{"type":"ScholarlyArticle","title":"Dynamic Hypergraph Neural Networks","authors":[{"name":"Jianwen Jiang","slug":"jianwen-jiang","given":"Jianwen","family":"Jiang","affiliation":"Beijing National Research Center for Information Science and Technology(BNRist); KLISS , School of Software , Tsinghua University , Beijing , China"},{"name":"Yuxuan Wei","slug":"yuxuan-wei","given":"Yuxuan","family":"Wei","affiliation":"Beijing National Research Center for Information Science and Technology(BNRist); KLISS , School of Software , Tsinghua University , Beijing , China"},{"name":"Yifan Feng","slug":"yifan-feng","given":"Yifan","family":"Feng","affiliation":"School of Information Science and Engineering , Xiamen University , Xiamen , China"},{"name":"Jingxuan Cao","slug":"jingxuan-cao","given":"Jingxuan","family":"Cao","affiliation":"Beijing National Research Center for Information Science and Technology(BNRist); KLISS , School of Software , Tsinghua University , Beijing , China"},{"name":"Yue Gao","slug":"yue-gao","given":"Yue","family":"Gao","affiliation":"Beijing National Research Center for Information Science and Technology(BNRist); KLISS , School of Software , Tsinghua University , Beijing , China"}],"abstract":"In recent years, graph/hypergraph-based deep learning methods have attracted much attention from researchers. These deep learning methods take graph/hypergraph structure as prior knowledge in the model. However, hidden and important relations are not directly represented in the inherent structure. To tackle this issue, we propose a dynamic hypergraph neural networks framework (DHGNN), which is composed of the stacked layers of two modules: dynamic hypergraph construction (DHG) and hypergrpah convolution (HGC). Considering initially constructed hypergraph is probably not a suitable representation for data, the DHG module dynamically updates hypergraph structure on each layer. Then hypergraph convolution is introduced to encode high-order data relations in a hypergraph structure. The HGC module includes two phases: vertex convolution and hyperedge convolution, which are designed to aggregate feature among vertices and hyperedges, respectively. We have evaluated our method on standard datasets, the Cora citation network and Microblog dataset. Our method outperforms state-of-the-art methods. More experiments are conducted to demonstrate the effectiveness and robustness of our method to diverse data distributions.","doi":"10.24963/ijcai.2019/366","url":"https://ijcai.org/proceedings/2019/366","pdf":"https://ijcai.org/proceedings/2019/0366.pdf","markdown":"https://ijcai.org/proceedings/2019/366.md","bibtex":"https://ijcai.org/proceedings/2019/bibtex/366","volume":{"slug":"2019","title":"Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2019","url":"https://ijcai.org/proceedings/2019/","year":2019,"location":"Macao","dates":"10-16 August 2019","editors":"Sarit Kraus","isbn":"978-0-9992411-4-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2019"},"section":"Machine Learning A-L","track":"Main track","number":366,"pages":{"first":2635,"last":2641},"keywords":["Computer Vision: Structural and Model-Based Approaches, Knowledge Representation and Reasoning","Machine Learning: Dimensionality Reduction and Manifold Learning","Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation","Machine Learning: Learning Graphical Models"],"videos":[],"references":[{"position":1,"raw":"[Borth et al., 2013] Damian Borth, Rongrong Ji, Tao Chen, Thomas Breuel, and Shih-Fu Chang. 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