{"type":"ScholarlyArticle","title":"Totally Dynamic Hypergraph Neural Networks","authors":[{"name":"Peng Zhou","slug":"peng-zhou","given":"Peng","family":"Zhou","affiliation":"Guangxi Key Lab of Multi-Source Information Mining and Security , Guangxi Normal University , Guilin 541004 , China"},{"name":"Zongqian Wu","slug":"zongqian-wu","given":"Zongqian","family":"Wu","affiliation":"Guangxi Key Lab of Multi-Source Information Mining and Security , Guangxi Normal University , Guilin 541004 , China"},{"name":"Xiangxiang Zeng","slug":"xiangxiang-zeng","given":"Xiangxiang","family":"Zeng","affiliation":"Hunan University"},{"name":"Guoqiu Wen","slug":"guoqiu-wen","given":"Guoqiu","family":"Wen","affiliation":"Guangxi Key Lab of Multi-Source Information Mining and Security , Guangxi Normal University , Guilin 541004 , China"},{"name":"Junbo Ma","slug":"junbo-ma","given":"Junbo","family":"Ma","affiliation":"Guangxi Key Lab of Multi-Source Information Mining and Security , Guangxi Normal University , Guilin 541004 , China"},{"name":"Xiaofeng Zhu","slug":"xiaofeng-zhu","given":"Xiaofeng","family":"Zhu","affiliation":"Guangxi Key Lab of Multi-Source Information Mining and Security , Guangxi Normal University , Guilin 541004 , China; University of Electronic Science and Technology of China"}],"abstract":"Recent dynamic hypergraph neural networks (DHGNNs) are designed to adaptively optimize the hypergraph structure to avoid the dependence on the initial hypergraph structure, thus capturing more hidden information for representation learning. However, most existing DHGNNs cannot adjust the hyperedge number and thus fail to fully explore the underlying hypergraph structure. This paper proposes a new method, namely, totally hypergraph neural network (TDHNN), to adjust the hyperedge number for optimizing the hypergraph structure. Specifically, the proposed method first captures hyperedge feature distribution to obtain dynamical hyperedge features rather than fixed ones, by conducting the sampling from the learned distribution.\r\nThe hypergraph is then constructed based on the attention coefficients of both sampled hyperedges and nodes. The node features are dynamically updated by designing a simple hypergraph convolution algorithm. Experimental results on real datasets demonstrate the effectiveness of the proposed method, compared to SOTA methods. The source code can be accessed via https://github.com/HHW-zhou/TDHNN.","doi":"10.24963/ijcai.2023/275","url":"https://ijcai.org/proceedings/2023/275","pdf":"https://ijcai.org/proceedings/2023/0275.pdf","markdown":"https://ijcai.org/proceedings/2023/275.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/275","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Data Mining","track":"Main Track","number":275,"pages":{"first":2476,"last":2483},"keywords":["Data Mining: DM: Networks","Data Mining: DM: Mining graphs"],"videos":[],"references":[{"position":1,"raw":"[Arya et al., 2020] Devanshu Arya, Deepak K Gupta, Stevan Rudinac, and Marcel Worring. 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