no code implementations • 6 Mar 2024 • Bingheng Li, Xuanting Xie, Haoxiang Lei, Ruiyi Fang, Zhao Kang
Graph Neural Networks (GNNs) have garnered significant attention for their success in learning the representation of homophilic or heterophilic graphs.
1 code implementation • 2 Sep 2022 • Ruiyi Fang, Liangjian Wen, Zhao Kang, Jianzhuang Liu
To this end, we propose a novel Structure-Preserving Graph Representation Learning (SPGRL) method, to fully capture the structure information of graphs.