{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T22:31:14Z","timestamp":1780353074693,"version":"3.54.1"},"publisher-location":"New York, NY, USA","reference-count":41,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,4,30]],"date-time":"2023-04-30T00:00:00Z","timestamp":1682812800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Australian Research Council (ARC) Projects","award":["DE200100964, LP210301259, DP230100899"],"award-info":[{"award-number":["DE200100964, LP210301259, DP230100899"]}]},{"name":"Key Research Project of Zhejiang Lab","award":["2022PI0AC01"],"award-info":[{"award-number":["2022PI0AC01"]}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2021YFB3100600, 2022YFB4500300"],"award-info":[{"award-number":["2021YFB3100600, 2022YFB4500300"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Joint Research Fund of Guangzhou University","award":["202201020218, 202201020380"],"award-info":[{"award-number":["202201020218, 202201020380"]}]},{"name":"CAS Project for Young Scientists in Basic Research","award":["YSBR-008"],"award-info":[{"award-number":["YSBR-008"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,4,30]]},"DOI":"10.1145\/3543507.3583269","type":"proceedings-article","created":{"date-parts":[[2023,4,26]],"date-time":"2023-04-26T23:30:51Z","timestamp":1682551851000},"page":"221-230","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":23,"title":["CurvDrop: A Ricci Curvature Based Approach to Prevent Graph Neural Networks from Over-Smoothing and Over-Squashing"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3791-4343","authenticated-orcid":false,"given":"Yang","family":"Liu","sequence":"first","affiliation":[{"name":"Academy of Mathematics and Systems Science, Chinese Academy of Sciences, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9958-8673","authenticated-orcid":false,"given":"Chuan","family":"Zhou","sequence":"additional","affiliation":[{"name":"AMSS, Chinese Academy of Science, China and School of Cyber Security, University of Chinese Academy of Science, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0794-527X","authenticated-orcid":false,"given":"Shirui","family":"Pan","sequence":"additional","affiliation":[{"name":"School of Information and Communication Technology, Griffith University, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1371-5801","authenticated-orcid":false,"given":"Jia","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computing, Macquarie University, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5056-0351","authenticated-orcid":false,"given":"Zhao","family":"Li","sequence":"additional","affiliation":[{"name":"Hangzhou link2do Technology, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7626-0162","authenticated-orcid":false,"given":"Hongyang","family":"Chen","sequence":"additional","affiliation":[{"name":"Research Center for Graph Computing, Zhejiang Lab, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0994-2382","authenticated-orcid":false,"given":"Peng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Cyberspace Institute of Advanced Technology, Guangzhou University, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,4,30]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"International Conference on Learning Representations.","author":"Alon Uri","year":"2020","unstructured":"Uri Alon and Eran Yahav. 2020. On the Bottleneck of Graph Neural Networks and its Practical Implications. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_2_1","volume-title":"On the sum of Ricci-curvatures for weighted graphs. arXiv preprint arXiv:2001.01776","author":"Bai Shuliang","year":"2020","unstructured":"Shuliang Bai, An Huang, Linyuan Lu, and Shing-Tung Yau. 2020. On the sum of Ricci-curvatures for weighted graphs. arXiv preprint arXiv:2001.01776 (2020)."},{"key":"e_1_3_2_1_3_1","volume-title":"International conference on machine learning. PMLR, 610\u2013619","author":"Bojchevski Aleksandar","year":"2018","unstructured":"Aleksandar Bojchevski, Oleksandr Shchur, Daniel Z\u00fcgner, and Stephan G\u00fcnnemann. 2018. Netgan: Generating graphs via random walks. In International conference on machine learning. PMLR, 610\u2013619."},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"e_1_3_2_1_5_1","volume-title":"International Conference on Learning Representations. International Conference on Learning Representations, ICLR.","author":"Chen Jie","year":"2018","unstructured":"Jie Chen, Tengfei Ma, and Cao Xiao. 2018. FastGCN: Fast learning with graph convolutional networks via importance sampling. In International Conference on Learning Representations. International Conference on Learning Representations, ICLR."