{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T18:38:55Z","timestamp":1771267135505,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":47,"publisher":"ACM","funder":[{"name":"the State Key Laboratory of General Artificial Intelligence","award":["N&#x5c;&#x2f;A"],"award-info":[{"award-number":["N&#x5c;&#x2f;A"]}]},{"name":"the National Natural Science Foundation of China","award":["62276006"],"award-info":[{"award-number":["62276006"]}]},{"name":"the Humanities and Social Sciences Research Planning Fund Project of The Ministry Of Education","award":["23YJA880091"],"award-info":[{"award-number":["23YJA880091"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2026,2,22]]},"DOI":"10.1145\/3773966.3778014","type":"proceedings-article","created":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T17:50:01Z","timestamp":1771264201000},"page":"1036-1046","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Addressing Aggregation-Induced Information Loss in Graph Neural Networks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0249-2817","authenticated-orcid":false,"given":"Wenhao","family":"Zhu","sequence":"first","affiliation":[{"name":"State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2441-9474","authenticated-orcid":false,"given":"Haonan","family":"Dong","sequence":"additional","affiliation":[{"name":"State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8295-2520","authenticated-orcid":false,"given":"Guojie","family":"Song","sequence":"additional","affiliation":[{"name":"State Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6283-062X","authenticated-orcid":false,"given":"Zhengzhou","family":"Zhu","sequence":"additional","affiliation":[{"name":"School of Software and Microelectronics, Peking University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2026,2,21]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=i80OPhOCVH2","author":"Alon Uri","year":"2021","unstructured":"Uri Alon and Eran Yahav. 2021. On the Bottleneck of Graph Neural Networks and its Practical Implications. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=i80OPhOCVH2"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1109\/Allerton49937.2022.9929363"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2022.3154319"},{"key":"e_1_3_2_1_4_1","unstructured":"Xavier Bresson and Thomas Laurent. 2018. Residual Gated Graph ConvNets. https:\/\/openreview.net\/forum?id=HyXBcYg0b"},{"key":"e_1_3_2_1_5_1","volume-title":"International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=F72ximsx7C1","author":"Brody Shaked","year":"2022","unstructured":"Shaked Brody, Uri Alon, and Eran Yahav. 2022. How Attentive are Graph Attention Networks?. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=F72ximsx7C1"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"e_1_3_2_1_7_1","volume-title":"International conference on machine learning. PMLR, 1725-1735","author":"Chen Ming","year":"2020","unstructured":"Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li. 2020b. Simple and deep graph convolutional networks. In International conference on machine learning. PMLR, 1725-1735."},{"key":"e_1_3_2_1_8_1","first-page":"13260","article-title":"Principal neighbourhood aggregation for graph nets","volume":"33","author":"Corso Gabriele","year":"2020","unstructured":"Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Li\u00f2, and Petar Veli\u010dkovi\u0107. 2020. Principal neighbourhood aggregation for graph nets. Advances in Neural Information Processing Systems, Vol. 33 (2020), 13260-13271.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_9_1","volume-title":"Proceedings of the Fifteenth National\/Tenth Conference on Artificial Intelligence\/Innovative Applications of Artificial Intelligence","author":"Craven Mark","year":"1998","unstructured":"Mark Craven, Dan DiPasquo, Dayne Freitag, Andrew McCallum, Tom Mitchell, Kamal Nigam, and Se\u00e1n Slattery. 1998. Learning to extract symbolic knowledge from the World Wide Web. In Proceedings of the Fifteenth National\/Tenth Conference on Artificial Intelligence\/Innovative Applications of Artificial Intelligence (Madison, Wisconsin, USA) (AAAI '98\/IAAI '98). American Association for Artificial Intelligence, USA, 509\u2013516."