{"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/unsupervised-inductive-whole-graph-embedding","title":"Unsupervised Inductive Graph-Level Representation Learning via Graph-Graph Proximity","arxiv_id":"1904.01098","date":"2019-04-01","proceeding":null,"authors":["Yunsheng Bai","Hao Ding","Yang Qiao","Agustin Marinovic","Ken Gu","Ting Chen","Yizhou Sun","Wei Wang"],"abstract":"We introduce a novel approach to graph-level representation learning, which is to embed an entire graph into a vector space where the embeddings of two graphs preserve their graph-graph proximity. Our approach, UGRAPHEMB, is a general framework that provides a novel means to performing graph-level embedding in a completely unsupervised and inductive manner. The learned neural network can be considered as a function that receives any graph as input, either seen or unseen in the training set, and transforms it into an embedding. A novel graph-level embedding generation mechanism called Multi-Scale Node Attention (MSNA), is proposed. Experiments on five real graph datasets show that UGRAPHEMB achieves competitive accuracy in the tasks of graph classification, similarity ranking, and graph visualization.","url_abs":"https://arxiv.org/abs/1904.01098v2","url_pdf":"https://arxiv.org/pdf/1904.01098v2.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":"unsupervised-inductive-whole-graph-embedding","repo_url":"https://github.com/yunshengb/UGraphEmb","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"graph-classification","task_name":"Graph Classification"},{"task_slug":"graph-embedding","task_name":"Graph Embedding"},{"task_slug":"graph-similarity","task_name":"Graph Similarity"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/graph-classification-on-imdb-m","task":"Graph Classification","dataset":"IMDb-M","model":"UGraphEmb-F","rank_in_archive_order":17,"of":36,"metrics":{"Accuracy":"50.97%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-imdb-m","task":"Graph Classification","dataset":"IMDb-M","model":"UGraphEmb","rank_in_archive_order":23,"of":36,"metrics":{"Accuracy":"50.06%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-nci109","task":"Graph Classification","dataset":"NCI109","model":"UGraphEmb-F","rank_in_archive_order":29,"of":38,"metrics":{"Accuracy":"74.48"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-nci109","task":"Graph Classification","dataset":"NCI109","model":"UGraphEmb","rank_in_archive_order":36,"of":38,"metrics":{"Accuracy":"69.17"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-ptc","task":"Graph Classification","dataset":"PTC","model":"UGraphEmb-F","rank_in_archive_order":5,"of":37,"metrics":{"Accuracy":"73.56%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-ptc","task":"Graph Classification","dataset":"PTC","model":"UGraphEmb","rank_in_archive_order":10,"of":37,"metrics":{"Accuracy":"72.54%"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-reddit-multi-12k","task":"Graph Classification","dataset":"REDDIT-MULTI-12K","model":"UGraphEmb-F","rank_in_archive_order":2,"of":3,"metrics":{"Accuracy":"41.84"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-reddit-multi-12k","task":"Graph Classification","dataset":"REDDIT-MULTI-12K","model":"UGraphEmb","rank_in_archive_order":3,"of":3,"metrics":{"Accuracy":"39.97"},"uses_additional_data":false},{"leaderboard":"/sota/graph-classification-on-web","task":"Graph Classification","dataset":"Web","model":"UGraphEmb-F","rank_in_archive_order":1,"of":1,"metrics":{"Accuracy":"45.03"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.01098","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}