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Identifying critical nodes in complex networks by graph representation learning. (arXiv:2201.07988v1 [cs.SI])
Jan. 21, 2022, 2:10 a.m. | Enyu Yu, Duanbing Chen, Yan Fu, Yuanyuan Xu
cs.LG updates on arXiv.org arxiv.org
Because of its wide application, critical nodes identification has become an
important research topic at the micro level of network science. Influence
maximization is one of the main problems in critical nodes mining and is
usually handled with heuristics. In this paper, a deep graph learning framework
IMGNN is proposed and the corresponding training sample generation scheme is
designed. The framework takes centralities of nodes in a network as input and
the probability that nodes in the optimal initial spreaders …
More from arxiv.org / cs.LG updates on arXiv.org
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