{"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/hyper-sagnn-a-self-attention-based-graph-1","title":"Hyper-SAGNN: a self-attention based graph neural network for hypergraphs","arxiv_id":"1911.02613","date":"2019-11-06","proceeding":"ICLR 2020 1","authors":["Ruochi Zhang","Yuesong Zou","Jian Ma"],"abstract":"Graph representation learning for hypergraphs can be used to extract patterns among higher-order interactions that are critically important in many real world problems. Current approaches designed for hypergraphs, however, are unable to handle different types of hypergraphs and are typically not generic for various learning tasks. Indeed, models that can predict variable-sized heterogeneous hyperedges have not been available. Here we develop a new self-attention based graph neural network called Hyper-SAGNN applicable to homogeneous and heterogeneous hypergraphs with variable hyperedge sizes. We perform extensive evaluations on multiple datasets, including four benchmark network datasets and two single-cell Hi-C datasets in genomics. We demonstrate that Hyper-SAGNN significantly outperforms the state-of-the-art methods on traditional tasks while also achieving great performance on a new task called outsider identification. Hyper-SAGNN will be useful for graph representation learning to uncover complex higher-order interactions in different applications.","url_abs":"https://arxiv.org/abs/1911.02613v1","url_pdf":"https://arxiv.org/pdf/1911.02613v1.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":"hyper-sagnn-a-self-attention-based-graph-1","repo_url":"https://github.com/ma-compbio/Hyper-SAGNN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"graph-neural-network","task_name":"Graph Neural Network"},{"task_slug":"graph-representation-learning","task_name":"Graph Representation Learning"},{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[{"method_slug":"graph-neural-network","method_name":"Graph Neural Network"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/link-prediction-on-gps","task":"Link Prediction","dataset":"GPS","model":"Hyper-SAGNN-E","rank_in_archive_order":1,"of":2,"metrics":{"AUC":"0.9520000000000001","AUPR":"0.7979999999999999"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-gps","task":"Link Prediction","dataset":"GPS","model":"Hyper-SAGNN-W","rank_in_archive_order":2,"of":2,"metrics":{"AUC":"0.922","AUPR":"0.722"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-movielens-1m","task":"Link Prediction","dataset":"MovieLens 1M","model":"Hyper-SAGNN-W","rank_in_archive_order":1,"of":2,"metrics":{"AUC":"0.93","AUPR":"0.81"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-movielens-1m","task":"Link Prediction","dataset":"MovieLens 1M","model":"Hyper-SAGNN-E","rank_in_archive_order":2,"of":2,"metrics":{"AUPR":"0.7929999999999999"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-wordnet","task":"Link Prediction","dataset":"WordNet","model":"Hyper-SAGNN-E","rank_in_archive_order":6,"of":7,"metrics":{"AUC":"0.89","AUPR":"0.705"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-wordnet","task":"Link Prediction","dataset":"WordNet","model":"Hyper-SAGNN-W","rank_in_archive_order":7,"of":7,"metrics":{"AUC":"0.88","AUPR":"0.706"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1911.02613","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.02613"}},"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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