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403192"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295399"},{"key":"e_1_3_2_1_8_1","volume-title":"Heterogeneous Graph Neural Architecture Search. In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 1066\u20131071","author":"Gao Yang","year":"2021","unstructured":"Yang Gao, Peng Zhang, Zhao Li, Chuan Zhou, Yongchao Liu, and Yue Hu. 2021. Heterogeneous Graph Neural Architecture Search. In 2021 IEEE International Conference on Data Mining (ICDM). IEEE, 1066\u20131071."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2023.3239842"},{"key":"e_1_3_2_1_10_1","volume-title":"International Conference on Learning Representations (ICLR).","author":"Gasteiger Johannes","year":"2019","unstructured":"Johannes Gasteiger, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2019. Predict then Propagate: Graph Neural Networks meet Personalized PageRank. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_11_1","volume-title":"International conference on machine learning. PMLR, 1263\u20131272","author":"Gilmer Justin","year":"2017","unstructured":"Justin Gilmer, Samuel\u00a0S Schoenholz, Patrick\u00a0F Riley, Oriol Vinyals, and George\u00a0E Dahl. 2017. Neural message passing for quantum chemistry. In International conference on machine learning. PMLR, 1263\u20131272."},{"key":"e_1_3_2_1_12_1","unstructured":"William\u00a0L. Hamilton Rex Ying and Jure Leskovec. 2017. Inductive Representation Learning on Large Graphs. In NIPS."},{"key":"e_1_3_2_1_13_1","volume-title":"Adaptive sampling towards fast graph representation learning. Advances in neural information processing systems 31","author":"Huang Wenbing","year":"2018","unstructured":"Wenbing Huang, Tong Zhang, Yu Rong, and Junzhou Huang. 2018. Adaptive sampling towards fast graph representation learning. Advances in neural information processing systems 31 (2018)."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-20351-1_6"},{"key":"e_1_3_2_1_15_1","volume-title":"Large scale Ricci curvature on graphs. Calculus of variations and partial differential equations 59, 5","author":"Kempton Mark","year":"2020","unstructured":"Mark Kempton, Gabor Lippner, and Florentin M\u00fcnch. 2020. Large scale Ricci curvature on graphs. Calculus of variations and partial differential equations 59, 5 (2020), 1\u201317."},{"key":"e_1_3_2_1_16_1","volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations.","author":"Kipf N","year":"2016","unstructured":"Thomas\u00a0N Kipf and Max Welling. 2016. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P19-1247"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2021.12.077"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00371"},{"key":"e_1_3_2_1_20_1","volume-title":"Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning","author":"Li Qimai","unstructured":"Qimai Li, Zhichao Han, and Xiao-Ming Wu. 2018. Deeper Insights Into Graph Convolutional Networks for Semi-Supervised Learning. In AAAI. AAAI Press, 3538\u20133545."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.2748\/tmj\/1325886283"},{"key":"e_1_3_2_1_22_1","volume-title":"Ricci curvature and eigenvalue estimate on locally finite graphs. Mathematical research letters 17, 2","author":"Lin Yong","year":"2010","unstructured":"Yong Lin and Shing-Tung Yau. 2010. Ricci curvature and eigenvalue estimate on locally finite graphs. Mathematical research letters 17, 2 (2010), 343\u2013356."},{"key":"e_1_3_2_1_23_1","volume-title":"Paul erdos is eighty 2, 1-46","author":"Lov\u00e1sz L\u00e1szl\u00f3","year":"1993","unstructured":"L\u00e1szl\u00f3 Lov\u00e1sz. 1993. Random walks on graphs. Combinatorics, Paul erdos is eighty 2, 1-46 (1993), 4."},{"key":"e_1_3_2_1_24_1","volume-title":"Community detection on networks with Ricci flow. Scientific reports 9, 1","author":"Ni Chien-Chun","year":"2019","unstructured":"Chien-Chun Ni, Yu-Yao Lin, Feng Luo, and Jie Gao. 2019. Community detection on networks with Ricci flow. Scientific reports 9, 1 (2019), 1\u201312."},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfa.2008.11.001"},{"key":"e_1_3_2_1_26_1","unstructured":"Kenta Oono and Taiji Suzuki. 2019. On asymptotic behaviors of graph cnns from dynamical systems perspective. (2019)."