Ordering heuristics for k-clique listing
WebWe present a parallel k-clique listing algorithm with improved work bounds (for the same depth) in sparse graphs with low degeneracy or arboricity. We achieve this by introducing and analyzing a new pruning criterion for a backtracking search. WebOrdering Heuristics for k-clique Listing. In Proceedings of the VLDB Endowment. Feng Luo, Bo Li, Xiu-Feng Wan, and Richard H Scheuermann. 2009. Core and periphery structures in protein interaction networks. In BMC bioinformatics, Vol. 10. Springer, S8. Hassan Mahdikhani, Rasoul Shahsavarifar, Rongxing Lu, and David Bremner. 2024.
Ordering heuristics for k-clique listing
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WebTo address these issues, we present a comprehensive comparison of all the state-of-the-art k -clique listing and counting algorithms. We also propose a new color ordering heuristics … WebJul 28, 2024 · Leveraging this bias is a good way to provide prospects with the bits of information they need to make a decision and exit the ‘messy middle’ loop. Category …
WebMotivated by the aforementioned studies, we develop the most efficient algorithm for listing and counting allk-cliques in large sparse real-world graphs, with k being an input parameter. In fact, real-world graphs are often “sparse” and rarely contain very large cliques which allows us to solve such a problem efficiently.
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