Geometric Deep Learning sub-network extraction for Maximum Clique Enumeration.

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.

SUBMITTER: Carchiolo V 

PROVIDER: S-EPMC10790985 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature