{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T02:50:15Z","timestamp":1783738215684,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":92,"publisher":"ACM","funder":[{"DOI":"10.13039\/501100006374","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. U24B20143, No. 62372030"],"award-info":[{"award-number":["No. U24B20143, No. 62372030"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,8,3]]},"DOI":"10.1145\/3711896.3737006","type":"proceedings-article","created":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T13:30:13Z","timestamp":1754055013000},"page":"733-744","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Improving Subgraph Matching by Combining Algorithms and Graph Neural Networks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-5486-1664","authenticated-orcid":false,"given":"Shuyang","family":"Guo","sequence":"first","affiliation":[{"name":"SKLCCSE, Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-1354-5407","authenticated-orcid":false,"given":"Wenjin","family":"Xie","sequence":"additional","affiliation":[{"name":"SKLCCSE, Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4919-8989","authenticated-orcid":false,"given":"Ping","family":"Lu","sequence":"additional","affiliation":[{"name":"SKLCCSE, Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-8286-1014","authenticated-orcid":false,"given":"Ting","family":"Deng","sequence":"additional","affiliation":[{"name":"SKLCCSE, Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1207-0300","authenticated-orcid":false,"given":"Richong","family":"Zhang","sequence":"additional","affiliation":[{"name":"SKLCCSE, Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5152-0055","authenticated-orcid":false,"given":"Jianxin","family":"Li","sequence":"additional","affiliation":[{"name":"SKLCCSE, Beihang University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0834-6181","authenticated-orcid":false,"given":"Xiangping","family":"Huang","sequence":"additional","affiliation":[{"name":"TravelSky Technology Limited, Beijing, China and Beijing Engineering Research Center of Civil Aviation Big Data, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-4080-0776","authenticated-orcid":false,"given":"Zhongyi","family":"Liu","sequence":"additional","affiliation":[{"name":"Travelsky Technology Limited, Beijing, China and Beijing Engineering Research Center of Civil Aviation Big Data, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,8,3]]},"reference":[{"key":"e_1_3_2_2_1_1","unstructured":"2021. Citation. https:\/\/aminer.cn\/citation."},{"key":"e_1_3_2_2_2_1","unstructured":"2021. Citeseerx. citeseerx.ist.psu.edu."},{"key":"e_1_3_2_2_3_1","unstructured":"2021. DBLP. https:\/\/dblp.uni-trier.de\/xml\/."},{"key":"e_1_3_2_2_4_1","unstructured":"2021. DBpedia. https:\/\/en.wikipedia.org\/wiki\/DBpedia."},{"key":"e_1_3_2_2_5_1","unstructured":"2021. IMDB. https:\/\/developer.imdb.com\/non-commercial-datasets\/."},{"key":"e_1_3_2_2_6_1","unstructured":"2021. YAGO. https:\/\/yago-knowledge.org\/."},{"key":"e_1_3_2_2_7_1","unstructured":"2023. Neo4j 5.10.0. https:\/\/neo4j.com\/."},{"key":"e_1_3_2_2_8_1","unstructured":"2025. Wikidata: SPARQL query service\/queries\/examples. https:\/\/www.wikidata.org\/wiki\/Wikidata:SPARQL_query_service\/queries\/examples"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589312"},{"key":"e_1_3_2_2_10_1","first-page":"46","article-title":"Weisfeiler and leman go relational","author":"Barcel\u00f3 Pablo","year":"2022","unstructured":"Pablo Barcel\u00f3, Mikhail Galkin, Christopher Morris, and Miguel A. Romero Orth. 2022. Weisfeiler and leman go relational. In LoG. 46-1.","journal-title":"LoG."