{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T06:02:45Z","timestamp":1781762565162,"version":"3.54.5"},"publisher-location":"New York, NY, USA","reference-count":59,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T00:00:00Z","timestamp":1697846400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"DATAIA","award":["ANR-17-CONV-0003"],"award-info":[{"award-number":["ANR-17-CONV-0003"]}]},{"DOI":"10.13039\/501100001665","name":"Agence Nationale de la Recherche","doi-asserted-by":"publisher","award":["ANR-20-CE23-0009-01"],"award-info":[{"award-number":["ANR-20-CE23-0009-01"]}],"id":[{"id":"10.13039\/501100001665","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,10,21]]},"DOI":"10.1145\/3583780.3614997","type":"proceedings-article","created":{"date-parts":[[2023,10,21]],"date-time":"2023-10-21T07:45:42Z","timestamp":1697874342000},"page":"566-576","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":55,"title":["On the Trade-off between Over-smoothing and Over-squashing in Deep Graph Neural Networks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0039-1270","authenticated-orcid":false,"given":"Jhony H.","family":"Giraldo","sequence":"first","affiliation":[{"name":"LTCI, T\u00e9l\u00e9com Paris - Institut Polytechnique de Paris, Palaiseau, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8804-6320","authenticated-orcid":false,"given":"Konstantinos","family":"Skianis","sequence":"additional","affiliation":[{"name":"BLUAI, Athens, Greece"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4018-8856","authenticated-orcid":false,"given":"Thierry","family":"Bouwmans","sequence":"additional","affiliation":[{"name":"Laboratoire MIA, La Rochelle Universit\u00e9, La Rochelle, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8770-3969","authenticated-orcid":false,"given":"Fragkiskos D.","family":"Malliaros","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris-Saclay, CentraleSup\u00e9lec, Inria, Gif-sur-Yvette, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2023,10,21]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"International Conference on Learning Representations.","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. Uri Alon and Eran Yahav. 2021. On the bottleneck of graph neural networks and its practical implications. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_2_1","volume-title":"Semi-Supervised Learning and Graph Neural Networks for Fake News Detection. In International Conference on Advances in Social Networks Analysis and Mining.","author":"Benamira Adrien","unstructured":"Adrien Benamira , Benjamin Devillers , Etienne Lesot , Ayush K. Ray , Manal Saadi , and Fragkiskos D. Malliaros . 2019 . Semi-Supervised Learning and Graph Neural Networks for Fake News Detection. In International Conference on Advances in Social Networks Analysis and Mining. Adrien Benamira, Benjamin Devillers, Etienne Lesot, Ayush K. Ray, Manal Saadi, and Fragkiskos D. Malliaros. 2019. Semi-Supervised Learning and Graph Neural Networks for Fake News Detection. In International Conference on Advances in Social Networks Analysis and Mining."},{"key":"e_1_3_2_2_3_1","volume-title":"International Conference on Learning Representations.","author":"Bruna Joan","year":"2014","unstructured":"Joan Bruna , Wojciech Zaremba , Arthur Szlam , and Yann LeCun . 2014 . Spectral networks and locally connected networks on graphs . In International Conference on Learning Representations. Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2014. Spectral networks and locally connected networks on graphs. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i05.6243"},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR46437.2021.00659"},{"key":"e_1_3_2_2_6_1","first-page":"195","article-title":"A lower bound for the smallest eigenvalue of the Laplacian","volume":"625","author":"Cheeger Jeff","year":"1970","unstructured":"Jeff Cheeger . 1970 . A lower bound for the smallest eigenvalue of the Laplacian . Problems in Analysis , Vol. 625 , 195 -- 199 (1970), 110. Jeff Cheeger. 