{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:28:26Z","timestamp":1763202506212,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":35,"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\/3442381.3449896","type":"proceedings-article","created":{"date-parts":[[2021,6,3]],"date-time":"2021-06-03T19:01:20Z","timestamp":1622746880000},"page":"2438-2447","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Improving Graph Neural Networks with Structural Adaptive Receptive Fields"],"prefix":"10.1145","author":[{"given":"Xiaojun","family":"Ma","sequence":"first","affiliation":[{"name":"Peking University, China"}]},{"given":"Junshan","family":"Wang","sequence":"additional","affiliation":[{"name":"Peking University, China"}]},{"given":"Hanyue","family":"Chen","sequence":"additional","affiliation":[{"name":"Peking University, China"}]},{"given":"Guojie","family":"Song","sequence":"additional","affiliation":[{"name":"Peking University, China"}]}],"member":"320","published-online":{"date-parts":[[2021,6,3]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1132516.1132557"},{"key":"e_1_3_2_1_2_1","first-page":"1297","article-title":"MISEP\u2013Linear and nonlinear ICA based on mutual information","author":"Almeida B","year":"2003","unstructured":"Lu\u00eds\u00a0 B Almeida . 2003 . MISEP\u2013Linear and nonlinear ICA based on mutual information . Journal of Machine Learning Research 4 , Dec (2003), 1297 \u2013 1318 . Lu\u00eds\u00a0B Almeida. 2003. MISEP\u2013Linear and nonlinear ICA based on mutual information. Journal of Machine Learning Research 4, Dec (2003), 1297\u20131318.","journal-title":"Journal of Machine Learning Research 4"},{"key":"e_1_3_2_1_3_1","unstructured":"Anonymous. 2021. Learning Discrete Adaptive Receptive Fields for Graph Convolutional Networks. In Submitted to International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=pHkBwAaZ3UK under review.  Anonymous. 2021. Learning Discrete Adaptive Receptive Fields for Graph Convolutional Networks. In Submitted to International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=pHkBwAaZ3UK under review."},{"key":"e_1_3_2_1_4_1","volume-title":"International Conference on Machine Learning. 531\u2013540","author":"Belghazi Mohamed\u00a0Ishmael","year":"2018","unstructured":"Mohamed\u00a0Ishmael Belghazi , Aristide Baratin , Sai Rajeshwar , Sherjil Ozair , Yoshua Bengio , Aaron Courville , and Devon Hjelm . 2018 . Mutual information neural estimation . In International Conference on Machine Learning. 531\u2013540 . Mohamed\u00a0Ishmael Belghazi, Aristide Baratin, Sai Rajeshwar, Sherjil Ozair, Yoshua Bengio, Aaron Courville, and Devon Hjelm. 2018. Mutual information neural estimation. In International Conference on Machine Learning. 531\u2013540."},{"key":"e_1_3_2_1_5_1","volume-title":"An information-maximization approach to blind separation and blind deconvolution. Neural computation 7, 6","author":"Bell J","year":"1995","unstructured":"Anthony\u00a0 J Bell and Terrence\u00a0 J Sejnowski . 1995. An information-maximization approach to blind separation and blind deconvolution. Neural computation 7, 6 ( 1995 ), 1129\u20131159. Anthony\u00a0J Bell and Terrence\u00a0J Sejnowski. 1995. An information-maximization approach to blind separation and blind deconvolution. Neural computation 7, 6 (1995), 1129\u20131159."},{"key":"e_1_3_2_1_6_1","unstructured":"Benjamin Bloem-Reddy and Yee\u00a0Whye Teh. 2019. Probabilistic symmetry and invariant neural networks. arXiv preprint arXiv:1901.06082(2019).  Benjamin Bloem-Reddy and Yee\u00a0Whye Teh. 2019. Probabilistic symmetry and invariant neural networks. arXiv preprint arXiv:1901.06082(2019)."},{"key":"e_1_3_2_1_7_1","volume-title":"Exact matrix completion via convex optimization. Foundations of Computational mathematics 9, 6","author":"Cand\u00e8s J","year":"2009","unstructured":"Emmanuel\u00a0 J Cand\u00e8s and Benjamin Recht . 