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442322.3442324"},{"key":"e_1_3_2_2_12_1","first-page":"1447","article-title":"CECI","author":"Bhattarai Bibek","year":"2019","unstructured":"Bibek Bhattarai, Hang Liu, and H. Howie Huang. 2019. CECI: Compact Embedding Cluster Index for Scalable Subgraph Matching. In SIGMOD. 1447-1462.","journal-title":"Compact Embedding Cluster Index for Scalable Subgraph Matching. In SIGMOD."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"publisher","DOI":"10.1145\/2882903.2915236"},{"key":"e_1_3_2_2_14_1","volume-title":"S-7","author":"Bonnici Vincenzo","year":"2013","unstructured":"Vincenzo Bonnici, Rosalba Giugno, Alfredo Pulvirenti, Dennis E. Shasha, and Alfredo Ferro. 2013. A subgraph isomorphism algorithm and its application to biochemical data. BMC Bioinform. 14, S-7 (2013), S13."},{"key":"e_1_3_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3154319"},{"key":"e_1_3_2_2_16_1","first-page":"48","article-title":"Detecting social positions using simulation","author":"Brynielsson Joel","year":"2010","unstructured":"Joel Brynielsson, Johanna H\u00f6gberg, Lisa Kaati, Christian M\u00e5rtenson, and Pontus Svenson. 2010. Detecting social positions using simulation. In ASONAM. 48-55.","journal-title":"ASONAM."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF01305232"},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2019.07.001"},{"key":"e_1_3_2_2_19_1","first-page":"128","article-title":"Introducing VF3: A new algorithm for subgraph isomorphism","author":"Carletti Vincenzo","year":"2017","unstructured":"Vincenzo Carletti, Pasquale Foggia, Alessia Saggese, and Mario Vento. 2017. Introducing VF3: A new algorithm for subgraph isomorphism. In GbRPR. 128-139.","journal-title":"GbRPR."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bty662"},{"key":"e_1_3_2_2_21_1","first-page":"1006","article-title":"Graph pattern matching: A join\/semijoin approach","volume":"23","author":"Cheng Jiefeng","year":"2010","unstructured":"Jiefeng Cheng, Jeffrey Xu Yu, and S Yu Philip. 2010. Graph pattern matching: A join\/semijoin approach. TKDE 23, 7 (2010), 1006-1021.","journal-title":"TKDE"},{"key":"e_1_3_2_2_22_1","first-page":"1033","article-title":"Top-k graph pattern matching over large graphs","author":"Cheng Jiefeng","year":"2013","unstructured":"Jiefeng Cheng, Xianggang Zeng, and Jeffrey Xu Yu. 2013. Top-k graph pattern matching over large graphs. In ICDE. 1033-1044.","journal-title":"ICDE."},{"key":"e_1_3_2_2_23_1","first-page":"143","article-title":"The complexity of theorem-proving procedures","author":"Cook Stephen A","year":"2023","unstructured":"Stephen A Cook. 2023. The complexity of theorem-proving procedures. In Logic, Automata, and Computational Complexity: The Works of Stephen A. Cook. 143-152.","journal-title":"Logic, Automata, and Computational Complexity: The Works of Stephen A. Cook."},{"key":"e_1_3_2_2_24_1","volume-title":"DL","volume":"3515","author":"Dragovic Nikola","year":"2023","unstructured":"Nikola Dragovic, Cem Okulmus, and Magdalena Ortiz. 2023. Rewriting Ontology-Mediated Navigational Queries into Cypher. In DL, Vol. 3515."