{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T19:26:22Z","timestamp":1776281182886,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":51,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,1,30]],"date-time":"2019-01-30T00:00:00Z","timestamp":1548806400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000098","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["U01HG008488, R01GM115833"],"award-info":[{"award-number":["U01HG008488, R01GM115833"]}],"id":[{"id":"10.13039\/100000098","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["DBI 1565137, DGE1829071, III-1705169, CAREER Award 1741634"],"award-info":[{"award-number":["DBI 1565137, DGE1829071, III-1705169, CAREER Award 1741634"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Snapchat","award":["gift funds"],"award-info":[{"award-number":["gift funds"]}]},{"name":"PPDai","award":["gift fund"],"award-info":[{"award-number":["gift fund"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,1,30]]},"DOI":"10.1145\/3289600.3290967","type":"proceedings-article","created":{"date-parts":[[2019,3,11]],"date-time":"2019-03-11T12:33:01Z","timestamp":1552307581000},"page":"384-392","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":276,"title":["SimGNN"],"prefix":"10.1145","author":[{"given":"Yunsheng","family":"Bai","sequence":"first","affiliation":[{"name":"University of California, Los Angeles, Los Angeles, CA, USA"}]},{"given":"Hao","family":"Ding","sequence":"additional","affiliation":[{"name":"Purdue University, West Lafayette, IN, USA"}]},{"given":"Song","family":"Bian","sequence":"additional","affiliation":[{"name":"Zhejiang University, Zhejiang, China"}]},{"given":"Ting","family":"Chen","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles, Los Angeles, CA, USA"}]},{"given":"Yizhou","family":"Sun","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles, Los Angeles, CA, USA"}]},{"given":"Wei","family":"Wang","sequence":"additional","affiliation":[{"name":"University of California, Los Angeles, Los Angeles, CA, USA"}]}],"member":"320","published-online":{"date-parts":[[2019,1,30]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"On the exact computation of the graph edit distance. Pattern Recognition Letters","author":"Blumenthal David B","year":"2018","unstructured":"David B Blumenthal and Johann Gamper . 2018. On the exact computation of the graph edit distance. Pattern Recognition Letters ( 2018 ). David B Blumenthal and Johann Gamper. 2018. On the exact computation of the graph edit distance. Pattern Recognition Letters (2018)."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2016.10.001"},{"key":"e_1_3_2_1_3_1","first-page":"35","article-title":"What is the distance between graphs","volume":"20","author":"Bunke H","year":"1983","unstructured":"H Bunke . 1983 . What is the distance between graphs . Bulletin of the EATCS , Vol. 20 (1983), 35 -- 39 . H Bunke. 1983. What is the distance between graphs. Bulletin of the EATCS, Vol. 20 (1983), 35--39.","journal-title":"Bulletin of the EATCS"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8655(97)00060-3"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0167-8655(97)00179-7"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"\u00c9variste Daller S\u00e9bastien Bougleux Benoit Ga\u00fcz\u00e8re and Luc Brun. 2018. Approximate graph edit distance by several local searches in parallel. In ICPRAM .  \u00c9variste Daller S\u00e9bastien Bougleux Benoit Ga\u00fcz\u00e8re and Luc Brun. 2018. Approximate graph edit distance by several local searches in parallel. In ICPRAM .","DOI":"10.5220\/0006599901490158"},{"key":"e_1_3_2_1_7_1","unstructured":"Micha\u00ebl Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In NIPS . 3844--3852.   Micha\u00ebl Defferrard Xavier Bresson and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. In NIPS . 3844--3852."},{"key":"e_1_3_2_1_8_1","unstructured":"David K Duvenaud Dougal Maclaurin Jorge Iparraguirre Rafael Bombarell Timothy Hirzel Al\u00e1n Aspuru-Guzik and Ryan P Adams. 2015. Convolutional networks on graphs for learning molecular fingerprints. In NIPS . 2224--2232.   David K Duvenaud Dougal Maclaurin Jorge Iparraguirre Rafael Bombarell Timothy Hirzel Al\u00e1n Aspuru-Guzik and Ryan P Adams. 2015. Convolutional networks on graphs for learning molecular fingerprints. In NIPS . 2224--2232."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.5555\/2009206.2009219"},{"key":"e_1_3_2_1_10_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-38221-5_21"},{"key":"e_1_3_2_1_11_1","volume-title":"On graph kernels: Hardness results and efficient alternatives","author":"Thomas","unstructured":"Thomas G\"artner, Peter Flach , and Stefan Wrobel . 2003. On graph kernels: Hardness results and efficient alternatives . COLT. Springer , 129--143. Thomas G\"artner, Peter Flach, and Stefan Wrobel. 2003. On graph kernels: Hardness results and efficient alternatives. COLT. Springer, 129--143."