2009. Exact matrix completion via convex optimization. Foundations of Computational mathematics 9, 6 ( 2009 ), 717. Emmanuel\u00a0J Cand\u00e8s and Benjamin Recht. 2009. Exact matrix completion via convex optimization. Foundations of Computational mathematics 9, 6 (2009), 717."},{"key":"e_1_3_2_1_8_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. 15894\u201315902.  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. 15894\u201315902."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_1_10_1","volume-title":"Graphite: Iterative Generative Modeling of Graphs. In ICML(Proceedings of Machine Learning Research, Vol.\u00a097). PMLR, 2434\u20132444.","author":"Grover Aditya","year":"2019","unstructured":"Aditya Grover , Aaron Zweig , and Stefano Ermon . 2019 . Graphite: Iterative Generative Modeling of Graphs. In ICML(Proceedings of Machine Learning Research, Vol.\u00a097). PMLR, 2434\u20132444. Aditya Grover, Aaron Zweig, and Stefano Ermon. 2019. Graphite: Iterative Generative Modeling of Graphs. In ICML(Proceedings of Machine Learning Research, Vol.\u00a097). PMLR, 2434\u20132444."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"publisher","DOI":"10.5555\/3294771.3294869"},{"key":"e_1_3_2_1_12_1","unstructured":"Arman Hasanzadeh Ehsan Hajiramezanali Krishna\u00a0R. Narayanan Nick Duffield Mingyuan Zhou and Xiaoning Qian. 2019. Semi-Implicit Graph Variational Auto-Encoders. In NeurIPS. 10711\u201310722.  Arman Hasanzadeh Ehsan Hajiramezanali Krishna\u00a0R. Narayanan Nick Duffield Mingyuan Zhou and Xiaoning Qian. 2019. Semi-Implicit Graph Variational Auto-Encoders. In NeurIPS. 10711\u201310722."},{"key":"e_1_3_2_1_13_1","unstructured":"Yifan Hou Jian Zhang James Cheng Kaili Ma Richard T.\u00a0B. Ma Hongzhi Chen and Ming-Chang Yang. 2020. Measuring and Improving the Use of Graph Information in Graph Neural Networks. In ICLR. OpenReview.net.  Yifan Hou Jian Zhang James Cheng Kaili Ma Richard T.\u00a0B. Ma Hongzhi Chen and Ming-Chang Yang. 2020. Measuring and Improving the Use of Graph Information in Graph Neural Networks. In ICLR. OpenReview.net."},{"key":"e_1_3_2_1_14_1","volume-title":"Independent component analysis: algorithms and applications. Neural networks 13, 4-5","author":"Hyv\u00e4rinen Aapo","year":"2000","unstructured":"Aapo Hyv\u00e4rinen and Erkki Oja . 2000. Independent component analysis: algorithms and applications. Neural networks 13, 4-5 ( 2000 ), 411\u2013430. Aapo Hyv\u00e4rinen and Erkki Oja. 2000. Independent component analysis: algorithms and applications. Neural networks 13, 4-5 (2000), 411\u2013430."},{"key":"e_1_3_2_1_15_1","volume-title":"Proceedings of the 5th International Conference on Learning Representations (ICLR","author":"N.","year":"2017","unstructured":"Thomas\u00a0 N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks . In Proceedings of the 5th International Conference on Learning Representations (ICLR 2017 ). Thomas\u00a0N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In Proceedings of the 5th International Conference on Learning Representations (ICLR 2017)."},{"key":"e_1_3_2_1_16_1","unstructured":"Risi Kondor and Shubhendu Trivedi. 2018. On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups. In ICML(Proceedings of Machine Learning Research Vol.\u00a080). PMLR 2752\u20132760.  Risi Kondor and Shubhendu Trivedi. 2018. On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups. In ICML(Proceedings of Machine Learning Research Vol.\u00a080). PMLR 2752\u20132760."},{"key":"e_1_3_2_1_17_1","volume-title":"Wang Ling, Lei Yu, Zihang Dai, and Dani Yogatama.","author":"Kong Lingpeng","year":"2019","unstructured":"Lingpeng Kong , Cyprien de\u00a0Masson d\u2019Autume , Wang Ling, Lei Yu, Zihang Dai, and Dani Yogatama. 