Joint group and topic discovery from relations and text

Abstract. We present a probabilistic generative model of entity re-lationships and textual attributes; the model simultaneously discovers groups among the entities and topics among the corresponding text. Block models of relationship data have been studied in social network analysis for some time, however here we cluster in multiple modalities at once. Significantly, joint inference allows the discovery of groups to be guided by the emerging topics, and vice-versa. We present experi-mental results on two large data sets: sixteen years of bills put before the U.S. Senate, comprising their corresponding text and voting records, and 43 years of similar data from the United Nations. We show that in comparison with traditional, separate latent-variable models for words or block structures for votes, our Group-Topic model’s joint inference im-proves both the groups and topics discovered. Additionally, we present a non-Markov continouous-time group model to capture shifting group structure over time.