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2017.11.008"},{"key":"e_1_3_2_2_26_1","first-page":"1281","article-title":"Neighbor-aware search for approximate labeled graph matching using the chi-square statistics","author":"Dutta Sourav","year":"2017","unstructured":"Sourav Dutta, Pratik Nayek, and Arnab Bhattacharya. 2017. Neighbor-aware search for approximate labeled graph matching using the chi-square statistics. In WWW. 1281-1290.","journal-title":"WWW."},{"key":"e_1_3_2_2_27_1","doi-asserted-by":"publisher","DOI":"10.14778\/1920841.1920878"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.14778\/1920841.1920986"},{"key":"e_1_3_2_2_29_1","volume-title":"Making It Tractable to Detect and Correct Errors in Graphs. TODS 49, 4","author":"Fan Wenfei","year":"2024","unstructured":"Wenfei Fan, Kehan Pang, Ping Lu, and Chao Tian. 2024. Making It Tractable to Detect and Correct Errors in Graphs. TODS 49, 4 (2024), 16:1-16:75."},{"key":"e_1_3_2_2_30_1","first-page":"1316","article-title":"ExpFinder: Finding experts by graph pattern matching","author":"Fan Wenfei","year":"2013","unstructured":"Wenfei Fan, Xin Wang, and Yinghui Wu. 2013. ExpFinder: Finding experts by graph pattern matching. In ICDE. 1316-1319.","journal-title":"ICDE."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/2489791"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.14778\/2732977.2732983"},{"key":"e_1_3_2_2_33_1","first-page":"1","article-title":"Distributed and scalable graph pattern matching: Models and algorithms","volume":"1","author":"Fard Arash","year":"2014","unstructured":"Arash Fard, M Usman Nisar, John A Miller, and Lakshmish Ramaswamy. 2014. Distributed and scalable graph pattern matching: Models and algorithms. IJBD 1, 1 (2014), 1-14.","journal-title":"IJBD"},{"key":"e_1_3_2_2_34_1","first-page":"4776","article-title":"How powerful are k-hop message passing graph neural networks","volume":"35","author":"Feng Jiarui","year":"2022","unstructured":"Jiarui Feng, Yixin Chen, Fuhai Li, Anindya Sarkar, and Muhan Zhang. 2022. How powerful are k-hop message passing graph neural networks. NeurIPS 35 (2022), 4776-4790.","journal-title":"NeurIPS"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1093\/comjnl\/bxu159"},{"key":"e_1_3_2_2_36_1","first-page":"3419","article-title":"Generalization and Representational Limits of Graph Neural Networks","volume":"119","author":"Garg Vikas K.","year":"2020","unstructured":"Vikas K. Garg, Stefanie Jegelka, and Tommi S. Jaakkola. 2020. Generalization and Representational Limits of Graph Neural Networks. In ICML, Vol. 119. 3419-3430.","journal-title":"ICML"},{"key":"e_1_3_2_2_37_1","volume-title":"Reutter","author":"Geerts Floris","year":"2022","unstructured":"Floris Geerts and Juan L. Reutter. 2022. Expressiveness and Approximation Properties of Graph Neural Networks. ICLR (2022)."},{"key":"e_1_3_2_2_38_1","first-page":"20","article-title":"On the Expressive Power of Message-Passing Neural Networks as Global Feature Map Transformers","volume":"13388","author":"Geerts Floris","year":"2022","unstructured":"Floris Geerts, Jasper Steegmans, and Jan Van den Bussche. 2022. On the Expressive Power of Message-Passing Neural Networks as Global Feature Map Transformers. In FoIKS, Vol. 13388. 20-34.","journal-title":"FoIKS"},{"key":"e_1_3_2_2_39_1","first-page":"1","article-title":"The Descriptive Complexity of Graph Neural Networks","author":"Grohe Martin","year":"2023","unstructured":"Martin Grohe. 2023. The Descriptive Complexity of Graph Neural Networks. In LICS. 1-14.","journal-title":"LICS."