{"doi":"10.1109/tnnls.2021.3102234","title":"Multilevel Graph Matching Networks for Deep Graph Similarity Learning","abstract":null,"journal":"IEEE Transactions on Neural Networks and Learning Systems","year":2023,"id":590389,"datarank":2.297859134552695,"base_score":4.406719247264253,"endowment":4.406719247264253,"self_citation_contribution":0.6610078870896381,"citation_network_contribution":1.636851247463057,"self_endowment_contribution":0.6610078870896381,"citer_contribution":1.636851247463057,"corpus_percentile":null,"corpus_rank":null,"citation_count":81,"citer_count":75,"citers_with_citation_signal":52,"citers_with_endowment":52,"datacite_reuse_total":1,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":471374,"name":"Lingfei Wu","orcid":"0000-0002-3660-651X","position":1,"is_corresponding":false},{"id":1510578,"name":"Saizhuo Wang","orcid":null,"position":2,"is_corresponding":false},{"id":657121,"name":"Tengfei Ma","orcid":"0000-0002-8916-7370","position":3,"is_corresponding":false},{"id":1510579,"name":"Fangli Xu","orcid":"0000-0003-1519-2909","position":4,"is_corresponding":false},{"id":1510580,"name":"Alex X. Liu","orcid":null,"position":5,"is_corresponding":false},{"id":1510581,"name":"Chunming Wu","orcid":"0000-0001-7958-9687","position":6,"is_corresponding":false},{"id":1510582,"name":"Shouling Ji","orcid":"0000-0003-4268-372X","position":7,"is_corresponding":false},{"id":364192,"name":"Xiang Ling","orcid":"0009-0004-1587-8315","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"Multilevel Graph Matching Networks for Deep Graph Similarity Learning","abstract":"While the celebrated graph neural networks (GNNs) yield effective representations for individual nodes of a graph, there has been relatively less success in extending to the task of graph similarity learning. Recent work on graph similarity learning has considered either global-level graph-graph interactions or low-level node-node interactions, however, ignoring the rich cross-level interactions (e.g., between each node of one graph and the other whole graph). In this article, we propose a multilevel graph matching network (MGMN) framework for computing the graph similarity between any pair of graph-structured objects in an end-to-end fashion. In particular, the proposed MGMN consists of a node-graph matching network (NGMN) for effectively learning cross-level interactions between each node of one graph and the other whole graph, and a siamese GNN to learn global-level interactions between two input graphs. Furthermore, to compensate for the lack of standard benchmark datasets, we have created and collected a set of datasets for both the graph-graph classification and graph-graph regression tasks with different sizes in order to evaluate the effectiveness and robustness of our models. Comprehensive experiments demonstrate that MGMN consistently outperforms state-of-the-art baseline models on both the graph-graph classification and graph-graph regression tasks. Compared with previous work, multilevel graph matching network (MGMN) also exhibits stronger robustness as the sizes of the two input graphs increase.","is_dataset_classified":null,"base_score":0.0,"endowment":0.0,"datacite_reuse_total":1,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"34406948","pmcid":null,"openalex_id":null,"authors":[],"funders":[{"funder_name":"National Key Research and Development Program of China","grant_id":"2020YFB1804705","title":null},{"funder_name":"NSFC","grant_id":"61772466","title":null},{"funder_name":"NSFC","grant_id":"U1936215","title":null},{"funder_name":"Key Research and Development Program of Zhejiang Province","grant_id":"2021C01036","title":null},{"funder_name":"Key Research and Development Program of Zhejiang Province","grant_id":"2020C01021","title":null},{"funder_name":"Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars","grant_id":"LR19F020003","title":null},{"funder_name":"Major Scientific Project of Zhejiang Laboratory","grant_id":"2018FD0ZX01","title":null},{"funder_name":"Fundamental Research Funds for the Central Universities","grant_id":"","title":null}],"total_grants":8,"fwci":null,"citation_percentile":null,"influential_citations":0,"citation_trend":[],"oa_status":"green","license":"https://doi.org/10.15223/policy-029","oa_locations":[{"url":"https://arxiv.org/pdf/2007.04395","host_type":"repository"},{"url":"http://xplorestaging.ieee.org/ielx7/5962385/10036162/09516695.pdf?arnumber=9516695","host_type":"publisher"}],"fields_of_study":[],"mesh_terms":[],"keywords":[],"sdg_mappings":[],"linked_datasets":[{"doi":"10.48550/arxiv.2007.04395","title":"Multilevel Graph Matching Networks for Deep Graph Similarity Learning","publisher":"arXiv","resource_type":"Text"}],"clinical_trials":[],"software_tools":[],"database_accessions":[],"source":"live","citation_network_status":"fetched"},"created_at":"2026-07-24T19:39:07.729779Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}