{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:27:06Z","timestamp":1760711226518,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":64,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T00:00:00Z","timestamp":1691107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Engineering Research Center of Next-Generation Intelligent Search and Recommendation, Ministry of Education"},{"name":"Beijing Outstanding Young Scientist Program","award":["No.BJJWZYJH012019100020098"],"award-info":[{"award-number":["No.BJJWZYJH012019100020098"]}]},{"name":"Intelligent Social Governance Interdisciplinary Platform, Major Innovation & Planning Interdisciplinary Platform for the ?Double-First Class? Initiative, Public Policy and Decision-making Research Lab, Public Computing Cloud, Renmin University of China"},{"name":"Beijing Natural Science Foundation","award":["No. 4222028"],"award-info":[{"award-number":["No. 4222028"]}]},{"name":"Huawei-Renmin University joint program on Information Retrieval"},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. U2241212, No. 61972401, No. 61932001, No. 61832017"],"award-info":[{"award-number":["No. U2241212, No. 61972401, No. 61932001, No. 61832017"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the major key project of PCL","award":["PCL2021A12"],"award-info":[{"award-number":["PCL2021A12"]}]},{"name":"Alibaba Group through Alibaba Innovative Research Program"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,8,6]]},"DOI":"10.1145\/3580305.3599431","type":"proceedings-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T18:13:58Z","timestamp":1691172838000},"page":"335-347","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":5,"title":["MGNN: Graph Neural Networks Inspired by Distance Geometry Problem"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-3074-1313","authenticated-orcid":false,"given":"Guanyu","family":"Cui","sequence":"first","affiliation":[{"name":"Renmin University of China, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3620-5086","authenticated-orcid":false,"given":"Zhewei","family":"Wei","sequence":"additional","affiliation":[{"name":"Renmin University of China, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2023,8,4]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"On dimensional rigidity of bar-and-joint frameworks. Discrete applied mathematics","author":"Alfakih Abdo Y","year":"2007","unstructured":"Abdo Y Alfakih . 2007. On dimensional rigidity of bar-and-joint frameworks. Discrete applied mathematics , Vol. 155 , 10 ( 2007 ), 1244--1253. Abdo Y Alfakih. 2007. On dimensional rigidity of bar-and-joint frameworks. Discrete applied mathematics, Vol. 155, 10 (2007), 1244--1253."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.3390\/biom9100549"},{"key":"e_1_3_2_2_3_1","volume-title":"9th International Conference on Learning Representations, ICLR 2021","author":"Balcilar Muhammet","year":"2021","unstructured":"Muhammet Balcilar , Guillaume Renton , Pierre H\u00e9roux , Benoit Ga\u00fcz\u00e8 re, S\u00e9bastien Adam , and Paul Honeine . 2021 . Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective . In 9th International Conference on Learning Representations, ICLR 2021 , Virtual Event, Austria , May 3-7, 2021. OpenReview.net. https:\/\/openreview.net\/forum?id=-qh0M9XWxnv Muhammet Balcilar, Guillaume Renton, Pierre H\u00e9roux, Benoit Ga\u00fcz\u00e8 re, S\u00e9bastien Adam, and Paul Honeine. 2021. Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net. https:\/\/openreview.net\/forum?id=-qh0M9XWxnv"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1149283.1149286"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16514"},{"key":"e_1_3_2_2_6_1","volume-title":"Benjamin Paul Chamberlain, Pietro Li\u00f2, and Michael M. Bronstein.","author":"Bodnar Cristian","year":"2022","unstructured":"Cristian Bodnar , Francesco Di Giovanni , Benjamin Paul Chamberlain, Pietro Li\u00f2, and Michael M. Bronstein. 2022 . Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs. In Advances in Neural Information Processing Systems, Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho (Eds .). Cristian Bodnar, Francesco Di Giovanni, Benjamin Paul Chamberlain, Pietro Li\u00f2, and Michael M. Bronstein. 2022. Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs. In Advances in Neural Information Processing Systems, Alice H. Oh, Alekh Agarwal, Danielle Belgrave, and Kyunghyun Cho (Eds.)."