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Geometric Deep Learning sub-network extraction for Maximum Clique Enumeration.
- Source :
- PLoS ONE; 1/16/2024, Vol. 19 Issue 1, p1-12, 12p
- Publication Year :
- 2024
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- DEEP learning
NP-hard problems
MACHINE learning
ALGORITHMS
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 19
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- PLoS ONE
- Publication Type :
- Academic Journal
- Accession number :
- 174821810
- Full Text :
- https://doi.org/10.1371/journal.pone.0296185