},{"key":"e_1_3_2_2_40_1","first-page":"1024","article-title":"Inductive Representation Learning on Large Graphs","author":"Hamilton William L.","year":"2017","unstructured":"William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NIPS. 1024-1034.","journal-title":"NIPS."},{"key":"e_1_3_2_2_41_1","first-page":"1429","article-title":"Efficient Subgraph Matching: Harmonizing Dynamic Programming, Adaptive Matching Order, and Failing Set Together","author":"Han Myoungji","year":"2019","unstructured":"Myoungji Han, Hyunjoon Kim, Geonmo Gu, Kunsoo Park, and Wook-Shin Han. 2019. Efficient Subgraph Matching: Harmonizing Dynamic Programming, Adaptive Matching Order, and Failing Set Together. In SIGMOD. 1429-1446.","journal-title":"SIGMOD."},{"key":"e_1_3_2_2_42_1","first-page":"337","article-title":"Turboiso: towards ultrafast and robust subgraph isomorphism search in large graph databases","author":"Han Wook-Shin","year":"2013","unstructured":"Wook-Shin Han, Jinsoo Lee, and Jeong-Hoon Lee. 2013. Turboiso: towards ultrafast and robust subgraph isomorphism search in large graph databases. In SIGMOD. 337-348.","journal-title":"SIGMOD."},{"key":"e_1_3_2_2_43_1","first-page":"4040","volume-title":"ICML","volume":"139","author":"Hang Mengyue","year":"2021","unstructured":"Mengyue Hang, Jennifer Neville, and Bruno Ribeiro. 2021. A Collective Learning Framework to Boost GNN Expressiveness for Node Classification. In ICML, Vol. 139. 4040-4050."},{"key":"e_1_3_2_2_44_1","first-page":"405","article-title":"Graphs-at-a-time: query language and access methods for graph databases","author":"He Huahai","year":"2008","unstructured":"Huahai He and Ambuj K. Singh. 2008. Graphs-at-a-time: query language and access methods for graph databases. In SIGMOD. ACM, 405-418.","journal-title":"SIGMOD. ACM"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.14778\/2535569.2448952"},{"key":"e_1_3_2_2_46_1","first-page":"925","article-title":"Versatile Equivalences: Speeding up Subgraph Query Processing and Subgraph Matching","author":"Kim Hyunjoon","year":"2021","unstructured":"Hyunjoon Kim, Yunyoung Choi, Kunsoo Park, Xuemin Lin, Seok-Hee Hong, and Wook-Shin Han. 2021. Versatile Equivalences: Speeding up Subgraph Query Processing and Subgraph Matching. In SIGMOD. 925-937.","journal-title":"SIGMOD."},{"key":"e_1_3_2_2_47_1","first-page":"1","article-title":"Sub-GMN","author":"Lan Zixun","year":"2023","unstructured":"Zixun Lan, Limin Yu, Linglong Yuan, Zili Wu, Qiang Niu, and Fei Ma. 2023. Sub-GMN: The Neural Subgraph Matching Network Model. In CISP-BMEI. 1-7.","journal-title":"The Neural Subgraph Matching Network Model. In CISP-BMEI."},{"key":"e_1_3_2_2_48_1","volume-title":"Hismatch: Historical structure matching based temporal knowledge graph reasoning. arXiv preprint arXiv:2210.09708","author":"Li Zixuan","year":"2022","unstructured":"Zixuan Li, Zhongni Hou, Saiping Guan, Xiaolong Jin, Weihua Peng, Long Bai, Yajuan Lyu, Wei Li, Jiafeng Guo, and Xueqi Cheng. 2022. Hismatch: Historical structure matching based temporal knowledge graph reasoning. arXiv preprint arXiv:2210.09708 (2022)."},{"key":"e_1_3_2_2_49_1","volume-title":"GRAPES-DD: exploiting decision diagrams for index-driven search in biological graph databases. BMC bioinformatics 22","author":"Licheri Nicola","year":"2021","unstructured":"Nicola Licheri, Vincenzo Bonnici, Marco Beccuti, and Rosalba Giugno. 