},{"key":"e_1_3_2_2_7_1","volume-title":"6th International Conference on Learning Representations, ICLR","author":"Bojchevski Aleksandar","year":"2018","unstructured":"Aleksandar Bojchevski and Stephan G\u00fcnnemann . 2018. Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking . In 6th International Conference on Learning Representations, ICLR 2018 , Vancouver, BC , Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview .net. https:\/\/openreview.net\/forum?id=r1ZdKJ-0W Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2018. Deep Gaussian Embedding of Graphs: Unsupervised Inductive Learning via Ranking. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net. https:\/\/openreview.net\/forum?id=r1ZdKJ-0W"},{"volume-title":"Modern multidimensional scaling: Theory and applications","author":"Borg Ingwer","key":"e_1_3_2_2_8_1","unstructured":"Ingwer Borg and Patrick JF Groenen . 2005. Modern multidimensional scaling: Theory and applications . Springer Science & Business Media . Ingwer Borg and Patrick JF Groenen. 2005. Modern multidimensional scaling: Theory and applications. Springer Science & Business Media."},{"key":"e_1_3_2_2_9_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning, ICML 2020","volume":"1735","author":"Chen Ming","year":"2020","unstructured":"Ming Chen , Zhewei Wei , Zengfeng Huang , Bolin Ding , and Yaliang Li . 2020 . Simple and Deep Graph Convolutional Networks . In Proceedings of the 37th International Conference on Machine Learning, ICML 2020 , 13-18 July 2020, Virtual Event (Proceedings of Machine Learning Research , Vol. 119). PMLR, 1725-- 1735 . http:\/\/proceedings.mlr.press\/v119\/chen20v.html Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li. 2020. Simple and Deep Graph Convolutional Networks. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event (Proceedings of Machine Learning Research, Vol. 119). PMLR, 1725--1735. http:\/\/proceedings.mlr.press\/v119\/chen20v.html"},{"key":"e_1_3_2_2_10_1","volume-title":"Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019","author":"Chen Zhengdao","year":"2019","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 32: Annual Conference on Neural Information Processing Systems 2019 , NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada. 15868--15876. 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 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8-14, 2019, Vancouver, BC, Canada. 15868--15876."},{"key":"e_1_3_2_2_11_1","volume-title":"Adaptive Universal Generalized PageRank Graph Neural Network. In 9th International Conference on Learning Representations, ICLR 2021","author":"Chien Eli","year":"2021","unstructured":"Eli Chien , Jianhao Peng , Pan Li , and Olgica Milenkovic . 2021 . Adaptive Universal Generalized PageRank Graph Neural Network. In 9th International Conference on Learning Representations, ICLR 2021 , Virtual Event, Austria , May 3-7, 2021. OpenReview.net. https:\/\/openreview.net\/forum?id=n6jl7fLxrP Eli Chien, Jianhao Peng, Pan Li, and Olgica Milenkovic. 2021. Adaptive Universal Generalized PageRank Graph Neural Network. In 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021. OpenReview.net. https:\/\/openreview.net\/forum?id=n6jl7fLxrP"},{"key":"e_1_3_2_2_12_1","unstructured":"Gordon M Crippen Timothy F Havel etal 1988. Distance geometry and molecular conformation. Vol. 74. Research Studies Press Taunton.  Gordon M Crippen Timothy F Havel et al. 1988. Distance geometry and molecular conformation. Vol. 74. Research Studies Press Taunton."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"crossref","unstructured":"Jan De Leeuw. 2005. Applications of convex analysis to multidimensional scaling. (2005).  Jan De Leeuw. 2005. Applications of convex analysis to multidimensional scaling. (2005).","DOI":"10.1002\/0470013192.bsa416"},{"key":"e_1_3_2_2_14_1","volume-title":"Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016","author":"Defferrard Micha\u00eb","year":"2016","unstructured":"Micha\u00eb l Defferrard , Xavier Bresson , and Pierre Vandergheynst . 2016 . Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016 , December 5-10, 2016, Barcelona, Spain, Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, and Roman Garnett (Eds.). 3837--3845. https:\/\/proceedings.neurips.cc\/paper\/ 2016\/hash\/04df4d434d481c5bb723be1b6df1ee65-Abstract.html Micha\u00eb l Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. In Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain, Daniel D. Lee, Masashi Sugiyama, Ulrike von Luxburg, Isabelle Guyon, and Roman Garnett (Eds.). 