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• W4285102016 abstract "Attributed graphs are complex networks with non-trivial topological structures and rich node contents. Recently, a variety of algorithms have been proposed to detect meaningful communities from a given attributed graph by combining these topology and content information. However, few of them is capable of detecting the communities of links, which are better to interpret the node behaviors in forming communities. Furthermore, since nodes are possible to grouped into more than one communities, community detection upon links is natural and parameter-free to incorporate overlap, thus revealing the intrinsic organization of attributed graphs in a more reasonable way. In this work, we propose a novel Bayesian probabilistic model to approximately simulate the generative process of an attributed graph given the prior knowledge regarding the distribution of community labels over links. An efficient variational algorithm, namely VBLCD, is developed to solve the inference problem of the proposed model, thus completing the task of detecting link communities. To evaluate the performance of VBLCD, we have applied it to address two practical applications including document classification and social community detection, and also compared VBLCD with several state-of-the-art algorithms. Experimental results demonstrate the promising performance of VBLCD." @default.
• W4285102016 created "2022-07-14" @default.
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• W4285102016 date "2022-05-27" @default.
• W4285102016 modified "2023-09-26" @default.
• W4285102016 title "Detection of Link Communities in Attributed Graphs via an Approximate Bayesian Generative Model" @default.
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• W4285102016 doi "https://doi.org/10.1109/icaibd55127.2022.9820158" @default.
• W4285102016 hasPublicationYear "2022" @default.
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