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- W4384648370 abstract "Community search, which looks for query-dependent communities in a graph, is an important task in graph analysis. Existing community search studies address the problem by finding a densely-connected subgraph containing the query. However, many real-world networks are heterogeneous with rich semantics. Queries in heterogeneous networks generally involve in multiple communities with different semantic connections, while returning a single community with mixed semantics has limited applications. In this paper, we revisit the community search problem on heterogeneous networks and introduce a novel paradigm of heterogeneous community search and ranking. We propose to automatically discover the query semantics to enable the search of different semantic communities and develop a comprehensive community evaluation model to support the ranking of results. We build HeteroCS, a heterogeneous community search system with semantic explanation, upon our semantic community model, and deploy it on two real-world graphs. We present a demonstration case to illustrate the novelty and effectiveness of the system." @default.
- W4384648370 created "2023-07-19" @default.
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- W4384648370 date "2023-07-18" @default.
- W4384648370 modified "2023-09-26" @default.
- W4384648370 title "HeteroCS: A Heterogeneous Community Search System With Semantic Explanation" @default.
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- W4384648370 doi "https://doi.org/10.1145/3539618.3591812" @default.
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