Topic Block: Mining User Inner Interests for Text and Link Analysis in Social Networks

Abstract

Text corpus and link network are interrelated data in social networks. Discovering the inner relationship between these two kinds of data can help better understand the evolution mechanism underneath social networks. Moreover, social networks exhibit unique characteristics such as sparse and noisy in both text and link data. Thus, it is imperative to combine both text and link data to complement and correct mining results. However, previous work did not explore a uniform generative model that can unveil their inner relationship probably because of the difficulty to harness the heterogenous data in social networks. To address this issue, in this paper we present a generative model Topic Block that clearly pinpoints the latent concept underlying the text corpus and link network, i.e., User inner interests. In our generative model, user inner interests guide the generation of the topic and community distributions underlying the text corpus and link data. We can infer the topic and community distributions based on the user inner interests through both content and topology information. Compared to existing popular models, our method experimentally outperforms on three real world social network data sets.