{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T02:09:06Z","timestamp":1782180546604,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":36,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,4,19]],"date-time":"2021-04-19T00:00:00Z","timestamp":1618790400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,4,19]]},"DOI":"10.1145\/3442381.3449929","type":"proceedings-article","created":{"date-parts":[[2021,6,3]],"date-time":"2021-06-03T19:03:16Z","timestamp":1622746996000},"page":"2058-2068","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":15,"title":["Soft-mask: Adaptive Substructure Extractions for Graph Neural Networks"],"prefix":"10.1145","author":[{"given":"Mingqi","family":"Yang","sequence":"first","affiliation":[{"name":"Dalian University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanming","family":"Shen","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Heng","family":"Qi","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Baocai","family":"Yin","sequence":"additional","affiliation":[{"name":"Dalian University of Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2021,6,3]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"James Atwood and Don Towsley. 2016. Diffusion-convolutional neural networks. In Advances in Neural Information Processing Systems. 1993\u20132001.  James Atwood and Don Towsley. 2016. Diffusion-convolutional neural networks. In Advances in Neural Information Processing Systems. 1993\u20132001."},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/MSP.2017.2693418"},{"key":"e_1_3_2_1_3_1","unstructured":"C\u0103t\u0103lina Cangea Petar Veli\u010dkovi\u0107 Nikola Jovanovi\u0107 Thomas Kipf and Pietro Li\u00f2. 2018. Towards sparse hierarchical graph classifiers. arXiv preprint arXiv:1811.01287(2018).  C\u0103t\u0103lina Cangea Petar Veli\u010dkovi\u0107 Nikola Jovanovi\u0107 Thomas Kipf and Pietro Li\u00f2. 2018. Towards sparse hierarchical graph classifiers. arXiv preprint arXiv:1811.01287(2018)."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"crossref","unstructured":"Deli Chen Yankai Lin Wei Li Peng Li Jie Zhou and Xu Sun. 2020. Measuring and Relieving the Over-Smoothing Problem for Graph Neural Networks from the Topological View.. In AAAI. 3438\u20133445.  Deli Chen Yankai Lin Wei Li Peng Li Jie Zhou and Xu Sun. 2020. Measuring and Relieving the Over-Smoothing Problem for Graph Neural Networks from the Topological View.. In AAAI. 3438\u20133445.","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"e_1_3_2_1_5_1","unstructured":"Zhengdao Chen Soledad Villar Lei Chen and Joan Bruna. 2019. On the equivalence between graph isomorphism testing and function approximation with gnns. In Advances in Neural Information Processing Systems. 15868\u201315876.  Zhengdao Chen Soledad Villar Lei Chen and Joan Bruna. 2019. On the equivalence between graph isomorphism testing and function approximation with gnns. In Advances in Neural Information Processing Systems. 15868\u201315876."},{"key":"e_1_3_2_1_6_1","volume-title":"Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds.","author":"Fey Matthias","year":"2019","unstructured":"Matthias Fey and Jan\u00a0 E. Lenssen . 2019 . Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds. Matthias Fey and Jan\u00a0E. Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds."},{"key":"e_1_3_2_1_7_1","unstructured":"Hongyang Gao and Shuiwang Ji. 2019. Graph U-Nets. arXiv preprint arXiv:1905.05178(2019).  Hongyang Gao and Shuiwang Ji. 2019. Graph U-Nets. arXiv preprint arXiv:1905.05178(2019)."},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.5555\/3305381.3305512"},{"key":"e_1_3_2_1_9_1","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems. 1024\u20131034.  Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems. 1024\u20131034."},{"key":"e_1_3_2_1_10_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a080)","author":"Ivanov Sergey","year":"2018","unstructured":"Sergey Ivanov and Evgeny Burnaev . 2018 . Anonymous Walk Embeddings . In Proceedings of the 35th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a080) , Jennifer Dy and Andreas Krause (Eds.). PMLR, Stockholmsm\u00e4ssan, Stockholm Sweden, 2191\u20132200. http:\/\/proceedings.mlr.press\/v80\/ivanov18a.html Sergey Ivanov and Evgeny Burnaev. 2018. Anonymous Walk Embeddings. In Proceedings of the 35th International Conference on Machine Learning(Proceedings of Machine Learning Research, Vol.