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- W4380433246 abstract "In this paper, we study the k-clique densest subgraph problem, which detects the subgraph that maximizes the ratio between the number of k-cliques and the number of vertices in it. The problem has been extensively studied in the literature and has many applications in a wide range of fields such as biology and finance. Existing solutions rely heavily on repeatedly computing all the k-cliques, which are not scalable to handle large k values on large-scale graphs. In this paper, by adapting the idea of pivoting, we propose the SCT*-Index to compactly organize the k-cliques. Based on the SCT*-Index, our SCTL algorithm can directly obtain the k-cliques from the index and efficiently achieve near-optimal approximation. To further improve SCTL, we propose SCTL* that includes novel graph reductions and batch-processing optimizations to reduce the search space and decrease the number of visited k-cliques, respectively. As evaluated in our experiments, SCTL* significantly outperform existing approaches by up to two orders of magnitude. In addition, we propose a sampling-based approximate algorithm that can provide reasonable approximations for any k value on billion-scale graphs. Extensive experiments on 12 real-world graphs validate both the efficiency and effectiveness of the proposed techniques." @default.
- W4380433246 created "2023-06-14" @default.
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- W4380433246 date "2023-05-26" @default.
- W4380433246 modified "2023-10-03" @default.
- W4380433246 title "Scaling Up k-Clique Densest Subgraph Detection" @default.
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- W4380433246 doi "https://doi.org/10.1145/3588923" @default.
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