},{"key":"e_1_3_2_1_10_1","volume-title":"Yu Guang Wang, and Junyu Xuan","author":"Duan Wei","year":"2024","unstructured":"Wei Duan, Jie Lu, Yu Guang Wang, and Junyu Xuan. 2024. Layer-diverse Negative Sampling for Graph Neural Networks. Transactions on Machine Learning Research (2024)."},{"key":"e_1_3_2_1_11_1","volume-title":"A generalization of transformer networks to graphs. arXiv preprint arXiv:2012.09699","author":"Dwivedi Vijay Prakash","year":"2020","unstructured":"Vijay Prakash Dwivedi and Xavier Bresson. 2020. A generalization of transformer networks to graphs. arXiv preprint arXiv:2012.09699 (2020)."},{"key":"e_1_3_2_1_12_1","first-page":"1","article-title":"Benchmarking graph neural networks","volume":"24","author":"Dwivedi Vijay Prakash","year":"2023","unstructured":"Vijay Prakash Dwivedi, Chaitanya K Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson. 2023. Benchmarking graph neural networks. Journal of Machine Learning Research, Vol. 24, 43 (2023), 1-48.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_2_1_13_1","volume-title":"Graph Neural Networks with Learnable Structural and Positional Representations. In International Conference on Learning Representations.","author":"Dwivedi Vijay Prakash","year":"2022","unstructured":"Vijay Prakash Dwivedi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson. 2022a. Graph Neural Networks with Learnable Structural and Positional Representations. In International Conference on Learning Representations."},{"key":"e_1_3_2_1_14_1","first-page":"22326","article-title":"Long range graph benchmark","volume":"35","author":"Dwivedi Vijay Prakash","year":"2022","unstructured":"Vijay Prakash Dwivedi, Ladislav Ramp\u00e1\u0161ek, Michael Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, and Dominique Beaini. 2022b. Long range graph benchmark. Advances in Neural Information Processing Systems, Vol. 35 (2022), 22326-22340.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3614997"},{"key":"e_1_3_2_1_16_1","volume-title":"Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 249-256","author":"Glorot Xavier","year":"2010","unstructured":"Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. In Proceedings of the thirteenth international conference on artificial intelligence and statistics. JMLR Workshop and Conference Proceedings, 249-256."},{"key":"e_1_3_2_1_17_1","volume-title":"Benjam\u00edn S\u00e1nchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Al\u00e1n Aspuru-Guzik.","author":"G\u00f3mez-Bombarelli Rafael","year":"2018","unstructured":"Rafael G\u00f3mez-Bombarelli, Jennifer N Wei, David Duvenaud, Jos\u00e9 Miguel Hern\u00e1ndez-Lobato, Benjam\u00edn S\u00e1nchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Al\u00e1n Aspuru-Guzik. 2018. Automatic chemical design using a data-driven continuous representation of molecules. ACS central science, Vol. 4, 2 (2018), 268-276."},{"key":"e_1_3_2_1_18_1","volume-title":"Inductive representation learning on large graphs. Advances in neural information processing systems","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. Advances in neural information processing systems, Vol. 30 (2017)."},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2015.123"},{"key":"e_1_3_2_1_20_1","volume-title":"Strategies for Pre-training Graph Neural Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=HJlWWJSFDH","author":"Weihua","year":"2020","unstructured":"Weihua Hu*, Bowen Liu*, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec. 2020. Strategies for Pre-training Graph Neural Networks. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=HJlWWJSFDH"},{"key":"e_1_3_2_1_21_1","volume-title":"Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167","author":"Ioffe Sergey","year":"2015","unstructured":"Sergey Ioffe. 2015. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv preprint arXiv:1502.03167 (2015)."},{"key":"e_1_3_2_1_22_1","first-page":"2268","article-title":"Not too little, not too much: a theoretical analysis of graph (over) smoothing","volume":"35","author":"Keriven Nicolas","year":"2022","unstructured":"Nicolas Keriven. 