Abstract
Community clustering means the vertices in networks are often used to cluster into tightly-knit group with a high density of within-group edges and a lower density of between-group edges. However, most community clustering algorithms do not involve the node attributes and relationship, and these approaches lead to inaccuracy clustering. In this paper, we propose two algorithms which involve both node attributes and link structure in social networks based on Girvan-Newman algorithm (GN) and Weighted Informative Graph (WIG). The related experimental results verify the effectiveness of our proposed algorithms.
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Xu, Y., Gao, Y., Li, W. (2016). Community Clustering Based on Weighted Informative Graph. In: Hung, J., Yen, N., Li, KC. (eds) Frontier Computing. Lecture Notes in Electrical Engineering, vol 375. Springer, Singapore. https://doi.org/10.1007/978-981-10-0539-8_22
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DOI: https://doi.org/10.1007/978-981-10-0539-8_22
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