1970. A lower bound for the smallest eigenvalue of the Laplacian. Problems in Analysis, Vol. 625, 195--199 (1970), 110.","journal-title":"Problems in Analysis"},{"key":"e_1_3_2_2_7_1","volume-title":"A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective. arXiv preprint arXiv:2209.13232","author":"Chen Chaoqi","year":"2022","unstructured":"Chaoqi Chen , Yushuang Wu , Qiyuan Dai , Hong-Yu Zhou , Mutian Xu , Sibei Yang , Xiaoguang Han , and Yizhou Yu. 2022. A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective. arXiv preprint arXiv:2209.13232 ( 2022 ). Chaoqi Chen, Yushuang Wu, Qiyuan Dai, Hong-Yu Zhou, Mutian Xu, Sibei Yang, Xiaoguang Han, and Yizhou Yu. 2022. A Survey on Graph Neural Networks and Graph Transformers in Computer Vision: A Task-Oriented Perspective. arXiv preprint arXiv:2209.13232 (2022)."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i04.5747"},{"key":"e_1_3_2_2_9_1","unstructured":"Yu Chen Lingfei Wu and Mohammed Zaki. 2020b. Iterative deep graph learning for graph neural networks: Better and robust node embeddings. In Advances in Neural Information Processing Systems.  Yu Chen Lingfei Wu and Mohammed Zaki. 2020b. Iterative deep graph learning for graph neural networks: Better and robust node embeddings. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_10_1","volume-title":"International Conference on Learning Representations.","author":"Chien Eli","year":"2021","unstructured":"Eli Chien , Jianhao Peng , Pan Li , and Olgica Milenkovic . 2021 . Adaptive universal generalized PageRank graph neural network . In International Conference on Learning Representations. Eli Chien, Jianhao Peng, Pan Li, and Olgica Milenkovic. 2021. Adaptive universal generalized PageRank graph neural network. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_11_1","unstructured":"Fan R. K. Chung. 1997. Spectral graph theory. Number 92. American Mathematical Soc.  Fan R. K. Chung. 1997. Spectral graph theory. Number 92. American Mathematical Soc."},{"key":"e_1_3_2_2_12_1","volume-title":"On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology. arXiv preprint arXiv:2302.02941","author":"Giovanni Francesco Di","year":"2023","unstructured":"Francesco Di Giovanni , Lorenzo Giusti , Federico Barbero , Giulia Luise , Pietro Lio , and Michael Bronstein . 2023. On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology. arXiv preprint arXiv:2302.02941 ( 2023 ). Francesco Di Giovanni, Lorenzo Giusti, Federico Barbero, Giulia Luise, Pietro Lio, and Michael Bronstein. 2023. On Over-Squashing in Message Passing Neural Networks: The Impact of Width, Depth, and Topology. arXiv preprint arXiv:2302.02941 (2023)."},{"key":"e_1_3_2_2_13_1","volume-title":"FAENet: Frame Averaging Equivariant GNN for Materials Modeling. In International Conference on Machine Learning.","author":"Duval Alexandre","year":"2023","unstructured":"Alexandre Duval , Victor Schmidt , Alex Hern\u00e1ndez-Garc'ia , Santiago Miret , Fragkiskos D. Malliaros , Yoshua Bengio , and David Rolnick . 2023 . FAENet: Frame Averaging Equivariant GNN for Materials Modeling. In International Conference on Machine Learning. Alexandre Duval, Victor Schmidt, Alex Hern\u00e1ndez-Garc'ia, Santiago Miret, Fragkiskos D. Malliaros, Yoshua Bengio, and David Rolnick. 2023. FAENet: Frame Averaging Equivariant GNN for Materials Modeling. In International Conference on Machine Learning."},{"key":"e_1_3_2_2_14_1","volume-title":"Mikhail Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, and Dominique Beaini.","author":"Dwivedi Vijay Prakash","year":"2022","unstructured":"Vijay Prakash Dwivedi , Ladislav Ramp\u00e1vs ek , Mikhail Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, and Dominique Beaini. 2022 . Long range graph benchmark. In Advances in Neural Information Processing Systems . Vijay Prakash Dwivedi, Ladislav Ramp\u00e1vs ek, Mikhail Galkin, Ali Parviz, Guy Wolf, Anh Tuan Luu, and Dominique Beaini. 2022. Long range graph benchmark. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_15_1","volume-title":"Fast Graph Representation Learning with PyTorch Geometric. In International Conference on Learning Representations - Workshops.","author":"Fey Matthias","year":"2019","unstructured":"Matthias Fey and Jan Eric Lenssen . 2019 . Fast Graph Representation Learning with PyTorch Geometric. In International Conference on Learning Representations - Workshops. Matthias Fey and Jan Eric Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. In International Conference on Learning Representations - Workshops."