June 6, 2024, 4:42 a.m. | Yang Liu, Peng Zhang, Yang Gao, Chuan Zhou, Zhao Li, Hongyang Chen

cs.LG updates on arXiv.org arxiv.org

arXiv:2406.02872v1 Announce Type: new
Abstract: In recent years, graph neural networks (GNNs) have become increasingly popular for solving NP-hard combinatorial optimization (CO) problems, such as maximum cut and maximum independent set. The core idea behind these methods is to represent a CO problem as a graph and then use GNNs to learn the node/graph embedding with combinatorial information. Although these methods have achieved promising results, given a specific CO problem, the design of GNN architectures still requires heavy manual work …

abstract arxiv automated become core cs.ai cs.lg gnns graph graph neural networks independent learn maximum networks neural networks np-hard optimization popular problem set type

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