{"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/convolutional-set-matching-for-graph","title":"Convolutional Set Matching for Graph Similarity","arxiv_id":"1810.10866","date":"2018-10-23","proceeding":null,"authors":["Yunsheng Bai","Hao Ding","Yizhou Sun","Wei Wang"],"abstract":"We introduce GSimCNN (Graph Similarity Computation via Convolutional Neural\nNetworks) for predicting the similarity score between two graphs. As the core\noperation of graph similarity search, pairwise graph similarity computation is\na challenging problem due to the NP-hard nature of computing many graph\ndistance/similarity metrics. We demonstrate our model using the Graph Edit\nDistance (GED) as the example metric. Experiments on three real graph datasets\ndemonstrate that our model achieves the state-of-the-art performance on graph\nsimilarity search.","url_abs":"http://arxiv.org/abs/1810.10866v3","url_pdf":"http://arxiv.org/pdf/1810.10866v3.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":"convolutional-set-matching-for-graph","repo_url":"https://github.com/snap-stanford/neural-subgraph-learning-gnn","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"graph-similarity","task_name":"Graph Similarity"},{"task_slug":"set-matching","task_name":"set matching"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1810.10866","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}