\u00a080), Jennifer Dy and Andreas Krause (Eds.). PMLR, Stockholmsm\u00e4ssan, Stockholm Sweden, 2191\u20132200. http:\/\/proceedings.mlr.press\/v80\/ivanov18a.html"},{"key":"e_1_3_2_1_11_1","unstructured":"Kristian Kersting Nils\u00a0M. Kriege Christopher Morris Petra Mutzel and Marion Neumann. 2016. Benchmark Data Sets for Graph Kernels. http:\/\/graphkernels.cs.tu-dortmund.de.  Kristian Kersting Nils\u00a0M. Kriege Christopher Morris Petra Mutzel and Marion Neumann. 2016. Benchmark Data Sets for Graph Kernels. http:\/\/graphkernels.cs.tu-dortmund.de."},{"key":"e_1_3_2_1_12_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980(2014).","author":"Kingma P","year":"2014","unstructured":"Diederik\u00a0 P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980(2014). Diederik\u00a0P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980(2014)."},{"key":"e_1_3_2_1_13_1","unstructured":"Thomas\u00a0N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907(2016).  Thomas\u00a0N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907(2016)."},{"key":"e_1_3_2_1_14_1","unstructured":"Boris Knyazev Graham\u00a0W Taylor and Mohamed\u00a0R Amer. 2019. Understanding attention in graph neural networks. arXiv preprint arXiv:1905.02850(2019).  Boris Knyazev Graham\u00a0W Taylor and Mohamed\u00a0R Amer. 2019. Understanding attention in graph neural networks. arXiv preprint arXiv:1905.02850(2019)."},{"key":"e_1_3_2_1_15_1","unstructured":"Junhyun Lee Inyeop Lee and Jaewoo Kang. 2019. Self-Attention Graph Pooling. arXiv preprint arXiv:1904.08082(2019).  Junhyun Lee Inyeop Lee and Jaewoo Kang. 2019. Self-Attention Graph Pooling. arXiv preprint arXiv:1904.08082(2019)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219980"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"e_1_3_2_1_18_1","unstructured":"Haggai Maron Heli Ben-Hamu Hadar Serviansky and Yaron Lipman. 2019. Provably Powerful Graph Networks. arXiv preprint arXiv:1905.11136(2019).  Haggai Maron Heli Ben-Hamu Hadar Serviansky and Yaron Lipman. 2019. Provably Powerful Graph Networks. arXiv preprint arXiv:1905.11136(2019)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014602"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-015-5517-9"},{"key":"e_1_3_2_1_21_1","volume-title":"International Conference on Machine Learning. 2014\u20132023","author":"Niepert Mathias","year":"2016","unstructured":"Mathias Niepert , Mohamed Ahmed , and Konstantin Kutzkov . 2016 . Learning convolutional neural networks for graphs . In International Conference on Machine Learning. 2014\u20132023 . Mathias Niepert, Mohamed Ahmed, and Konstantin Kutzkov. 2016. Learning convolutional neural networks for graphs. In International Conference on Machine Learning. 2014\u20132023."},{"key":"e_1_3_2_1_22_1","unstructured":"Kenta Oono and Taiji Suzuki. 2019. Graph neural networks exponentially lose expressive power for node classification. arXiv preprint arXiv:1905.10947(2019).  Kenta Oono and Taiji Suzuki. 2019. Graph neural networks exponentially lose expressive power for node classification. arXiv preprint arXiv:1905.10947(2019)."},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"crossref","unstructured":"Raghunathan Ramakrishnan Pavlo\u00a0O Dral Matthias Rupp and O\u00a0Anatole Von\u00a0Lilienfeld. 2014. Quantum chemistry structures and properties of 134 kilo molecules. Scientific data 1(2014) 140022.  Raghunathan Ramakrishnan Pavlo\u00a0O Dral Matthias Rupp and O\u00a0Anatole Von\u00a0Lilienfeld. 2014. Quantum chemistry structures and properties of 134 kilo molecules. Scientific data 1(2014) 140022.","DOI":"10.1038\/sdata.2014.22"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1021\/ci300415d"},{"key":"e_1_3_2_1_25_1","first-page":"2539","article-title":"Weisfeiler-lehman graph kernels","author":"Shervashidze Nino","year":"2011","unstructured":"Nino Shervashidze , Pascal Schweitzer , Erik Jan\u00a0van Leeuwen , Kurt Mehlhorn , and Karsten\u00a0 M Borgwardt . 2011 . Weisfeiler-lehman graph kernels . Journal of Machine Learning Research 12 , Sep (2011), 2539 \u2013 2561 . Nino Shervashidze, Pascal Schweitzer, Erik Jan\u00a0van Leeuwen, Kurt Mehlhorn, and Karsten\u00a0M Borgwardt. 2011. Weisfeiler-lehman graph kernels. Journal of Machine Learning Research 12, Sep (2011), 2539\u20132561.","journal-title":"Journal of Machine Learning Research 12"},{"key":"e_1_3_2_1_26_1","unstructured":"Nino Shervashidze SVN Vishwanathan Tobias Petri Kurt Mehlhorn and Karsten Borgwardt. 2009. Efficient graphlet kernels for large graph comparison. In Artificial Intelligence and Statistics. 488\u2013495.  Nino Shervashidze SVN Vishwanathan Tobias Petri Kurt Mehlhorn and Karsten Borgwardt. 