2019 . A Mutual Information Maximization Perspective of Language Representation Learning . arXiv preprint arXiv:1910.08350(2019). Lingpeng Kong, Cyprien de\u00a0Masson d\u2019Autume, Wang Ling, Lei Yu, Zihang Dai, and Dani Yogatama. 2019. A Mutual Information Maximization Perspective of Language Representation Learning. arXiv preprint arXiv:1910.08350(2019)."},{"key":"e_1_3_2_1_18_1","volume-title":"Proceedings of the 4th International Conference on Learning Representations (ICLR","author":"Li Yujia","year":"2016","unstructured":"Yujia Li , Daniel Tarlow , Marc Brockschmidt , and Richard Zemel . 2016 . Gated Graph Sequence Neural Networks . In Proceedings of the 4th International Conference on Learning Representations (ICLR 2016). Yujia Li, Daniel Tarlow, Marc Brockschmidt, and Richard Zemel. 2016. Gated Graph Sequence Neural Networks. In Proceedings of the 4th International Conference on Learning Representations (ICLR 2016)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014424"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dam.2015.06.035"},{"key":"e_1_3_2_1_21_1","unstructured":"Sebastian Nowozin Botond Cseke and Ryota Tomioka. 2016. f-gan: Training generative neural samplers using variational divergence minimization. In Advances in neural information processing systems. 271\u2013279.  Sebastian Nowozin Botond Cseke and Ryota Tomioka. 2016. f-gan: Training generative neural samplers using variational divergence minimization. In Advances in neural information processing systems. 271\u2013279."},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1145\/3366423.3380112"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_1_24_1","series-title":"SIAM review 52, 3","volume-title":"Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization","author":"Recht Benjamin","year":"2010","unstructured":"Benjamin Recht , Maryam Fazel , and Pablo\u00a0 A Parrilo . 2010. Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization . SIAM review 52, 3 ( 2010 ), 471\u2013501. Benjamin Recht, Maryam Fazel, and Pablo\u00a0A Parrilo. 2010. Guaranteed minimum-rank solutions of linear matrix equations via nuclear norm minimization. SIAM review 52, 3 (2010), 471\u2013501."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1109\/ISIT.2002.1023636"},{"key":"e_1_3_2_1_26_1","unstructured":"Balasubramaniam Srinivasan and Bruno Ribeiro. 2020. On the Equivalence between Positional Node Embeddings and Structural Graph Representations. In ICLR. OpenReview.net.  Balasubramaniam Srinivasan and Bruno Ribeiro. 2020. On the Equivalence between Positional Node Embeddings and Structural Graph Representations. In ICLR. OpenReview.net."},{"key":"e_1_3_2_1_27_1","unstructured":"Fan-Yun Sun Jordan Hoffmann Vikas Verma and Jian Tang. 2020. InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. In ICLR. OpenReview.net.  Fan-Yun Sun Jordan Hoffmann Vikas Verma and Jian Tang. 2020. InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization. In ICLR. OpenReview.net."},{"key":"e_1_3_2_1_28_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning, ICML 2019","author":"Tang Da","year":"2019","unstructured":"Da Tang , Dawen Liang , Tony Jebara , and Nicholas Ruozzi . 2019 . Correlated Variational Auto-Encoders . In Proceedings of the 36th International Conference on Machine Learning, ICML 2019 , 9-15 June 2019, Vol.\u00a097. PMLR, 6135\u20136144. http:\/\/proceedings.mlr.press\/v97\/tang19b.html Da Tang, Dawen Liang, Tony Jebara, and Nicholas Ruozzi. 2019. Correlated Variational Auto-Encoders. In Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Vol.\u00a097. PMLR, 6135\u20136144. http:\/\/proceedings.mlr.press\/v97\/tang19b.html"},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_3_2_1_30_1","unstructured":"Michael Tschannen Josip Djolonga Paul\u00a0K Rubenstein Sylvain Gelly and Mario Lucic. 2019. On mutual information maximization for representation learning. arXiv preprint arXiv:1907.13625(2019).  