2021. GRAPES-DD: exploiting decision diagrams for index-driven search in biological graph databases. BMC bioinformatics 22 (2021), 1-24."},{"key":"e_1_3_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i7.20725"},{"key":"e_1_3_2_2_51_1","first-page":"22454","volume-title":"ICML","volume":"202","author":"Liu Xuanzhou","year":"2023","unstructured":"Xuanzhou Liu, Lin Zhang, Jiaqi Sun, Yujiu Yang, and Haiqin Yang. 2023. D2Match: Leveraging Deep Learning and Degeneracy for Subgraph Matching. In ICML, Vol. 202. 22454-22472."},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.14778\/2095686.2095690"},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/2528937"},{"key":"e_1_3_2_2_54_1","volume-title":"Invariant and equivariant graph networks. ICLR","author":"Maron Haggai","year":"2019","unstructured":"Haggai Maron, Heli Ben-Hamu, Nadav Shamir, and Yaron Lipman. 2019. Invariant and equivariant graph networks. ICLR (2019)."},{"key":"e_1_3_2_2_55_1","first-page":"25275","volume-title":"WL meet VC. In ICML","volume":"202","author":"Morris Christopher","year":"2023","unstructured":"Christopher Morris, Floris Geerts, Jan T\u00f6nshoff, and Martin Grohe. 2023. WL meet VC. In ICML, Vol. 202. 25275-25302."},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014602"},{"key":"e_1_3_2_2_57_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-024-00860-1"},{"key":"e_1_3_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i7.20784"},{"key":"e_1_3_2_2_59_1","volume-title":"Markus Hagenbuchner, and Gabriele Monfardini.","author":"Scarselli Franco","year":"2008","unstructured":"Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2008. The graph neural network model. IEEE transactions on neural networks 20, 1 (2008), 61-80."},{"key":"e_1_3_2_2_60_1","first-page":"593","article-title":"Modeling Relational Data with Graph Convolutional Networks","volume":"10843","author":"Schlichtkrull Michael Sejr","year":"2018","unstructured":"Michael Sejr 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, Vol. 10843. 593-607.","journal-title":"ESWC"},{"key":"e_1_3_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1145\/3661821"},{"key":"e_1_3_2_2_62_1","first-page":"588","article-title":"Multi-constrained top-K graph pattern matching in contextual social graphs","author":"Shi Qun","year":"2017","unstructured":"Qun Shi, Guanfeng Liu, Kai Zheng, An Liu, Zhixu Li, Lei Zhao, and Xiaofang Zhou. 2017. Multi-constrained top-K graph pattern matching in contextual social graphs. In ICWS. 588-595.","journal-title":"ICWS."},{"key":"e_1_3_2_2_63_1","first-page":"220","article-title":"Scaling up subgraph query processing with efficient subgraph matching","author":"Sun Shixuan","year":"2019","unstructured":"Shixuan Sun and Qiong Luo. 2019. Scaling up subgraph query processing with efficient subgraph matching. In ICDE. 220-231.","journal-title":"ICDE."},{"key":"e_1_3_2_2_64_1","first-page":"491","article-title":"Subgraph matching with effective matching order and indexing","volume":"34","author":"Sun Shixuan","year":"2020","unstructured":"Shixuan Sun and Qiong Luo. 2020. Subgraph matching with effective matching order and indexing. TKDE 34, 1 (2020), 491-505.","journal-title":"TKDE"},{"key":"e_1_3_2_2_65_1","doi-asserted-by":"publisher","DOI":"10.14778\/3425879.3425888"},{"key":"e_1_3_2_2_66_1","volume-title":"International Conference on Machine Learning. PMLR, 9448-9457","author":"Teru Komal","year":"2020","unstructured":"Komal Teru, Etienne Denis, and Will Hamilton. 