},{"key":"e_1_3_2_1_27_1","volume-title":"DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=Hkx1qkrKPr","author":"Rong Yu","year":"2020","unstructured":"Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. 2020. DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=Hkx1qkrKPr"},{"key":"e_1_3_2_1_28_1","volume-title":"Collective classification in network data. AI magazine 29, 3","author":"Sen Prithviraj","year":"2008","unstructured":"Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. 2008. Collective classification in network data. AI magazine 29, 3 (2008), 93\u201393."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.1145\/3469379.3469387"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR42600.2020.00178"},{"key":"e_1_3_2_1_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441835"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449912"},{"key":"e_1_3_2_1_33_1","volume-title":"International Conference on Learning Representations.","author":"Topping Jake","year":"2022","unstructured":"Jake Topping, Francesco\u00a0Di Giovanni, Benjamin\u00a0Paul Chamberlain, Xiaowen Dong, and Michael\u00a0M. Bronstein. 2022. Understanding over-squashing and bottlenecks on graphs via curvature. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Guangtao Wang Rex Ying Jing Huang and Jure Leskovec. 2021. Multi-hop Attention Graph Neural Networks. In IJCAI.","DOI":"10.24963\/ijcai.2021\/425"},{"key":"e_1_3_2_1_35_1","volume-title":"Graph Stochastic Neural Networks for Semi-supervised Learning. In The Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS-20)","author":"Wang Haibo","year":"2020","unstructured":"Haibo Wang, Chuan Zhou, Xin Chen, Jia Wu, Shirui Pan, and Jilong Wang. 2020. Graph Stochastic Neural Networks for Semi-supervised Learning. In The Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS-20)."},{"key":"e_1_3_2_1_36_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. In ICML."},{"key":"e_1_3_2_1_37_1","volume-title":"Curvature Graph Network. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=BylEqnVFDB","author":"Ye Ze","year":"2020","unstructured":"Ze Ye, Kin\u00a0Sum Liu, Tengfei Ma, Jie Gao, and Chao Chen. 2020. Curvature Graph Network. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=BylEqnVFDB"},{"key":"e_1_3_2_1_38_1","volume-title":"Deep graph neural networks with shallow subgraph samplers. arXiv preprint arXiv:2012.01380","author":"Zeng Hanqing","year":"2020","unstructured":"Hanqing Zeng, Muhan Zhang, Yinglong Xia, Ajitesh Srivastava, Andrey Malevich, Rajgopal Kannan, Viktor Prasanna, Long Jin, and Ren Chen. 2020. Deep graph neural networks with shallow subgraph samplers. arXiv preprint arXiv:2012.01380 (2020)."},{"key":"e_1_3_2_1_39_1","volume-title":"Adaptive Structural Fingerprints for Graph Attention Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=BJxWx0NYPr","author":"Zhang Kai","year":"2020","unstructured":"Kai Zhang, Yaokang Zhu, Jun Wang, and Jie Zhang. 2020. Adaptive Structural Fingerprints for Graph Attention Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=BJxWx0NYPr"},{"key":"e_1_3_2_1_40_1","volume-title":"Graph Geometry Interaction Learning. In The Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS-20)","author":"Zhu Shichao","year":"2020","unstructured":"Shichao Zhu, Shirui Pan, Chuan Zhou, Jia Wu, Yanan Cao, and Bin Wang. 2020. Graph Geometry Interaction Learning. In The Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS-20)."},{"key":"e_1_3_2_1_41_1","doi-asserted-by":"crossref","unstructured":"Fariba Zohrizadeh Mohsen Kheirandishfard Farhad Kamangar and Ramtin Madani. 2019. Non-smooth Optimization over Stiefel Manifolds with Applications to Dimensionality Reduction and Graph Clustering.. In IJCAI. 1319\u20131326.","DOI":"10.24963\/ijcai.2019\/183"}],"event":{"name":"WWW '23: The ACM Web Conference 2023","location":"Austin TX USA","acronym":"WWW '23","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2023"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3543507.3583269","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3543507.3583269","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:37:22Z","timestamp":1750178242000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3543507.3583269"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,4,30]]},"references-count":41,"alternative-id":["10.1145\/3543507.3583269","10.1145\/3543507"],"URL":"https:\/\/doi.org\/10.1145\/3543507.3583269","relation":{},"subject":[],"published":{"date-parts":[[2023,4,30]]},"assertion":[{"value":"2023-04-30","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}