Abstract
Local community detection is an important research focus in social network analysis. Most existing methods share the intrinsic limitation of utilizing undirected and unweighted networks. In this paper, we propose a novel local community detection algorithm that fuses social relations and topic features in social networks. By defining a new social similarity, the proposed algorithm can effectively reveal the dynamic characteristics in social networks. In addition, the topic similarity is measured by Jensen–Shannon divergence, in which the topics are extracted from the user-generated content by topic models. Extensive experiments conducted on a real social network dataset demonstrate that our proposed algorithm outperforms methods based on social relations or topic features alone.
This work is sponsored by National Basic Research Program of China (973 Program, Grant No.: 2013CB32960601).
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Xu, C., Zhang, H., Lu, B., Wu, S. (2017). Local Community Detection Using Social Relations and Topic Features in Social Networks. In: Sun, M., Wang, X., Chang, B., Xiong, D. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. NLP-NABD CCL 2017 2017. Lecture Notes in Computer Science(), vol 10565. Springer, Cham. https://doi.org/10.1007/978-3-319-69005-6_31
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DOI: https://doi.org/10.1007/978-3-319-69005-6_31
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