Michael Tschannen Josip Djolonga Paul\u00a0K Rubenstein Sylvain Gelly and Mario Lucic. 2019. On mutual information maximization for representation learning. arXiv preprint arXiv:1907.13625(2019)."},{"key":"e_1_3_2_1_31_1","volume-title":"Proceedings of the 6th International Conference on Learning Representations (ICLR","author":"Veli\u010dkovi\u0107 Petar","year":"2018","unstructured":"Petar Veli\u010dkovi\u0107 , Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Lio , and Yoshua Bengio . 2018 . Graph attention networks . In Proceedings of the 6th International Conference on Learning Representations (ICLR 2018). Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph attention networks. In Proceedings of the 6th International Conference on Learning Representations (ICLR 2018)."},{"key":"e_1_3_2_1_32_1","volume-title":"7th International Conference on Learning Representations, ICLR 2019","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu , Weihua Hu , Jure Leskovec , and Stefanie Jegelka . 2019 . How Powerful are Graph Neural Networks? . In 7th International Conference on Learning Representations, ICLR 2019 , New Orleans, LA, USA , May 6-9, 2019. OpenReview.net. https:\/\/openreview.net\/forum?id=ryGs6iA5Km Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How Powerful are Graph Neural Networks?. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net. https:\/\/openreview.net\/forum?id=ryGs6iA5Km"},{"key":"e_1_3_2_1_33_1","volume-title":"Proceedings of the 35th International Conference on Machine Learning, ICML 2018","author":"Xu Keyulu","year":"2018","unstructured":"Keyulu Xu , Chengtao Li , Yonglong Tian , Tomohiro Sonobe , Ken-ichi Kawarabayashi, and Stefanie Jegelka . 2018 . Representation Learning on Graphs with Jumping Knowledge Networks . In Proceedings of the 35th International Conference on Machine Learning, ICML 2018 , July 10-15, 2018(Proceedings of Machine Learning Research, Vol.\u00a080), Jennifer\u00a0G. Dy and Andreas Krause (Eds.). PMLR, 5449\u20135458. Keyulu Xu, Chengtao Li, Yonglong Tian, Tomohiro Sonobe, Ken-ichi Kawarabayashi, and Stefanie Jegelka. 2018. Representation Learning on Graphs with Jumping Knowledge Networks. In Proceedings of the 35th International Conference on Machine Learning, ICML 2018, July 10-15, 2018(Proceedings of Machine Learning Research, Vol.\u00a080), Jennifer\u00a0G. Dy and Andreas Krause (Eds.). PMLR, 5449\u20135458."},{"key":"e_1_3_2_1_34_1","unstructured":"Jiaxuan You Rex Ying and Jure Leskovec. 2019. Position-aware Graph Neural Networks. In ICML(Proceedings of Machine Learning Research Vol.\u00a097). PMLR 7134\u20137143.  Jiaxuan You Rex Ying and Jure Leskovec. 2019. Position-aware Graph Neural Networks. In ICML(Proceedings of Machine Learning Research Vol.\u00a097). PMLR 7134\u20137143."},{"key":"e_1_3_2_1_35_1","unstructured":"Kai Zhang Yaokang Zhu Jun Wang and Jie Zhang. 2020. Adaptive Structural Fingerprints for Graph Attention Networks. In ICLR. OpenReview.net.  Kai Zhang Yaokang Zhu Jun Wang and Jie Zhang. 2020. Adaptive Structural Fingerprints for Graph Attention Networks. In ICLR. OpenReview.net."}],"event":{"name":"WWW '21: The Web Conference 2021","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"],"location":"Ljubljana Slovenia","acronym":"WWW '21"},"container-title":["Proceedings of the Web Conference 2021"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3442381.3449896","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3442381.3449896","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T21:24:31Z","timestamp":1750195471000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3442381.3449896"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,4,19]]},"references-count":35,"alternative-id":["10.1145\/3442381.3449896","10.1145\/3442381"],"URL":"https:\/\/doi.org\/10.1145\/3442381.3449896","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"}}]}}