Geometric Deep Learning sub-network extraction for Maximum Clique Enumeration

Vincenza Carchiolo, Marco Grassia, Michele Malgeri and Giuseppe Mangioni

PLOS ONE, 2024, vol. 19, issue 1, 1-12

Abstract: The paper presents an algorithm to approach the problem of Maximum Clique Enumeration, a well known NP-hard problem that have several real world applications. The proposed solution, called LGP-MCE, exploits Geometric Deep Learning, a Machine Learning technique on graphs, to filter out nodes that do not belong to maximum cliques and then applies an exact algorithm to the pruned network. To assess the LGP-MCE, we conducted multiple experiments using a substantial dataset of real-world networks, varying in size, density, and other characteristics. We show that LGP-MCE is able to drastically reduce the running time, while retaining all the maximum cliques.

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0296185

DOI: 10.1371/journal.pone.0296185

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