In this paper, we propose and study a novel cohesive subgraph model, named (k,s)-core, which requires each user to have at least k familiars or friends (not just acquaintances) in the subgraph. The model considers both user engagement and tie strength to discover strong communities. We compare the (k,s)-core model with k-core and k-truss theoretically and experimentally. We propose efficient algorithms to compute the (k,s)-core and decompose the graph by a particular sub-model k-fami. Extensive experiments show (1) our (k,s)-core and k-fami are effective cohesive subgraph models and (2) the (k,s)-core computation and k-fami decomposition are efficient on various real-life social networks.
CITATION STYLE
Zhang, F., Yuan, L., Zhang, Y., Qin, L., Lin, X., & Zhou, A. (2018). Discovering strong communities with user engagement and tie strength. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10827 LNCS, pp. 425–441). Springer Verlag. https://doi.org/10.1007/978-3-319-91452-7_28
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