},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_1_13_1","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec. 2017a. Inductive representation learning on large graphs. NIPS. 1024--1034.   Will Hamilton Zhitao Ying and Jure Leskovec. 2017a. Inductive representation learning on large graphs. NIPS. 1024--1034."},{"key":"e_1_3_2_1_14_1","volume-title":"Representation learning on graphs: Methods and applications. Data Engineering Bulletin","author":"Hamilton William L","year":"2017","unstructured":"William L Hamilton , Rex Ying , and Jure Leskovec . 2017b. Representation learning on graphs: Methods and applications. Data Engineering Bulletin ( 2017 ). William L Hamilton, Rex Ying, and Jure Leskovec. 2017b. Representation learning on graphs: Methods and applications. Data Engineering Bulletin (2017)."},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/N16-1108"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/1014052.1014072"},{"key":"e_1_3_2_1_17_1","unstructured":"Baotian Hu Zhengdong Lu Hang Li and Qingcai Chen. 2014. Convolutional neural network architectures for matching natural language sentences. In NIPS . 2042--2050.   Baotian Hu Zhengdong Lu Hang Li and Qingcai Chen. 2014. Convolutional neural network architectures for matching natural language sentences. In NIPS . 2042--2050."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02278710"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10822-016-9938-8"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1093\/biomet\/30.1-2.81"},{"key":"e_1_3_2_1_21_1","volume-title":"Adam: A method for stochastic optimization. ICLR","author":"Kingma Diederik P","year":"2015","unstructured":"Diederik P Kingma and Jimmy Ba . 2015 . Adam: A method for stochastic optimization. ICLR (2015). Diederik P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. ICLR (2015)."},{"key":"e_1_3_2_1_22_1","volume-title":"Semi-supervised classification with graph convolutional networks. ICLR","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling . 2016. Semi-supervised classification with graph convolutional networks. ICLR ( 2016 ). Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. ICLR (2016)."},{"key":"e_1_3_2_1_23_1","volume-title":"The Hungarian method for the assignment problem. Naval research logistics quarterly","author":"Kuhn Harold W","year":"1955","unstructured":"Harold W Kuhn . 1955. The Hungarian method for the assignment problem. Naval research logistics quarterly , Vol. 2 , 1--2 ( 1955 ), 83--97. Harold W Kuhn. 1955. The Hungarian method for the assignment problem. Naval research logistics quarterly, Vol. 2, 1--2 (1955), 83--97."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219980"},{"key":"e_1_3_2_1_25_1","volume-title":"Soviet physics doklady","author":"Levenshtein Vladimir I","unstructured":"Vladimir I Levenshtein . 1966. Binary codes capable of correcting deletions, insertions, and reversals . In Soviet physics doklady , Vol. 10 . 707--710. Vladimir I Levenshtein. 1966. Binary codes capable of correcting deletions, insertions, and reversals. In Soviet physics doklady, Vol. 10. 707--710."},{"key":"e_1_3_2_1_26_1","volume-title":"Similarity search in graph databases: A multi-layered indexing approach","author":"Liang Yongjiang","unstructured":"Yongjiang Liang and Peixiang Zhao . 2017. Similarity search in graph databases: A multi-layered indexing approach . In ICDE. IEEE , 783--794. Yongjiang Liang and Peixiang Zhao. 2017. Similarity search in graph databases: A multi-layered indexing approach. In ICDE. IEEE, 783--794."},{"key":"e_1_3_2_1_27_1","volume-title":"Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders. IJCAI","author":"Ma Tengfei","year":"2018","unstructured":"Tengfei Ma , Cao Xiao , Jiayu Zhou , and Fei Wang . 2018. Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders. IJCAI ( 2018 ). Tengfei Ma, Cao Xiao, Jiayu Zhou, and Fei Wang. 2018. Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders. IJCAI (2018)."},{"key":"e_1_3_2_1_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/11815921_17"},{"key":"e_1_3_2_1_29_1","unstructured":"Mathias Niepert Mohamed Ahmed and Konstantin Kutzkov. 2016. Learning convolutional neural networks for graphs. ICML. 2014--2023.   Mathias Niepert Mohamed Ahmed and Konstantin Kutzkov. 2016. Learning convolutional neural networks for graphs. ICML. 2014--2023."},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"Giannis Nikolentzos Polykarpos Meladianos and Michalis Vazirgiannis. 2017. Matching Node Embeddings for Graph Similarity. In AAAI. 2429--2435.   Giannis Nikolentzos Polykarpos Meladianos and Michalis Vazirgiannis. 