2020. Inductive relation prediction by subgraph reasoning. In International Conference on Machine Learning. PMLR, 9448-9457."},{"key":"e_1_3_2_2_67_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btl571"},{"key":"e_1_3_2_2_68_1","doi-asserted-by":"publisher","DOI":"10.1145\/321921.321925"},{"key":"e_1_3_2_2_69_1","unstructured":"Ivan Vendrov Ryan Kiros Sanja Fidler and Raquel Urtasun. 2016. OrderEmbeddings of Images and Language. In ICLR."},{"key":"e_1_3_2_2_70_1","volume-title":"Equivariant and stable positional encoding for more powerful graph neural networks. ICLR","author":"Wang Haorui","year":"2022","unstructured":"Haorui Wang, Haoteng Yin, Muhan Zhang, and Pan Li. 2022. Equivariant and stable positional encoding for more powerful graph neural networks. ICLR (2022)."},{"key":"e_1_3_2_2_71_1","first-page":"1239","article-title":"Decoupling Representation Learning and Classification for GNN-based Anomaly Detection","author":"Wang Yanling","year":"2021","unstructured":"Yanling Wang, Jing Zhang, Shasha Guo, Hongzhi Yin, Cuiping Li, and Hong Chen. 2021. Decoupling Representation Learning and Classification for GNN-based Anomaly Detection. In SIGIR. 1239-1248.","journal-title":"SIGIR."},{"key":"e_1_3_2_2_72_1","volume-title":"Twin Weisfeiler-Lehman: High Expressive GNNs for Graph Classification. arXiv preprint arXiv:2203.11683","author":"Wang Zhaohui","year":"2022","unstructured":"Zhaohui Wang, Qi Cao, Huawei Shen, Bingbing Xu, and Xueqi Cheng. 2022. Twin Weisfeiler-Lehman: High Expressive GNNs for Graph Classification. arXiv preprint arXiv:2203.11683 (2022)."},{"key":"e_1_3_2_2_73_1","volume-title":"Generalization of Geometric Graph Neural Networks. CoRR abs\/2409.05191","author":"Wang Zhiyang","year":"2024","unstructured":"Zhiyang Wang, Juan Cervi\u00f1o, and Alejandro Ribeiro. 2024. Generalization of Geometric Graph Neural Networks. CoRR abs\/2409.05191 (2024)."},{"key":"e_1_3_2_2_74_1","first-page":"12","article-title":"The reduction of a graph to canonical form and the algebra which appears therein. nti","volume":"2","author":"Weisfeiler Boris","year":"1968","unstructured":"Boris Weisfeiler and Andrei Leman. 1968. The reduction of a graph to canonical form and the algebra which appears therein. nti, Series 2, 9 (1968), 12-16.","journal-title":"Series"},{"key":"e_1_3_2_2_75_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.is.2023.102249"},{"key":"e_1_3_2_2_76_1","first-page":"697","article-title":"Ontology-based subgraph querying","author":"Wu Yinghui","year":"2013","unstructured":"Yinghui Wu, Shengqi Yang, and Xifeng Yan. 2013. Ontology-based subgraph querying. In ICDE. 697-708.","journal-title":"ICDE."},{"key":"e_1_3_2_2_77_1","volume-title":"How powerful are graph neural networks? ICLR","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How powerful are graph neural networks? ICLR (2019)."},{"key":"e_1_3_2_2_78_1","volume-title":"Cross-lingual knowledge graph alignment via graph matching neural network. arXiv preprint arXiv:1905.11605","author":"Xu Kun","year":"2019","unstructured":"Kun Xu, Liwei Wang, Mo Yu, Yansong Feng, Yan Song, Zhiguo Wang, and Dong Yu. 2019. Cross-lingual knowledge graph alignment via graph matching neural network. arXiv preprint arXiv:1905.11605 (2019)."},{"key":"e_1_3_2_2_79_1","first-page":"335","article-title":"Graph indexing: a frequent structure-based approach","author":"Yan Xifeng","year":"2004","unstructured":"Xifeng Yan, Philip S Yu, and Jiawei Han. 2004. Graph indexing: a frequent structure-based approach. In SIGMOD. 