex publication_date 2010/06/01 · arxiv created 2010/11/08 · arxiv updated 2010/11/09 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/06
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.