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• W2550459898 abstract "Maximal clique enumeration is a long-standing problem in graph mining and knowledge discovery. Numerous classic algorithms exist for solving this problem. However, these algorithms focus on enumerating all maximal cliques, which may be computationally impractical and much of the output may be irrelevant to the user. To address this issue, we introduce the problem of knowledge-biased clique enumeration, a query-driven formulation that reduces output space, computation time, and memory usage. Moreover, we introduce a dynamic state space indexing strategy for efficiently processing multiple queries over the same graph. This strategy reduces redundant computations by dynamically indexing the constituent state space generated with each query. Experimental results over real-world networks demonstrate this strategy’s effectiveness at reducing the cumulative query-response time. Although developed in the context of maximal cliques, our techniques could possibly be generalized to other constraint-based graph enumeration tasks." @default.
• W2550459898 created "2016-11-30" @default.
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• W2550459898 date "2016-01-01" @default.
• W2550459898 modified "2023-09-27" @default.
• W2550459898 title "Knowledge-Guided Maximal Clique Enumeration" @default.
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• W2550459898 doi "https://doi.org/10.1007/978-3-319-49586-6_43" @default.
• W2550459898 hasPublicationYear "2016" @default.
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