2022. Not too little, not too much: a theoretical analysis of graph (over) smoothing. Advances in Neural Information Processing Systems, Vol. 35 (2022), 2268-2281.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_23_1","first-page":"14582","article-title":"Pure transformers are powerful graph learners","volume":"35","author":"Kim Jinwoo","year":"2022","unstructured":"Jinwoo Kim, Dat Nguyen, Seonwoo Min, Sungjun Cho, Moontae Lee, Honglak Lee, and Seunghoon Hong. 2022. Pure transformers are powerful graph learners. Advances in Neural Information Processing Systems, Vol. 35 (2022), 14582-14595.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_24_1","volume-title":"Proceedings of the 5th International Conference on Learning Representations (Palais des Congr\u00e8s Neptune","author":"Thomas","unstructured":"Thomas N. Kipf and Max Welling. 2017a. Semi-Supervised Classification with Graph Convolutional Networks. In Proceedings of the 5th International Conference on Learning Representations (Palais des Congr\u00e8s Neptune, Toulon, France) (ICLR '17). https:\/\/openreview.net\/forum?id=SJU4ayYgl"},{"key":"e_1_3_2_1_25_1","volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations (ICLR).","author":"Thomas","unstructured":"Thomas N. Kipf and Max Welling. 2017b. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations (ICLR)."},{"key":"e_1_3_2_1_26_1","volume-title":"Addressing Over-Squashing in GNNs with Graph Rewiring and Ordered Neurons. In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 1-8.","author":"Li Hao","year":"2024","unstructured":"Hao Li, Chen Li, Jianfei Zhang, Yuanxin Ouyang, and Wenge Rong. 2024. Addressing Over-Squashing in GNNs with Graph Rewiring and Ordered Neurons. In 2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 1-8."},{"key":"e_1_3_2_1_27_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"e_1_3_2_1_28_1","volume-title":"Proceedings of ICLR'16","author":"Li Yujia","year":"2016","unstructured":"Yujia Li, Richard Zemel, Marc Brockschmidt, and Daniel Tarlow. 2016. Gated Graph Sequence Neural Networks. In Proceedings of ICLR'16."},{"key":"e_1_3_2_1_29_1","volume-title":"Decoupled Weight Decay Regularization. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=Bkg6RiCqY7","author":"Loshchilov Ilya","year":"2019","unstructured":"Ilya Loshchilov and Frank Hutter. 2019. Decoupled Weight Decay Regularization. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=Bkg6RiCqY7"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009953814988"},{"key":"e_1_3_2_1_31_1","first-page":"21824","volume-title":"Lin (Eds.)","volume":"33","author":"Morris Christopher","year":"2020","unstructured":"Christopher Morris, Gaurav Rattan, and Petra Mutzel. 2020. Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings. In Advances in Neural Information Processing Systems, H. Larochelle, M. Ranzato, R. Hadsell, M.F. Balcan, and H. Lin (Eds.), Vol. 33. Curran Associates, Inc., 21824-21840. https:\/\/proceedings.neurips.cc\/paper_files\/paper\/2020\/file\/f81dee42585b3814de199b2e88757f5c-Paper.pdf"},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v33i01.33014602"},{"key":"e_1_3_2_1_33_1","volume-title":"International Conference on Machine Learning. PMLR, 25956-25979","author":"Nguyen Khang","year":"2023","unstructured":"Khang Nguyen, Nong Minh Hieu, Vinh Duc Nguyen, Nhat Ho, Stanley Osher, and Tan Minh Nguyen. 2023. Revisiting over-smoothing and over-squashing using ollivier-ricci curvature. In International Conference on Machine Learning. PMLR, 25956-25979."},{"key":"e_1_3_2_1_34_1","volume-title":"Revisiting Graph Neural Networks: All We Have is Low-Pass Filters. ArXiv","author":"Takanori Maehara Hoang","year":"2019","unstructured":"Hoang NT and Takanori Maehara. 2019. Revisiting Graph Neural Networks: All We Have is Low-Pass Filters. ArXiv, Vol. abs\/1905.09550 (2019). https:\/\/api.semanticscholar.org\/CorpusID:162183860"},{"key":"e_1_3_2_1_35_1","volume-title":"Graph Neural Networks Exponentially Lose Expressive Power for Node Classification. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=S1ldO2EFPr","author":"Oono Kenta","year":"2020","unstructured":"Kenta Oono and Taiji Suzuki. 2020. Graph Neural Networks Exponentially Lose Expressive Power for Node Classification. In International Conference on Learning Representations. https:\/\/openreview.net\/forum?id=S1ldO2EFPr"},{"key":"e_1_3_2_1_36_1","first-page":"205","article-title":"Limits of depth: Over-smoothing and over-squashing in gnns","volume":"7","author":"Shaima Qureshi","year":"2023","unstructured":"Shaima Qureshi et al., 2023. Limits of depth: Over-smoothing and over-squashing in gnns. Big Data Mining and Analytics, Vol. 7, 1 (2023), 205-216.","journal-title":"Big Data Mining and Analytics"},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1038\/sdata.2014.22"},{"key":"e_1_3_2_1_38_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_39_1","doi-asserted-by":"publisher","DOI":"10.1093\/comnet\/cnab014"},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1021\/ci300415d"},{"key":"e_1_3_2_1_41_1","volume-title":"A survey on oversmoothing in graph neural networks. arXiv preprint arXiv:2303.10993","author":"Rusch T Konstantin","year":"2023","unstructured":"T Konstantin Rusch, Michael M Bronstein, and Siddhartha Mishra. 2023. A survey on oversmoothing in graph neural networks. arXiv preprint arXiv:2303.10993 (2023)."},{"key":"e_1_3_2_1_42_1","volume-title":"Markus Hagenbuchner, and Gabriele Monfardini.","author":"Scarselli Franco","year":"2008","unstructured":"Franco Scarselli, Marco Gori, Ah Chung Tsoi, Markus Hagenbuchner, and Gabriele Monfardini. 2008. The graph neural network model. IEEE transactions on neural networks, Vol. 20, 1 (2008), 61-80."},{"key":"e_1_3_2_1_43_1","volume-title":"International Conference on Learning Representations.","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 International Conference on Learning Representations."},{"key":"e_1_3_2_1_44_1","volume-title":"The First Learning on Graphs Conference. https:\/\/openreview.net\/forum?id=vEbUaN9Z2V8","author":"Tortorella Domenico","year":"2022","unstructured":"Domenico Tortorella and Alessio Micheli. 2022. Leave Graphs Alone: Addressing Over-Squashing without Rewiring. In The First Learning on Graphs Conference. https:\/\/openreview.net\/forum?id=vEbUaN9Z2V8"},{"key":"e_1_3_2_1_45_1","volume-title":"Graph Attention Networks. International Conference on Learning Representations","author":"Veli\u010dkovi\u00e7 Petar","year":"2018","unstructured":"Petar Veli\u010dkovi\u00e7, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph Attention Networks. International Conference on Learning Representations (2018). https:\/\/openreview.net\/forum?id=rJXMpikCZ"},{"key":"e_1_3_2_1_46_1","volume-title":"MoleculeNet: a benchmark for molecular machine learning. Chemical science","author":"Wu Zhenqin","year":"2018","unstructured":"Zhenqin Wu, Bharath Ramsundar, Evan N Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S Pappu, Karl Leswing, and Vijay Pande. 2018. MoleculeNet: a benchmark for molecular machine learning. Chemical science, Vol. 9, 2 (2018), 513-530."},{"key":"e_1_3_2_1_47_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"}],"event":{"name":"WSDM '26:The Nineteenth ACM International Conference on Web Search and Data Mining","location":"Boise ID USA","sponsor":["SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval","SIGMOD ACM Special Interest Group on Management of Data"]},"container-title":["Proceedings of the Nineteenth ACM International Conference on Web Search and Data Mining"],"original-title":[],"deposited":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T17:52:19Z","timestamp":1771264339000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3773966.3778014"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,21]]},"references-count":47,"alternative-id":["10.1145\/3773966.3778014","10.1145\/3773966"],"URL":"https:\/\/doi.org\/10.1145\/3773966.3778014","relation":{},"subject":[],"published":{"date-parts":[[2026,2,21]]},"assertion":[{"value":"2026-02-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}