3837--3845. https:\/\/proceedings.neurips.cc\/paper\/2016\/hash\/04df4d434d481c5bb723be1b6df1ee65-Abstract.html"},{"key":"e_1_3_2_2_15_1","volume-title":"Signed Graph Convolutional Networks. In IEEE International Conference on Data Mining, ICDM 2018","author":"Derr Tyler","year":"2018","unstructured":"Tyler Derr , Yao Ma , and Jiliang Tang . 2018 . Signed Graph Convolutional Networks. In IEEE International Conference on Data Mining, ICDM 2018 , Singapore , November 17-20, 2018. IEEE Computer Society, 929--934. https:\/\/doi.org\/10.1109\/ICDM.2018.00113 10.1109\/ICDM.2018.00113 Tyler Derr, Yao Ma, and Jiliang Tang. 2018. Signed Graph Convolutional Networks. In IEEE International Conference on Data Mining, ICDM 2018, Singapore, November 17-20, 2018. IEEE Computer Society, 929--934. https:\/\/doi.org\/10.1109\/ICDM.2018.00113"},{"key":"e_1_3_2_2_16_1","volume-title":"Bronstein","author":"Giovanni Francesco Di","year":"2022","unstructured":"Francesco Di Giovanni , James Rowbottom , Benjamin P. Chamberlain , Thomas Markovich , and Michael M . Bronstein . 2022 . Graph Neural Networks as Gradient Flows . https:\/\/arxiv.org\/abs\/2206.10991 Francesco Di Giovanni, James Rowbottom, Benjamin P. Chamberlain, Thomas Markovich, and Michael M. Bronstein. 2022. Graph Neural Networks as Gradient Flows. https:\/\/arxiv.org\/abs\/2206.10991"},{"volume-title":"Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds.","author":"Fey Matthias","key":"e_1_3_2_2_17_1","unstructured":"Matthias Fey and Jan E. Lenssen . 2019 . Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds. Matthias Fey and Jan E. Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds."},{"key":"e_1_3_2_2_18_1","volume-title":"Graph drawing by force-directed placement. Software: Practice and experience","author":"Fruchterman Thomas MJ","year":"1991","unstructured":"Thomas MJ Fruchterman and Edward M Reingold . 1991. Graph drawing by force-directed placement. Software: Practice and experience , Vol. 21 , 11 ( 1991 ), 1129--1164. Thomas MJ Fruchterman and Edward M Reingold. 1991. Graph drawing by force-directed placement. Software: Practice and experience, Vol. 21, 11 (1991), 1129--1164."},{"key":"e_1_3_2_2_19_1","volume-title":"International Conference on Machine Learning, ICML 2022","volume":"6917","author":"Fu Guoji","year":"2022","unstructured":"Guoji Fu , Peilin Zhao , and Yatao Bian . 2022 . p-Laplacian Based Graph Neural Networks . In International Conference on Machine Learning, ICML 2022 , 17-23 July 2022, Baltimore, Maryland, USA (Proceedings of Machine Learning Research , Vol. 162), Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesv\u00e1ri, Gang Niu, and Sivan Sabato (Eds.). PMLR, 6878-- 6917 . https:\/\/proceedings.mlr.press\/v162\/fu22e.html Guoji Fu, Peilin Zhao, and Yatao Bian. 2022. p-Laplacian Based Graph Neural Networks. In International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA (Proceedings of Machine Learning Research, Vol. 162), Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesv\u00e1ri, Gang Niu, and Sivan Sabato (Eds.). PMLR, 6878--6917. https:\/\/proceedings.mlr.press\/v162\/fu22e.html"},{"key":"e_1_3_2_2_20_1","volume-title":"International Symposium on Graph Drawing. Springer, 239--250","author":"Gansner Emden R","year":"2004","unstructured":"Emden R Gansner , Yehuda Koren , and Stephen North . 2004 . Graph drawing by stress majorization . In International Symposium on Graph Drawing. Springer, 239--250 . Emden R Gansner, Yehuda Koren, and Stephen North. 2004. Graph drawing by stress majorization. In International Symposium on Graph Drawing. Springer, 239--250."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02290164"},{"key":"e_1_3_2_2_22_1","article-title":"Gaussian Dynamics of Folded","volume":"79","author":"Haliloglu Turkan","year":"1997","unstructured":"Turkan Haliloglu , Ivet Bahar , and Burak Erman . 1997 . Gaussian Dynamics of Folded Proteins. Phys. Rev. Lett. , Vol. 79 (Oct 1997), 3090--3093. Issue 16. Turkan Haliloglu, Ivet Bahar, and Burak Erman. 1997. Gaussian Dynamics of Folded Proteins. Phys. Rev. Lett., Vol. 79 (Oct 1997), 3090--3093. Issue 16.","journal-title":"Proteins. Phys. Rev. Lett."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1307\/mmj\/1028989917"},{"key":"e_1_3_2_2_24_1","unstructured":"Mingguo He Zhewei Wei Zengfeng Huang and Hongteng Xu. 2021. BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation. In Advances in Neural Information Processing Systems.  