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-019-0666-6"},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1145\/800119.803884"},{"key":"e_1_3_2_2_18_1","volume-title":"International Conference on Learning Representations.","author":"Gasteiger Johannes","year":"2019","unstructured":"Johannes Gasteiger , Aleksandar Bojchevski , and Stephan G\u00fcnnemann . 2019 a. Predict then propagate: Graph neural networks meet personalized PageRank . In International Conference on Learning Representations. Johannes Gasteiger, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2019a. Predict then propagate: Graph neural networks meet personalized PageRank. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_19_1","unstructured":"Johannes Gasteiger Stefan Wei\u00dfenberger and Stephan G\u00fcnnemann. 2019b. Diffusion improves graph learning. In Advances in Neural Information Processing Systems.  Johannes Gasteiger Stefan Wei\u00dfenberger and Stephan G\u00fcnnemann. 2019b. Diffusion improves graph learning. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_20_1","volume-title":"International Conference on Machine Learning.","author":"Gilmer Justin","year":"2017","unstructured":"Justin Gilmer , Samuel S Schoenholz , Patrick F Riley , Oriol Vinyals , and George E Dahl . 2017 . Neural message passing for quantum chemistry . In International Conference on Machine Learning. Justin Gilmer, Samuel S Schoenholz, Patrick F Riley, Oriol Vinyals, and George E Dahl. 2017. Neural message passing for quantum chemistry. In International Conference on Machine Learning."},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCVW54120.2021.00030"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"publisher","DOI":"10.1090\/conm\/071\/954419"},{"key":"e_1_3_2_2_23_1","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems.  Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_24_1","unstructured":"Kai Han Yunhe Wang Jianyuan Guo Yehui Tang and Enhua Wu. 2022. Vision GNN: An image is worth graph of nodes. In Advances in Neural Information Processing Systems.  Kai Han Yunhe Wang Jianyuan Guo Yehui Tang and Enhua Wu. 2022. Vision GNN: An image is worth graph of nodes. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_25_1","volume-title":"Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective. In International Conference on Learning Representations.","author":"Huang Wei","year":"2022","unstructured":"Wei Huang , Yayong Li , Weitao Du , Jie Yin , Richard Yi Da Xu , Ling Chen , and Miao Zhang . 2022 . Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective. In International Conference on Learning Representations. Wei Huang, Yayong Li, Weitao Du, Jie Yin, Richard Yi Da Xu, Ling Chen, and Miao Zhang. 2022. Towards Deepening Graph Neural Networks: A GNTK-based Optimization Perspective. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00454-013-9558-1"},{"key":"e_1_3_2_2_27_1","volume-title":"International Conference on Learning Representations.","author":"Karhadkar Kedar","year":"2023","unstructured":"Kedar Karhadkar , Pradeep Kr Banerjee , and Guido Mont\u00fafar . 2023 . FoSR: First-order spectral rewiring for addressing oversquashing in GNNs . In International Conference on Learning Representations. Kedar Karhadkar, Pradeep Kr Banerjee, and Guido Mont\u00fafar. 2023. FoSR: First-order spectral rewiring for addressing oversquashing in GNNs. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_28_1","volume-title":"International Conference on Learning Representations.","author":"Kingma Diederik P","year":"2015","unstructured":"Diederik P Kingma and Jimmy Ba . 2015 . Adam: A method for stochastic optimization . In International Conference on Learning Representations. Diederik P Kingma and Jimmy Ba. 2015. Adam: A method for stochastic optimization. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_29_1","volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations.","author":"Kipf Thomas N","year":"2017","unstructured":"Thomas N Kipf and Max Welling . 2017 . Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations. Thomas N Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_30_1","unstructured":"Devin Kreuzer Dominique Beaini Will Hamilton Vincent L\u00e9tourneau and Prudencio Tossou. 2021. Rethinking graph transformers with spectral attention. In Advances in Neural Information Processing Systems.  