Investigating latent interactions beyond direct connections is essential for analyzing complex networks.However,traditional graph structures often fail to capture complex relationships,especially in the high-order int...Investigating latent interactions beyond direct connections is essential for analyzing complex networks.However,traditional graph structures often fail to capture complex relationships,especially in the high-order interactions among multiple individuals.To address this issue,we extend the graph isomorphism network(GIN)framework to hypergraphs,treat nodes as self-hyperedges,and propose the self-hypergraph isomorphism network(SHGIN).Meanwhile,the hypergraph Weisfeiler–Lehman(WL)test is also proposed to distinguish different isomorphisms of hypergraphs and improve the representation power of hypergraph neural networks.Extensive experiments on co-authorship and co-citation networks demonstrate the effectiveness of SHGIN.The results indicate that our model displays superior hypernode classification accuracy compared to traditional graph neural networks in semi-supervised learning(SSL).Furthermore,it surpasses existing hypergraph neural network models in co-authorship datasets,highlighting its effectiveness in capturing high-order relationships in complex networks.展开更多
基金supported in part by the National Key R&D Program of China under Grant 2022ZD0120004in part by Zhishan Youth Scholar Program+1 种基金in part by the National Natural Science Foundation of China under Grant 62233004,Grant 62273090,and Grant 62073076in part by the Jiangsu Provincial Scientific Research Center of Applied Mathematics under Grant BK20233002
文摘Investigating latent interactions beyond direct connections is essential for analyzing complex networks.However,traditional graph structures often fail to capture complex relationships,especially in the high-order interactions among multiple individuals.To address this issue,we extend the graph isomorphism network(GIN)framework to hypergraphs,treat nodes as self-hyperedges,and propose the self-hypergraph isomorphism network(SHGIN).Meanwhile,the hypergraph Weisfeiler–Lehman(WL)test is also proposed to distinguish different isomorphisms of hypergraphs and improve the representation power of hypergraph neural networks.Extensive experiments on co-authorship and co-citation networks demonstrate the effectiveness of SHGIN.The results indicate that our model displays superior hypernode classification accuracy compared to traditional graph neural networks in semi-supervised learning(SSL).Furthermore,it surpasses existing hypergraph neural network models in co-authorship datasets,highlighting its effectiveness in capturing high-order relationships in complex networks.