Mingguo He Zhewei Wei Zengfeng Huang and Hongteng Xu. 2021. BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_25_1","volume-title":"Open graph benchmark: Datasets for machine learning on graphs. Advances in neural information processing systems","author":"Hu Weihua","year":"2020","unstructured":"Weihua Hu , Matthias Fey , Marinka Zitnik , Yuxiao Dong , Hongyu Ren , Bowen Liu , Michele Catasta , and Jure Leskovec . 2020. Open graph benchmark: Datasets for machine learning on graphs. Advances in neural information processing systems , Vol. 33 ( 2020 ), 22118--22133. Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020. Open graph benchmark: Datasets for machine learning on graphs. Advances in neural information processing systems, Vol. 33 (2020), 22118--22133."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-30493-5_53"},{"key":"e_1_3_2_2_27_1","unstructured":"Bill Jackson. 2007. Notes on the Rigidity of Graphs.  Bill Jackson. 2007. Notes on the Rigidity of Graphs."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/0020-0190(89)90102-6"},{"key":"e_1_3_2_2_29_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2017","unstructured":"Thomas N. Kipf and Max Welling . 2017 . Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview .net. https:\/\/openreview.net\/forum?id=SJU4ayYgl Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings. OpenReview.net. https:\/\/openreview.net\/forum?id=SJU4ayYgl"},{"key":"e_1_3_2_2_30_1","volume-title":"7th International Conference on Learning Representations, ICLR 2019","author":"Klicpera Johannes","year":"2019","unstructured":"Johannes Klicpera , Aleksandar Bojchevski , and Stephan G\u00fcnnemann . 2019 . Predict then Propagate: Graph Neural Networks meet Personalized PageRank . In 7th International Conference on Learning Representations, ICLR 2019 , New Orleans, LA, USA , May 6-9, 2019. OpenReview.net. https:\/\/openreview.net\/forum?id=H1gL-2A9Ym Johannes Klicpera, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2019. Predict then Propagate: Graph Neural Networks meet Personalized PageRank. In 7th International Conference on Learning Representations, ICLR 2019, New Orleans, LA, USA, May 6-9, 2019. OpenReview.net. https:\/\/openreview.net\/forum?id=H1gL-2A9Ym"},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02289565"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1007\/BF02289694"},{"key":"e_1_3_2_2_33_1","volume-title":"6th International Conference on Learning Representations, ICLR","author":"Li Yaguang","year":"2018","unstructured":"Yaguang Li , Rose Yu , Cyrus Shahabi , and Yan Liu . 2018. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting . In 6th International Conference on Learning Representations, ICLR 2018 , Vancouver, BC , Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview .net. https:\/\/openreview.net\/forum?id=SJiHXGWAZ Yaguang Li, Rose Yu, Cyrus Shahabi, and Yan Liu. 2018. Diffusion Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net. https:\/\/openreview.net\/forum?id=SJiHXGWAZ"},{"volume-title":"Euclidean Distance Geometry: An Introduction","author":"Liberti Leo","key":"e_1_3_2_2_34_1","unstructured":"Leo Liberti and Carlile Lavor . 2017. Euclidean Distance Geometry: An Introduction . Springer International Publishing . Leo Liberti and Carlile Lavor. 2017. Euclidean Distance Geometry: An Introduction. Springer International Publishing."},{"key":"e_1_3_2_2_35_1","series-title":"SIAM review","volume-title":"Euclidean distance geometry and applications","author":"Liberti Leo","year":"2014","unstructured":"Leo Liberti , Carlile Lavor , Nelson Maculan , and Antonio Mucherino . 2014. Euclidean distance geometry and applications . SIAM review , Vol. 56 , 1 ( 2014 ), 3--69. Leo Liberti, Carlile Lavor, Nelson Maculan, and Antonio Mucherino. 2014. Euclidean distance geometry and applications. SIAM review, Vol. 56, 1 (2014), 3--69."},{"key":"e_1_3_2_2_36_1","first-page":"20887","article-title":"Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods","volume":"34","author":"Lim Derek","year":"2021","unstructured":"Derek Lim , Felix Hohne , Xiuyu Li , Sijia Linda Huang , Vaishnavi Gupta , Omkar Bhalerao , and Ser Nam Lim . 2021 . Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods . Advances in Neural Information Processing Systems , Vol. 34 (2021), 20887 -- 20902 . Derek Lim, Felix Hohne, Xiuyu Li, Sijia Linda Huang, Vaishnavi Gupta, Omkar Bhalerao, and Ser Nam Lim. 2021. Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods. Advances in Neural Information Processing Systems, Vol. 34 (2021), 20887--20902.