335-346.","journal-title":"SIGMOD."},{"key":"e_1_3_2_2_80_1","doi-asserted-by":"publisher","DOI":"10.1145\/3588695"},{"key":"e_1_3_2_2_81_1","doi-asserted-by":"publisher","DOI":"10.14778\/3654621.3654630"},{"key":"e_1_3_2_2_82_1","volume-title":"Representation Learning for Frequent Subgraph Mining. arXiv preprint arXiv:2402.14367","author":"Ying Rex","year":"2024","unstructured":"Rex Ying, Tianyu Fu, Andrew Wang, Jiaxuan You, Yu Wang, and Jure Leskovec. 2024. Representation Learning for Frequent Subgraph Mining. arXiv preprint arXiv:2402.14367 (2024)."},{"key":"e_1_3_2_2_83_1","volume-title":"Neural Subgraph Matching. CoRR abs\/2007.03092","author":"Ying Rex","year":"2020","unstructured":"Rex Ying, Zhaoyu Lou, Jiaxuan You, Chengtao Wen, Arquimedes Canedo, and Jure Leskovec. 2020. Neural Subgraph Matching. CoRR abs\/2007.03092 (2020). https:\/\/arxiv.org\/abs\/2007.03092"},{"key":"e_1_3_2_2_84_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17283"},{"key":"e_1_3_2_2_85_1","doi-asserted-by":"publisher","DOI":"10.14778\/3402707.3402726"},{"key":"e_1_3_2_2_86_1","volume-title":"The expressive power of graph neural networks: A survey. arXiv preprint arXiv:2308.08235","author":"Zhang Bingxu","year":"2023","unstructured":"Bingxu Zhang, Changjun Fan, Shixuan Liu, Kuihua Huang, Xiang Zhao, Jincai Huang, and Zhong Liu. 2023. The expressive power of graph neural networks: A survey. arXiv preprint arXiv:2308.08235 (2023)."},{"key":"e_1_3_2_2_87_1","unstructured":"Bohang Zhang Shengjie Luo Liwei Wang and Di He. 2023. Rethinking the Expressive Power of GNNs via Graph Biconnectivity. In ICLR."},{"key":"e_1_3_2_2_88_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11280-024-01259-2"},{"key":"e_1_3_2_2_89_1","first-page":"2544","article-title":"Eigen-gnn: A graph structure preserving plug-in for gnns","volume":"35","author":"Zhang Ziwei","year":"2021","unstructured":"Ziwei Zhang, Peng Cui, Jian Pei, Xin Wang, and Wenwu Zhu. 2021. Eigen-gnn: A graph structure preserving plug-in for gnns. TKDE 35, 3 (2021), 2544-2555.","journal-title":"TKDE"},{"key":"e_1_3_2_2_90_1","doi-asserted-by":"publisher","DOI":"10.1017\/9781009122115.002"},{"key":"e_1_3_2_2_91_1","first-page":"938","article-title":"Graph indexing: tree delta &gt;= graph","author":"Zhao Peixiang","year":"2007","unstructured":"Peixiang Zhao, Jeffrey Xu Yu, and Philip S Yu. 2007. Graph indexing: tree delta &gt;= graph. In VLDB. 938-949.","journal-title":"VLDB."},{"key":"e_1_3_2_2_92_1","doi-asserted-by":"publisher","DOI":"10.14778\/2732232.2732236"}],"event":{"name":"KDD '25: The 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Toronto ON Canada","acronym":"KDD '25","sponsor":["SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGMOD ACM Special Interest Group on Management of Data"]},"container-title":["Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3711896.3737006","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T18:11:02Z","timestamp":1777572662000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3711896.3737006"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,3]]},"references-count":92,"alternative-id":["10.1145\/3711896.3737006","10.1145\/3711896"],"URL":"https:\/\/doi.org\/10.1145\/3711896.3737006","relation":{},"subject":[],"published":{"date-parts":[[2025,8,3]]},"assertion":[{"value":"2025-08-03","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}