{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/hypergraph-neural-networks","title":"Hypergraph Neural Networks","arxiv_id":"1809.09401","date":"2018-09-25","proceeding":null,"authors":["Yifan Feng","Haoxuan You","Zizhao Zhang","Rongrong Ji","Yue Gao"],"abstract":"In this paper, we present a hypergraph neural networks (HGNN) framework for\ndata representation learning, which can encode high-order data correlation in a\nhypergraph structure. Confronting the challenges of learning representation for\ncomplex data in real practice, we propose to incorporate such data structure in\na hypergraph, which is more flexible on data modeling, especially when dealing\nwith complex data. In this method, a hyperedge convolution operation is\ndesigned to handle the data correlation during representation learning. In this\nway, traditional hypergraph learning procedure can be conducted using hyperedge\nconvolution operations efficiently. HGNN is able to learn the hidden layer\nrepresentation considering the high-order data structure, which is a general\nframework considering the complex data correlations. We have conducted\nexperiments on citation network classification and visual object recognition\ntasks and compared HGNN with graph convolutional networks and other traditional\nmethods. Experimental results demonstrate that the proposed HGNN method\noutperforms recent state-of-the-art methods. We can also reveal from the\nresults that the proposed HGNN is superior when dealing with multi-modal data\ncompared with existing methods.","url_abs":"http://arxiv.org/abs/1809.09401v3","url_pdf":"http://arxiv.org/pdf/1809.09401v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"hypergraph-neural-networks","repo_url":"https://github.com/imoonlab/deephypergraph","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"hypergraph-neural-networks","repo_url":"https://github.com/imoonlab/hyper-yolo","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"AGPL-3.0"}},{"paper_slug":"hypergraph-neural-networks","repo_url":"https://github.com/imoonlab/yolov13","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"AGPL-3.0"}},{"paper_slug":"hypergraph-neural-networks","repo_url":"https://github.com/iMoonLab/HGNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"object-recognition","task_name":"Object Recognition"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"graph-convolutional-networks","method_name":"Graph Convolutional Networks"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1809.09401","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1809.09401"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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