2009. Efficient graphlet kernels for large graph comparison. In Artificial Intelligence and Statistics. 488\u2013495."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.11"},{"key":"e_1_3_2_1_28_1","unstructured":"Saurabh Verma and Zhi-Li Zhang. 2017. Hunt for the unique stable sparse and fast feature learning on graphs. In Advances in Neural Information Processing Systems. 88\u201398.  Saurabh Verma and Zhi-Li Zhang. 2017. Hunt for the unique stable sparse and fast feature learning on graphs. In Advances in Neural Information Processing Systems. 88\u201398."},{"key":"e_1_3_2_1_29_1","first-page":"1201","article-title":"Graph kernels","author":"Vishwanathan N","year":"2010","unstructured":"S\u00a0Vichy\u00a0 N Vishwanathan , Nicol\u00a0 N Schraudolph , Risi Kondor , and Karsten\u00a0 M Borgwardt . 2010 . Graph kernels . Journal of Machine Learning Research 11 , Apr (2010), 1201 \u2013 1242 . S\u00a0Vichy\u00a0N Vishwanathan, Nicol\u00a0N Schraudolph, Risi Kondor, and Karsten\u00a0M Borgwardt. 2010. Graph kernels. Journal of Machine Learning Research 11, Apr (2010), 1201\u20131242.","journal-title":"Journal of Machine Learning Research 11"},{"key":"e_1_3_2_1_30_1","volume-title":"MoleculeNet: a benchmark for molecular machine learning. Chemical science 9, 2","author":"Wu Zhenqin","year":"2018","unstructured":"Zhenqin Wu , Bharath Ramsundar , Evan\u00a0 N Feinberg , Joseph Gomes , Caleb Geniesse , Aneesh\u00a0 S Pappu , Karl Leswing , and Vijay Pande . 2018. MoleculeNet: a benchmark for molecular machine learning. Chemical science 9, 2 ( 2018 ), 513\u2013530. Zhenqin Wu, Bharath Ramsundar, Evan\u00a0N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh\u00a0S Pappu, Karl Leswing, and Vijay Pande. 2018. MoleculeNet: a benchmark for molecular machine learning. Chemical science 9, 2 (2018), 513\u2013530."},{"key":"e_1_3_2_1_31_1","volume-title":"Capsule Graph Neural Network. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=Byl8BnRcYm","author":"Xinyi Zhang","year":"2019","unstructured":"Zhang Xinyi and Lihui Chen . 2019 . Capsule Graph Neural Network. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=Byl8BnRcYm Zhang Xinyi and Lihui Chen. 2019. Capsule Graph Neural Network. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=Byl8BnRcYm"},{"key":"e_1_3_2_1_32_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ryGs6iA5Km","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu , Weihua Hu , Jure Leskovec , and Stefanie Jegelka . 2019 . How Powerful are Graph Neural Networks? . In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ryGs6iA5Km Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=ryGs6iA5Km"},{"key":"e_1_3_2_1_33_1","unstructured":"Keyulu Xu Chengtao Li Yonglong Tian Tomohiro Sonobe Ken-ichi Kawarabayashi and Stefanie Jegelka. 2018. Representation learning on graphs with jumping knowledge networks. arXiv preprint arXiv:1806.03536(2018).  Keyulu Xu Chengtao Li Yonglong Tian Tomohiro Sonobe Ken-ichi Kawarabayashi and Stefanie Jegelka. 2018. Representation learning on graphs with jumping knowledge networks. arXiv preprint arXiv:1806.03536(2018)."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.1145\/2783258.2783417"},{"key":"e_1_3_2_1_35_1","unstructured":"Zhitao Ying Jiaxuan You Christopher Morris Xiang Ren Will Hamilton and Jure Leskovec. 2018. Hierarchical graph representation learning with differentiable pooling. In Advances in Neural Information Processing Systems. 4800\u20134810.  Zhitao Ying Jiaxuan You Christopher Morris Xiang Ren Will Hamilton and Jure Leskovec. 2018. Hierarchical graph representation learning with differentiable pooling. In Advances in Neural Information Processing Systems. 4800\u20134810."},{"key":"e_1_3_2_1_36_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11782"}],"event":{"name":"WWW '21: The Web Conference 2021","location":"Ljubljana Slovenia","acronym":"WWW '21","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the Web Conference 2021"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3442381.3449929","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3442381.3449929","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:24:32Z","timestamp":1750195472000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3442381.3449929"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,19]]},"references-count":36,"alternative-id":["10.1145\/3442381.3449929","10.1145\/3442381"],"URL":"https:\/\/doi.org\/10.1145\/3442381.3449929","relation":{},"subject":[],"published":{"date-parts":[[2021,4,19]]},"assertion":[{"value":"2021-06-03","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}