Journal
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
Volume 33, Issue 6, Pages 2737-2749Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2019.2958806
Keywords
Network topology; Social network services; Heuristic algorithms; Inference algorithms; Clustering algorithms; Bayes methods; Probabilistic logic; Social networks; community detection; topical summary; link communities; variational algorithm
Categories
Funding
- Natural Science Foundation of China [61772361, 61876128]
- National Key R&D Program of China [2017YFC0820106]
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The study discusses the issues of community detection in real networks and proposes a new Bayesian probabilistic approach to address these challenges. By exploring the correlation between communities and topics, the new method aims to discover link communities and extract semantically meaningful community summaries simultaneously. Experimental results demonstrate the effectiveness of the new approach and its ability to provide rich explanations through multiple topical summaries per community if desired.
Community detection has been extensively studied for various applications. Recent research has started to explore node contents to identify semantically meaningful communities. However, links in real networks typically have semantic descriptions and communities of links can better characterize community behaviors than communities of nodes. The second issue in community finding is that the most existing methods assume network topologies and descriptive contents carry the same or compatible information of node group membership, restricting them to one topic per community, which is generally violated in real networks. The third issue is that the existing methods use top ranked words or phrases to label topics when interpreting communities, which is often inadequate for comprehension. To address these issues altogether, we propose a new Bayesian probabilistic approach for modeling real networks and developing an efficient variational algorithm for model inference. Our new method explores the intrinsic correlation between communities and topics to discover link communities and extract semantically meaningful community summaries at the same time. If desired, it is able to derive more than one topical summary per community to provide rich explanations. We present experimental results to show the effectiveness of our new approach and evaluate the method by a case study.
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