2017. Matching Node Embeddings for Graph Similarity. In AAAI. 2429--2435.","DOI":"10.1609\/aaai.v31i1.10839"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3159652.3159706"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.5555\/1769371.1769377"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-540-89689-0_33"},{"key":"e_1_3_2_1_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.imavis.2008.04.004"},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-38221-5_15"},{"key":"e_1_3_2_1_37_1","volume-title":"Modeling relational data with graph convolutional networks","author":"Schlichtkrull Michael","unstructured":"Michael Schlichtkrull , Thomas N Kipf , Peter Bloem , Rianne van den Berg , Ivan Titov , and Max Welling . 2018. Modeling relational data with graph convolutional networks . In ESWC. Springer , 593--607. Michael Schlichtkrull, Thomas N Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling. 2018. Modeling relational data with graph convolutional networks. In ESWC. Springer, 593--607."},{"key":"e_1_3_2_1_38_1","unstructured":"Richard Socher Danqi Chen Christopher D Manning and Andrew Ng. 2013. Reasoning with neural tensor networks for knowledge base completion. In NIPS . 926--934.   Richard Socher Danqi Chen Christopher D Manning and Andrew Ng. 2013. Reasoning with neural tensor networks for knowledge base completion. In NIPS . 926--934."},{"key":"e_1_3_2_1_39_1","volume-title":"The proof and measurement of association between two things. The American journal of psychology","author":"Spearman Charles","year":"1904","unstructured":"Charles Spearman . 1904. The proof and measurement of association between two things. The American journal of psychology , Vol. 15 , 1 ( 1904 ), 72--101. Charles Spearman. 1904. The proof and measurement of association between two things. The American journal of psychology, Vol. 15, 1 (1904), 72--101."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_3_2_1_41_1","volume-title":"Attention-based Graph Neural Network for Semi-supervised Learning. ICLR","author":"Thekumparampil Kiran K","year":"2018","unstructured":"Kiran K Thekumparampil , Chong Wang , Sewoong Oh , and Li-Jia Li. 2018. Attention-based Graph Neural Network for Semi-supervised Learning. ICLR ( 2018 ). Kiran K Thekumparampil, Chong Wang, Sewoong Oh, and Li-Jia Li. 2018. Attention-based Graph Neural Network for Semi-supervised Learning. ICLR (2018)."},{"key":"e_1_3_2_1_42_1","volume-title":"Graph attention networks. ICLR","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic , Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Lio , and Yoshua Bengio . 2018. Graph attention networks. ICLR ( 2018 ). Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph attention networks. ICLR (2018)."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939753"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDE.2012.28"},{"key":"e_1_3_2_1_45_1","doi-asserted-by":"publisher","DOI":"10.5555\/1403745.1403746"},{"key":"e_1_3_2_1_46_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783417"},{"key":"e_1_3_2_1_47_1","volume-title":"Hierarchical Graph Representation Learning with Differentiable Pooling. arXiv preprint arXiv:1806.08804","author":"Ying Rex","year":"2018","unstructured":"Rex Ying , Jiaxuan You , Christopher Morris , Xiang Ren , William L Hamilton , and Jure Leskovec . 2018. Hierarchical Graph Representation Learning with Differentiable Pooling. arXiv preprint arXiv:1806.08804 ( 2018 ). Rex Ying, Jiaxuan You, Christopher Morris, Xiang Ren, William L Hamilton, and Jure Leskovec. 2018. Hierarchical Graph Representation Learning with Differentiable Pooling. arXiv preprint arXiv:1806.08804 (2018)."},{"key":"e_1_3_2_1_48_1","doi-asserted-by":"publisher","DOI":"10.14778\/1687627.1687631"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"publisher","DOI":"10.14778\/2732232.2732236"},{"key":"e_1_3_2_1_50_1","volume-title":"Substructure Assembling Network for Graph Classification. AAAI","author":"Zhao Xiaohan","year":"2018","unstructured":"Xiaohan Zhao , Bo Zong , Ziyu Guan , Kai Zhang , and Wei Zhao . 2018. Substructure Assembling Network for Graph Classification. AAAI ( 2018 ). Xiaohan Zhao, Bo Zong, Ziyu Guan, Kai Zhang, and Wei Zhao. 2018. Substructure Assembling Network for Graph Classification. AAAI (2018)."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/2505515.2505723"}],"event":{"name":"WSDM '19: The Twelfth ACM International Conference on Web Search and Data Mining","location":"Melbourne VIC Australia","acronym":"WSDM '19","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3289600.3290967","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3289600.3290967","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3289600.3290967","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T01:02:21Z","timestamp":1750208541000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3289600.3290967"}},"subtitle":["A Neural Network Approach to Fast Graph Similarity Computation"],"short-title":[],"issued":{"date-parts":[[2019,1,30]]},"references-count":51,"alternative-id":["10.1145\/3289600.3290967","10.1145\/3289600"],"URL":"https:\/\/doi.org\/10.1145\/3289600.3290967","relation":{},"subject":[],"published":{"date-parts":[[2019,1,30]]},"assertion":[{"value":"2019-01-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}