{"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/relational-pooling-for-graph-representations","title":"Relational Pooling for Graph Representations","arxiv_id":"1903.02541","date":"2019-03-06","proceeding":null,"authors":["Ryan L. Murphy","Balasubramaniam Srinivasan","Vinayak Rao","Bruno Ribeiro"],"abstract":"This work generalizes graph neural networks (GNNs) beyond those based on the Weisfeiler-Lehman (WL) algorithm, graph Laplacians, and diffusions. Our approach, denoted Relational Pooling (RP), draws from the theory of finite partial exchangeability to provide a framework with maximal representation power for graphs. RP can work with existing graph representation models and, somewhat counterintuitively, can make them even more powerful than the original WL isomorphism test. Additionally, RP allows architectures like Recurrent Neural Networks and Convolutional Neural Networks to be used in a theoretically sound approach for graph classification. We demonstrate improved performance of RP-based graph representations over state-of-the-art methods on a number of tasks.","url_abs":"https://arxiv.org/abs/1903.02541v2","url_pdf":"https://arxiv.org/pdf/1903.02541v2.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":"relational-pooling-for-graph-representations","repo_url":"https://github.com/PurdueMINDS/RelationalPooling","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"graph-classification","task_name":"Graph Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/drug-discovery-on-hiv-dataset","task":"Drug Discovery","dataset":"HIV dataset","model":"RNN-DFS","rank_in_archive_order":5,"of":5,"metrics":{"AUC":"0.627"},"uses_additional_data":false},{"leaderboard":"/sota/drug-discovery-on-muv","task":"Drug Discovery","dataset":"MUV","model":"RNN-DFS","rank_in_archive_order":5,"of":5,"metrics":{"AUC":"0.648"},"uses_additional_data":false},{"leaderboard":"/sota/drug-discovery-on-tox21","task":"Drug Discovery","dataset":"Tox21","model":"RNN-DFS","rank_in_archive_order":11,"of":11,"metrics":{"AUC":"0.748"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1903.02541","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.02541"}},"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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