Adding community and dynamic to topic models

Daifeng Li, Ying Ding, Xin Shuai, Johan Bollen, Jie Tang, Shanshan Chen, Jiayi Zhu and Guilherme Rocha

Journal of Informetrics, 2012, vol. 6, issue 2, 237-253

Abstract: The detection of communities in large social networks is receiving increasing attention in a variety of research areas. Most existing community detection approaches focus on the topology of social connections (e.g., coauthor, citation, and social conversation) without considering their topic and dynamic features. In this paper, we propose two models to detect communities by considering both topic and dynamic features. First, the Community Topic Model (CTM) can identify communities sharing similar topics. Second, the Dynamic CTM (DCTM) can capture the dynamic features of communities and topics based on the Bernoulli distribution that leverages the temporal continuity between consecutive timestamps. Both models were tested on two datasets: ArnetMiner and Twitter. Experiments show that communities with similar topics can be detected and the co-evolution of communities and topics can be observed by these two models, which allow us to better understand the dynamic features of social networks and make improved personalized recommendations.

Keywords: Social network; Semantic community; Topic mining; Dynamic (search for similar items in EconPapers)
Date: 2012
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:infome:v:6:y:2012:i:2:p:237-253

DOI: 10.1016/j.joi.2011.11.004

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