Devin Kreuzer Dominique Beaini Will Hamilton Vincent L\u00e9tourneau and Prudencio Tossou. 2021. Rethinking graph transformers with spectral attention. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_31_1","volume-title":"Nature","volume":"521","author":"LeCun Yann","year":"2015","unstructured":"Yann LeCun , Yoshua Bengio , and Geoffrey Hinton . 2015 . Deep learning . Nature , Vol. 521 , 7553 (2015), 436--444. Yann LeCun, Yoshua Bengio, and Geoffrey Hinton. 2015. Deep learning. Nature, Vol. 521, 7553 (2015), 436--444."},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00936"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"e_1_3_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.2748\/tmj\/1325886283"},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583269"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1023\/A:1009953814988"},{"key":"e_1_3_2_2_37_1","volume-title":"International Workshop on Mining and Learning with Graphs.","author":"Namata Galileo","year":"2012","unstructured":"Galileo Namata , Ben London , Lise Getoor , Bert Huang , and U Edu . 2012 . Query-driven active surveying for collective classification . In International Workshop on Mining and Learning with Graphs. Galileo Namata, Ben London, Lise Getoor, Bert Huang, and U Edu. 2012. Query-driven active surveying for collective classification. In International Workshop on Mining and Learning with Graphs."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jfa.2008.11.001"},{"key":"e_1_3_2_2_39_1","volume-title":"International Conference on Learning Representations.","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. Kenta Oono and Taiji Suzuki. 2020. Graph neural networks exponentially lose expressive power for node classification. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_40_1","volume-title":"International Conference on Learning Representations.","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 International Conference on Learning Representations. Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. 2020. Geom-GCN: Geometric graph convolutional networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_41_1","volume-title":"Inductive Graph Neural Networks for Moving Object Segmentation. In IEEE International Conference on Image Processing.","author":"Prummel Wieke","year":"2023","unstructured":"Wieke Prummel , Jhony H Giraldo , Anastasia Zakharova , and Thierry Bouwmans . 2023 . Inductive Graph Neural Networks for Moving Object Segmentation. In IEEE International Conference on Image Processing. Wieke Prummel, Jhony H Giraldo, Anastasia Zakharova, and Thierry Bouwmans. 2023. Inductive Graph Neural Networks for Moving Object Segmentation. In IEEE International Conference on Image Processing."},{"key":"e_1_3_2_2_42_1","volume-title":"International Conference on Learning Representations.","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. 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."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1093\/comnet\/cnab014"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/TSP.2014.2321121"},{"key":"e_1_3_2_2_45_1","volume-title":"Collective classification in network data. AI magazine","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 , Vol. 29 , 3 ( 2008 ), 93--93. Prithviraj Sen, Galileo Namata, Mustafa Bilgic, Lise Getoor, Brian Galligher, and Tina Eliassi-Rad. 2008. Collective classification in network data. AI magazine, Vol. 29, 3 (2008), 93--93."},{"key":"e_1_3_2_2_46_1","volume-title":"Algorithms for random generation and counting: a Markov chain approach","author":"Sinclair Alistair","unstructured":"Alistair Sinclair . 2012. Algorithms for random generation and counting: a Markov chain approach . Springer Science & Business Media . Alistair Sinclair. 2012. Algorithms for random generation and counting: a Markov chain approach. Springer Science & Business Media."},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557108"},{"key":"e_1_3_2_2_48_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. 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_2_49_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-010-5196-5"},{"key":"e_1_3_2_2_50_1","volume-title":"Graph Attention Networks. In International Conference on Learning Representations.","author":"Petar Velivc","year":"2018","unstructured":"Petar Velivc kovi\u0107, Guillem Cucurull , Arantxa Casanova , Adriana Romero , Pietro Lio , and Yoshua Bengio . 