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1109\/BIBMW.2009.5332117"},{"key":"e_1_3_2_2_38_1","volume-title":"Proceedings of the 38th International Conference on Machine Learning, ICML 2021","volume":"6849","author":"Liu Xiaorui","year":"2021","unstructured":"Xiaorui Liu , Wei Jin , Yao Ma , Yaxin Li , Hua Liu , Yiqi Wang , Ming Yan , and Jiliang Tang . 2021 . Elastic Graph Neural Networks . In Proceedings of the 38th International Conference on Machine Learning, ICML 2021 , 18-24 July 2021, Virtual Event (Proceedings of Machine Learning Research , Vol. 139), Marina Meila and Tong Zhang (Eds.). PMLR, 6837-- 6849 . http:\/\/proceedings.mlr.press\/v139\/liu21k.html Xiaorui Liu, Wei Jin, Yao Ma, Yaxin Li, Hua Liu, Yiqi Wang, Ming Yan, and Jiliang Tang. 2021. Elastic Graph Neural Networks. In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event (Proceedings of Machine Learning Research, Vol. 139), Marina Meila and Tong Zhang (Eds.). PMLR, 6837--6849. http:\/\/proceedings.mlr.press\/v139\/liu21k.html"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482225"},{"key":"e_1_3_2_2_40_1","volume-title":"Provably Powerful Graph Networks. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019","author":"Maron Haggai","year":"2019","unstructured":"Haggai Maron , Heli Ben-Hamu , Hadar Serviansky , and Yaron Lipman . 2019 . Provably Powerful Graph Networks. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019 , NeurIPS 2019, December 8--14, 2019, Vancouver, BC, Canada, Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alch\u00e9 -Buc, Emily B. Fox, and Roman Garnett (Eds.). 2153--2164. https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/bb04af0f7ecaee4aae62035497da1387-Abstract.html Haggai Maron, Heli Ben-Hamu, Hadar Serviansky, and Yaron Lipman. 2019. Provably Powerful Graph Networks. In Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, December 8--14, 2019, Vancouver, BC, Canada, Hanna M. Wallach, Hugo Larochelle, Alina Beygelzimer, Florence d'Alch\u00e9 -Buc, Emily B. Fox, and Roman Garnett (Eds.). 2153--2164. https:\/\/proceedings.neurips.cc\/paper\/2019\/hash\/bb04af0f7ecaee4aae62035497da1387-Abstract.html"},{"key":"e_1_3_2_2_41_1","volume-title":"Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks. CoRR","author":"Mernyei P\u00e9ter","year":"2020","unstructured":"P\u00e9ter Mernyei and Catalina Cangea . 2020. Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks. CoRR , Vol. abs\/ 2007 .02901 ( 2020 ). showeprint[arXiv]2007.02901 https:\/\/arxiv.org\/abs\/2007.02901 P\u00e9ter Mernyei and Catalina Cangea. 2020. Wiki-CS: A Wikipedia-Based Benchmark for Graph Neural Networks. CoRR, Vol. abs\/2007.02901 (2020). showeprint[arXiv]2007.02901 https:\/\/arxiv.org\/abs\/2007.02901"},{"key":"e_1_3_2_2_42_1","volume-title":"Gaurav Rattan, and Martin Grohe.","author":"Morris Christopher","year":"2019","unstructured":"Christopher Morris , Martin Ritzert , Matthias Fey , William L. Hamilton , Jan Eric Lenssen , Gaurav Rattan, and Martin Grohe. 2019 . Weisfeiler and Leman Go Neural: Higher- Order Graph Neural Networks. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019. AAAI Press , 4602--4609. https:\/\/doi.org\/10.1609\/aaai.v33i01.33014602 10.1609\/aaai.v33i01.33014602 Christopher Morris, Martin Ritzert, Matthias Fey, William L. Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe. 2019. Weisfeiler and Leman Go Neural: Higher-Order Graph Neural Networks. In The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019. AAAI Press, 4602--4609. https:\/\/doi.org\/10.1609\/aaai.v33i01.33014602"},{"key":"e_1_3_2_2_43_1","volume-title":"Geom-GCN: Geometric Graph Convolutional Networks. In 8th International Conference on Learning Representations, ICLR 2020","author":"Pei Hongbin","year":"2020","unstructured":"Hongbin Pei , Bingzhe Wei , Kevin Chen-Chuan Chang , Yu Lei , and Bo Yang . 2020 . Geom-GCN: Geometric Graph Convolutional Networks. In 8th International Conference on Learning Representations, ICLR 2020 , Addis Ababa, Ethiopia , April 26-30, 2020. OpenReview.net. https:\/\/openreview.net\/forum?id=S1e2agrFvS Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. 2020. Geom-GCN: Geometric Graph Convolutional Networks. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020. OpenReview.net. https:\/\/openreview.net\/forum?id=S1e2agrFvS"},{"key":"e_1_3_2_2_44_1","volume-title":"2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017","author":"Qi Charles Ruizhongtai","year":"2017","unstructured":"Charles Ruizhongtai Qi , Hao Su , Kaichun Mo , and Leonidas J. Guibas . 