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- W2772180402 abstract "The task of community detection over complex networks is of paramount importance in a multitude of applications. The present work puts forward a top-to-bottom community identification approach, termed DC-EgoTen, in which an egonet-tensor (EgoTen) based algorithm is developed in a divide-and-conquer (DC) fashion for breaking the network into smaller subgraphs, out of which the underlying communities progressively emerge. In particular, each step of DC-EgoTen forms a multi-dimensional egonet-based representation of the graph, whose induced structure enables casting the task of overlapping community identification as a constrained PARAFAC decomposition. Thanks to the higher representational capacity of tensors, the novel egonet-based representation improves the quality of detected communities by capturing multi-hop connectivity patterns of the network. In addition, the top-to-bottom approach ensures successive refinement of identified communities, so that the desired resolution is achieved. Synthetic as well as real-world tests corroborate the effectiveness of DC-EgoTen." @default.
- W2772180402 created "2017-12-22" @default.
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- W2772180402 date "2017-11-01" @default.
- W2772180402 modified "2023-09-24" @default.
- W2772180402 title "Overlapping Community Detection via Constrained PARAFAC: A Divide and Conquer Approach" @default.
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- W2772180402 doi "https://doi.org/10.1109/icdm.2017.22" @default.
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