2018 . Graph Attention Networks. In International Conference on Learning Representations. Petar Velivc kovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph Attention Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_51_1","volume-title":"International Conference on Machine Learning.","author":"Wu Felix","year":"2019","unstructured":"Felix Wu , Amauri Souza , Tianyi Zhang , Christopher Fifty , Tao Yu , and Kilian Weinberger . 2019 . Simplifying graph convolutional networks . In International Conference on Machine Learning. Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying graph convolutional networks. In International Conference on Machine Learning."},{"key":"e_1_3_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_2_53_1","unstructured":"Chengxuan Ying Tianle Cai Shengjie Luo Shuxin Zheng Guolin Ke Di He Yanming Shen and Tie-Yan Liu. 2021. Do transformers really perform badly for graph representation?. In Advances in Neural Information Processing Systems.  Chengxuan Ying Tianle Cai Shengjie Luo Shuxin Zheng Guolin Ke Di He Yanming Shen and Tie-Yan Liu. 2021. Do transformers really perform badly for graph representation?. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_54_1","unstructured":"Seongjun Yun Minbyul Jeong Raehyun Kim Jaewoo Kang and Hyunwoo J Kim. 2019. Graph transformer networks. In Advances in neural information processing systems.  Seongjun Yun Minbyul Jeong Raehyun Kim Jaewoo Kang and Hyunwoo J Kim. 2019. Graph transformer networks. In Advances in neural information processing systems."},{"key":"e_1_3_2_2_55_1","unstructured":"Hanqing Zeng Muhan Zhang Yinglong Xia Ajitesh Srivastava Andrey Malevich Rajgopal Kannan Viktor Prasanna Long Jin and Ren Chen. 2021. Decoupling the Depth and Scope of Graph Neural Networks. In Advances in Neural Information Processing Systems.  Hanqing Zeng Muhan Zhang Yinglong Xia Ajitesh Srivastava Andrey Malevich Rajgopal Kannan Viktor Prasanna Long Jin and Ren Chen. 2021. Decoupling the Depth and Scope of Graph Neural Networks. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_56_1","volume-title":"International Conference on Learning Representations.","author":"Zhao Lingxiao","year":"2020","unstructured":"Lingxiao Zhao and Leman Akoglu . 2020 . PairNorm: Tackling oversmoothing in GNNs . In International Conference on Learning Representations. Lingxiao Zhao and Leman Akoglu. 2020. PairNorm: Tackling oversmoothing in GNNs. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_57_1","unstructured":"Kaixiong Zhou Xiao Huang Yuening Li Daochen Zha Rui Chen and Xia Hu. 2020. Towards deeper graph neural networks with differentiable group normalization. In Advances in Neural Information Processing Systems.  Kaixiong Zhou Xiao Huang Yuening Li Daochen Zha Rui Chen and Xia Hu. 2020. Towards deeper graph neural networks with differentiable group normalization. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_58_1","volume-title":"International Conference on Learning Representations.","author":"Zhu Hao","year":"2021","unstructured":"Hao Zhu and Piotr Koniusz . 2021 . Simple spectral graph convolution . In International Conference on Learning Representations. Hao Zhu and Piotr Koniusz. 2021. Simple spectral graph convolution. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btx252"}],"event":{"name":"CIKM '23: The 32nd ACM International Conference on Information and Knowledge Management","location":"Birmingham United Kingdom","acronym":"CIKM '23","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 32nd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3614997","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3583780.3614997","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T16:36:44Z","timestamp":1750178204000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3583780.3614997"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,21]]},"references-count":59,"alternative-id":["10.1145\/3583780.3614997","10.1145\/3583780"],"URL":"https:\/\/doi.org\/10.1145\/3583780.3614997","relation":{},"subject":[],"published":{"date-parts":[[2023,10,21]]},"assertion":[{"value":"2023-10-21","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}