2017. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation . In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017 , Honolulu, HI, USA , July 21-26, 2017 . IEEE Computer Society, 77--85. https:\/\/doi.org\/10.1109\/CVPR.2017.16 10.1109\/CVPR.2017.16 Charles Ruizhongtai Qi, Hao Su, Kaichun Mo, and Leonidas J. Guibas. 2017. PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation. In 2017 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, Honolulu, HI, USA, July 21-26, 2017. IEEE Computer Society, 77--85. https:\/\/doi.org\/10.1109\/CVPR.2017.16"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1093\/comnet\/cnab014"},{"key":"e_1_3_2_2_46_1","volume-title":"Graph-Coupled Oscillator Networks. In International Conference on Machine Learning, ICML 2022","volume":"18909","author":"Rusch T. Konstantin","year":"2022","unstructured":"T. Konstantin Rusch , Ben Chamberlain , James Rowbottom , Siddhartha Mishra , and Michael M. Bronstein . 2022 . Graph-Coupled Oscillator Networks. In International Conference on Machine Learning, ICML 2022 , 17-23 July 2022 , Baltimore, Maryland, USA (Proceedings of Machine Learning Research , Vol. 162), Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesv\u00e1 ri, Gang Niu, and Sivan Sabato (Eds.). PMLR, 18888-- 18909 . https:\/\/proceedings.mlr.press\/v162\/rusch22a.html T. Konstantin Rusch, Ben Chamberlain, James Rowbottom, Siddhartha Mishra, and Michael M. Bronstein. 2022. Graph-Coupled Oscillator Networks. In International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA (Proceedings of Machine Learning Research, Vol. 162), Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesv\u00e1 ri, Gang Niu, and Sivan Sabato (Eds.). PMLR, 18888--18909. https:\/\/proceedings.mlr.press\/v162\/rusch22a.html"},{"key":"e_1_3_2_2_47_1","volume-title":"Proceedings of the 37th International Conference on Machine Learning, ICML 2020","volume":"8468","author":"Sanchez-Gonzalez Alvaro","year":"2020","unstructured":"Alvaro Sanchez-Gonzalez , Jonathan Godwin , Tobias Pfaff , Rex Ying , Jure Leskovec , and Peter W. Battaglia . 2020. Learning to Simulate Complex Physics with Graph Networks . In Proceedings of the 37th International Conference on Machine Learning, ICML 2020 , 13-18 July 2020 , Virtual Event (Proceedings of Machine Learning Research , Vol. 119). PMLR, 8459-- 8468 . http:\/\/proceedings.mlr.press\/v119\/sanchez-gonzalez20a.html Alvaro Sanchez-Gonzalez, Jonathan Godwin, Tobias Pfaff, Rex Ying, Jure Leskovec, and Peter W. Battaglia. 2020. Learning to Simulate Complex Physics with Graph Networks. In Proceedings of the 37th International Conference on Machine Learning, ICML 2020, 13-18 July 2020, Virtual Event (Proceedings of Machine Learning Research, Vol. 119). PMLR, 8459--8468. http:\/\/proceedings.mlr.press\/v119\/sanchez-gonzalez20a.html"},{"key":"e_1_3_2_2_49_1","volume-title":"Pitfalls of Graph Neural Network Evaluation. CoRR","author":"Shchur Oleksandr","year":"2018","unstructured":"Oleksandr Shchur , Maximilian Mumme , Aleksandar Bojchevski , and Stephan G\u00fc nnemann. 2018. Pitfalls of Graph Neural Network Evaluation. CoRR , Vol. abs\/ 1811 .05868 ( 2018 ). showeprint[arXiv]1811.05868 http:\/\/arxiv.org\/abs\/1811.05868 Oleksandr Shchur, Maximilian Mumme, Aleksandar Bojchevski, and Stephan G\u00fc nnemann. 2018. Pitfalls of Graph Neural Network Evaluation. CoRR, Vol. abs\/1811.05868 (2018). showeprint[arXiv]1811.05868 http:\/\/arxiv.org\/abs\/1811.05868"},{"key":"e_1_3_2_2_50_1","volume-title":"The Tenth International Conference on Learning Representations, ICLR 2022","author":"Topping Jake","year":"2022","unstructured":"Jake Topping , Francesco Di Giovanni , Benjamin Paul Chamberlain , Xiaowen Dong , and Michael M. Bronstein . 2022. Understanding over-squashing and bottlenecks on graphs via curvature . In The Tenth International Conference on Learning Representations, ICLR 2022 , Virtual Event , April 25-29, 2022 . OpenReview.net. https:\/\/openreview.net\/forum?id=7UmjRGzp-A Jake Topping, Francesco Di Giovanni, Benjamin Paul Chamberlain, Xiaowen Dong, and Michael M. Bronstein. 2022. Understanding over-squashing and bottlenecks on graphs via curvature. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview.net. https:\/\/openreview.net\/forum?id=7UmjRGzp-A"},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.2307\/2371045"},{"key":"e_1_3_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1112\/plms\/s3-13.1.743"},{"key":"e_1_3_2_2_54_1","volume-title":"6th International Conference on Learning Representations, ICLR","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic , Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Li\u00f2 , and Yoshua Bengio . 