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- W4289533907 abstract "Maximum clique identification is a fundamental problem for many domains. In real-world applications, signed information, e.g., friend and enemy, naturally exists in graphs. However, most existing research focuses on unsigned graph analysis. In this paper, we propose a new clique model for signed graphs, named signed <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$theta$</tex> -clique. We show that the problem of identifying the maximum signed <tex xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>$theta$</tex> -clique is NP-hard. To scale for large signed graphs, novel pruning rules and search strategies are developed. Extensive experiments are conducted on 8 real-world graphs to verify the advantages of proposed model and techniques." @default.
- W4289533907 created "2022-08-03" @default.
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- W4289533907 date "2022-05-01" @default.
- W4289533907 modified "2023-10-18" @default.
- W4289533907 title "Maximum Signed $theta$-Clique Identification in Large Signed Graphs (Extended abstract)" @default.
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- W4289533907 doi "https://doi.org/10.1109/icde53745.2022.00123" @default.
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