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pro vyhledávání: '"Lei, Minglong"'
Combinatorial optimization (CO) on graphs is a classic topic that has been extensively studied across many scientific and industrial fields. Recently, solving CO problems on graphs through learning methods has attracted great attention. Advanced deep
Externí odkaz:
http://arxiv.org/abs/2312.11547
Advanced graph neural networks have shown great potentials in graph classification tasks recently. Different from node classification where node embeddings aggregated from local neighbors can be directly used to learn node labels, graph classificatio
Externí odkaz:
http://arxiv.org/abs/2203.07691
Graph convolutional networks have made great progress in graph-based semi-supervised learning. Existing methods mainly assume that nodes connected by graph edges are prone to have similar attributes and labels, so that the features smoothed by local
Externí odkaz:
http://arxiv.org/abs/2112.01174
Graph auto-encoders have proved to be useful in network embedding task. However, current models only consider explicit structures and fail to explore the informative latent structures cohered in networks. To address this issue, we propose a latent ne
Externí odkaz:
http://arxiv.org/abs/2109.15257
Publikováno v:
In Information Sciences January 2024 654
In network embedding, random walks play a fundamental role in preserving network structures. However, random walk based embedding methods have two limitations. First, random walk methods are fragile when the sampling frequency or the number of node s
Externí odkaz:
http://arxiv.org/abs/1805.03504
Publikováno v:
In Neural Networks November 2022 155:318-327
Publikováno v:
In Neural Networks October 2022 154:413-424
Publikováno v:
In Neurocomputing 28 August 2022 501:778-789
Publikováno v:
In Information Sciences August 2022 608:1301-1316