2018. Graph Attention Networks . In 6th International Conference on Learning Representations, ICLR 2018 , Vancouver, BC , Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview .net. https:\/\/openreview.net\/forum?id=rJXMpikCZ Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph Attention Networks. In 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings. OpenReview.net. https:\/\/openreview.net\/forum?id=rJXMpikCZ"},{"key":"e_1_3_2_2_55_1","volume-title":"International Conference on Machine Learning, ICML 2022","volume":"23362","author":"Wang Xiyuan","year":"2022","unstructured":"Xiyuan Wang and Muhan Zhang . 2022 . How Powerful are Spectral Graph Neural Networks . In International Conference on Machine Learning, ICML 2022 , 17--23 July 2022, Baltimore, Maryland, USA (Proceedings of Machine Learning Research , Vol. 162), Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesv\u00e1 ri, Gang Niu, and Sivan Sabato (Eds.). PMLR, 23341-- 23362 . https:\/\/proceedings.mlr.press\/v162\/wang22am.html Xiyuan Wang and Muhan Zhang. 2022. How Powerful are Spectral Graph Neural Networks. In International Conference on Machine Learning, ICML 2022, 17--23 July 2022, Baltimore, Maryland, USA (Proceedings of Machine Learning Research, Vol. 162), Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesv\u00e1 ri, Gang Niu, and Sivan Sabato (Eds.). PMLR, 23341--23362. https:\/\/proceedings.mlr.press\/v162\/wang22am.html"},{"key":"e_1_3_2_2_56_1","volume-title":"Tree Decomposed Graph Neural Network. In CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event","author":"Wang Yu","year":"2021","unstructured":"Yu Wang and Tyler Derr . 2021 . Tree Decomposed Graph Neural Network. In CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event , Queensland, Australia, November 1 - 5 , 2021. ACM, 2040--2049. Yu Wang and Tyler Derr. 2021. Tree Decomposed Graph Neural Network. In CIKM '21: The 30th ACM International Conference on Information and Knowledge Management, Virtual Event, Queensland, Australia, November 1 - 5, 2021. ACM, 2040--2049."},{"key":"e_1_3_2_2_57_1","volume-title":"Revisiting stress majorization as a unified framework for interactive constrained graph visualization","author":"Wang Yunhai","year":"2017","unstructured":"Yunhai Wang , Yanyan Wang , Yinqi Sun , Lifeng Zhu , Kecheng Lu , Chi-Wing Fu , Michael Sedlmair , Oliver Deussen , and Baoquan Chen . 2017. Revisiting stress majorization as a unified framework for interactive constrained graph visualization . IEEE transactions on visualization and computer graphics, Vol. 24 , 1 ( 2017 ), 489--499. Yunhai Wang, Yanyan Wang, Yinqi Sun, Lifeng Zhu, Kecheng Lu, Chi-Wing Fu, Michael Sedlmair, Oliver Deussen, and Baoquan Chen. 2017. Revisiting stress majorization as a unified framework for interactive constrained graph visualization. IEEE transactions on visualization and computer graphics, Vol. 24, 1 (2017), 489--499."},{"key":"e_1_3_2_2_58_1","volume-title":"ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networks. In The Eleventh International Conference on Learning Representations.","author":"Wang Yuelin","year":"2023","unstructured":"Yuelin Wang , Kai Yi , Xinliang Liu , Yu Guang Wang , and Shi Jin . 2023 . ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networks. In The Eleventh International Conference on Learning Representations. Yuelin Wang, Kai Yi, Xinliang Liu, Yu Guang Wang, and Shi Jin. 2023. ACMP: Allen-Cahn Message Passing with Attractive and Repulsive Forces for Graph Neural Networks. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_2_59_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning. PMLR, 6861--6871","author":"Wu Felix","year":"2019","unstructured":"Felix Wu , Amauri Souza , Tianyi Zhang , Christopher Fifty , Tao Yu , and Kilian Weinberger . 2019 a. Simplifying Graph Convolutional Networks . In Proceedings of the 36th International Conference on Machine Learning. PMLR, 6861--6871 . Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019a. Simplifying Graph Convolutional Networks. In Proceedings of the 36th International Conference on Machine Learning. PMLR, 6861--6871."},{"key":"e_1_3_2_2_60_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.3301346"},{"key":"e_1_3_2_2_61_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_2_62_1","volume-title":"Diverse Message Passing for Attribute with Heterophily. