{"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/wasserstein-embedding-for-graph-learning","title":"Wasserstein Embedding for Graph Learning","arxiv_id":"2006.09430","date":"2020-06-16","proceeding":"ICLR 2021 1","authors":["Soheil Kolouri","Navid Naderializadeh","Gustavo K. Rohde","Heiko Hoffmann"],"abstract":"We present Wasserstein Embedding for Graph Learning (WEGL), a novel and fast framework for embedding entire graphs in a vector space, in which various machine learning models are applicable for graph-level prediction tasks. We leverage new insights on defining similarity between graphs as a function of the similarity between their node embedding distributions. Specifically, we use the Wasserstein distance to measure the dissimilarity between node embeddings of different graphs. Unlike prior work, we avoid pairwise calculation of distances between graphs and reduce the computational complexity from quadratic to linear in the number of graphs. WEGL calculates Monge maps from a reference distribution to each node embedding and, based on these maps, creates a fixed-sized vector representation of the graph. We evaluate our new graph embedding approach on various benchmark graph-property prediction tasks, showing state-of-the-art classification performance while having superior computational efficiency. The code is available at https://github.com/navid-naderi/WEGL.","url_abs":"https://arxiv.org/abs/2006.09430v2","url_pdf":"https://arxiv.org/pdf/2006.09430v2.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":"wasserstein-embedding-for-graph-learning","repo_url":"https://github.com/navid-naderi/WEGL","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"computational-efficiency","task_name":"Computational Efficiency"},{"task_slug":"graph-classification","task_name":"Graph Classification"},{"task_slug":"graph-embedding","task_name":"Graph Embedding"},{"task_slug":"graph-learning","task_name":"Graph Learning"},{"task_slug":"graph-property-prediction","task_name":"Graph Property Prediction"},{"task_slug":"property-prediction","task_name":"Property Prediction"}],"methods":[{"method_slug":"wegl","method_name":"WEGL"}],"datasets_introduced":[],"methods_introduced":[{"slug":"wegl","name":"WEGL","full_name":"Wasserstein Embedding for Graph Learning"}],"results":[{"leaderboard":"/sota/graph-classification-on-collab","task":"Graph Classification","dataset":"COLLAB","model":"WEGL","rank_in_archive_order":14,"of":39,"metrics":{"Accuracy":"79.8%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-dd","task":"Graph Classification","dataset":"D&D","model":"WEGL","rank_in_archive_order":27,"of":53,"metrics":{"Accuracy":"78.6%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-enzymes","task":"Graph Classification","dataset":"ENZYMES","model":"WEGL","rank_in_archive_order":28,"of":54,"metrics":{"Accuracy":"60.5"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-imdb-b","task":"Graph Classification","dataset":"IMDb-B","model":"WEGL","rank_in_archive_order":23,"of":51,"metrics":{"Accuracy":"75.4%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-imdb-m","task":"Graph Classification","dataset":"IMDb-M","model":"WEGL","rank_in_archive_order":12,"of":36,"metrics":{"Accuracy":"52%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-mutag","task":"Graph Classification","dataset":"MUTAG","model":"WEGL","rank_in_archive_order":41,"of":74,"metrics":{"Accuracy":"88.3%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-nci1","task":"Graph Classification","dataset":"NCI1","model":"WEGL","rank_in_archive_order":45,"of":69,"metrics":{"Accuracy":"76.8%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-proteins","task":"Graph Classification","dataset":"PROTEINS","model":"WEGL","rank_in_archive_order":43,"of":103,"metrics":{"Accuracy":"76.5%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-ptc","task":"Graph Classification","dataset":"PTC","model":"WEGL","rank_in_archive_order":17,"of":37,"metrics":{"Accuracy":"67.5%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-re-m12k","task":"Graph Classification","dataset":"RE-M12K","model":"WEGL","rank_in_archive_order":4,"of":6,"metrics":{"Accuracy":"47.8%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-re-m5k","task":"Graph Classification","dataset":"RE-M5K","model":"WEGL","rank_in_archive_order":3,"of":8,"metrics":{"Accuracy":"55.1%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-reddit-b","task":"Graph Classification","dataset":"REDDIT-B","model":"WEGL","rank_in_archive_order":5,"of":12,"metrics":{"Accuracy":"92"},"uses_additional_data":false},{"leaderboard":"/sota/graph-property-prediction-on-ogbg-molhiv","task":"Graph Property Prediction","dataset":"ogbg-molhiv","model":"WEGL","rank_in_archive_order":34,"of":43,"metrics":{"Ext. data":"No","Number of params":"361064","Test ROC-AUC":"0.7757 ± 0.0111","Validation ROC-AUC":"0.8101 ± 0.0097"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2006.09430","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}