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021","author":"Yang Liang","year":"2021","unstructured":"Liang Yang , Mengzhe Li , Liyang Liu , Bingxin Niu , Chuan Wang , Xiaochun Cao , and Yuanfang Guo . 2021 a. Diverse Message Passing for Attribute with Heterophily. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021 , NeurIPS 2021, December 6-14, 2021, virtual, Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan (Eds.). 4751--4763. https:\/\/proceedings.neurips.cc\/paper\/2021\/hash\/253614bbac999b38b5b60cae531c4969-Abstract.html Liang Yang, Mengzhe Li, Liyang Liu, Bingxin Niu, Chuan Wang, Xiaochun Cao, and Yuanfang Guo. 2021a. Diverse Message Passing for Attribute with Heterophily. In Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual, Marc'Aurelio Ranzato, Alina Beygelzimer, Yann N. Dauphin, Percy Liang, and Jennifer Wortman Vaughan (Eds.). 4751--4763. https:\/\/proceedings.neurips.cc\/paper\/2021\/hash\/253614bbac999b38b5b60cae531c4969-Abstract.html"},{"key":"e_1_3_2_2_63_1","volume-title":"Proceedings of the 38th International Conference on Machine Learning, ICML 2021","volume":"11783","author":"Yang Yongyi","year":"2021","unstructured":"Yongyi Yang , Tang Liu , Yangkun Wang , Jinjing Zhou , Quan Gan , Zhewei Wei , Zheng Zhang , Zengfeng Huang , and David Wipf . 2021 b. Graph Neural Networks Inspired by Classical Iterative Algorithms . In Proceedings of the 38th International Conference on Machine Learning, ICML 2021 , 18-24 July 2021, Virtual Event (Proceedings of Machine Learning Research , Vol. 139), Marina Meila and Tong Zhang (Eds.). PMLR, 11773-- 11783 . http:\/\/proceedings.mlr.press\/v139\/yang21g.html Yongyi Yang, Tang Liu, Yangkun Wang, Jinjing Zhou, Quan Gan, Zhewei Wei, Zheng Zhang, Zengfeng Huang, and David Wipf. 2021b. Graph Neural Networks Inspired by Classical Iterative Algorithms. In Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event (Proceedings of Machine Learning Research, Vol. 139), Marina Meila and Tong Zhang (Eds.). PMLR, 11773--11783. http:\/\/proceedings.mlr.press\/v139\/yang21g.html"},{"key":"e_1_3_2_2_64_1","volume-title":"Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016 (JMLR Workshop and Conference Proceedings","volume":"48","author":"Yang Zhilin","year":"2016","unstructured":"Zhilin Yang , William W. Cohen , and Ruslan Salakhutdinov . 2016 . Revisiting Semi-Supervised Learning with Graph Embeddings . In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016 (JMLR Workshop and Conference Proceedings , Vol. 48), Maria-Florina Balcan and Kilian Q. Weinberger (Eds.). JMLR.org, 40-- 48 . http:\/\/proceedings.mlr.press\/v48\/yanga16.html Zhilin Yang, William W. Cohen, and Ruslan Salakhutdinov. 2016. Revisiting Semi-Supervised Learning with Graph Embeddings. In Proceedings of the 33nd International Conference on Machine Learning, ICML 2016, New York City, NY, USA, June 19-24, 2016 (JMLR Workshop and Conference Proceedings, Vol. 48), Maria-Florina Balcan and Kilian Q. Weinberger (Eds.). JMLR.org, 40--48. http:\/\/proceedings.mlr.press\/v48\/yanga16.html"},{"key":"e_1_3_2_2_65_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_2_66_1","volume-title":"Advances in Neural Information Processing Systems","volume":"33","author":"Zhu Jiong","year":"2020","unstructured":"Jiong Zhu , Yujun Yan , Lingxiao Zhao , Mark Heimann , Leman Akoglu , and Danai Koutra . 2020 . Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs . Advances in Neural Information Processing Systems , Vol. 33 (2020). Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. 2020. Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs. Advances in Neural Information Processing Systems, Vol. 33 (2020)."}],"event":{"name":"KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"location":"Long Beach CA USA","acronym":"KDD '23"},"container-title":["Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599431","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3580305.3599431","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:36Z","timestamp":1750178256000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599431"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,4]]},"references-count":64,"alternative-id":["10.1145\/3580305.3599431","10.1145\/3580305"],"URL":"https:\/\/doi.org\/10.1145\/3580305.3599431","relation":{},"subject":[],"published":{"date-parts":[[2023,8,4]]},"assertion":[{"value":"2023-08-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}