{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,5]],"date-time":"2026-09-05T09:49:43Z","timestamp":1788601783222,"version":"build-2803163510"},"reference-count":128,"publisher":"Association for Computing Machinery (ACM)","issue":"4","funder":[{"DOI":"10.13039\/501100001459","name":"Ministry of Education - Singapore","doi-asserted-by":"publisher","award":["T2EP20122-0003"],"award-info":[{"award-number":["T2EP20122-0003"]}],"id":[{"id":"10.13039\/501100001459","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001475","name":"Nanyang Technological University","doi-asserted-by":"publisher","award":["022029-00001"],"award-info":[{"award-number":["022029-00001"]}],"id":[{"id":"10.13039\/501100001475","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000038","name":"Natural Sciences and Engineering Research Council of Canada","doi-asserted-by":"publisher","award":["RGPIN-2020-05408"],"award-info":[{"award-number":["RGPIN-2020-05408"]}],"id":[{"id":"10.13039\/501100000038","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. ACM Manag. Data"],"published-print":{"date-parts":[[2025,9,22]]},"abstract":"<jats:p>With recent advancements in graph neural networks (GNNs), spectral GNNs have received increasing popularity by virtue of their ability to retrieve graph signals in the spectral domain. These models feature uniqueness in efficient computation as well as rich expressiveness, which stems from advanced management and profound understanding of graph data. However, few systematic studies have been conducted to assess spectral GNNs, particularly in benchmarking their efficiency, memory consumption, and effectiveness in a unified and fair manner. There is also a pressing need to select spectral models suitable for learning specific graph data and deploying them to massive web-scale graphs, which is currently constrained by the varied model designs and training settings.<\/jats:p>\n                  <jats:p>In this work, we extensively benchmark spectral GNNs with a focus on the spectral perspective, demystifying them as spectral graph filters. We analyze and categorize 35 GNNs with 27 corresponding filters, spanning diverse formulations and utilizations of the graph data. Then, we implement the filters within a unified spectral-oriented framework with dedicated graph computations and efficient training schemes. In particular, our implementation enables the deployment of spectral GNNs over million-scale graphs and various tasks with comparable performance and less overhead. Thorough experiments are conducted on the graph filters with comprehensive metrics on effectiveness and efficiency, offering novel observations and practical guidelines that are only available from our evaluations across graph scales. Different from the prevailing belief, our benchmark reveals an intricate landscape regarding the effectiveness and efficiency of spectral graph filters, demonstrating the potential to achieve desirable performance through tailored spectral manipulation of graph data.<\/jats:p>","DOI":"10.1145\/3749156","type":"journal-article","created":{"date-parts":[[2025,9,23]],"date-time":"2025-09-23T17:17:03Z","timestamp":1758647823000},"page":"1-29","source":"Crossref","is-referenced-by-count":6,"title":["A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency, Memory, and Effectiveness"],"prefix":"10.1145","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3176-4401","authenticated-orcid":false,"given":"Ningyi","family":"Liao","sequence":"first","affiliation":[{"name":"College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0839-5460","authenticated-orcid":false,"given":"Haoyu","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5176-6378","authenticated-orcid":false,"given":"Zulun","family":"Zhu","sequence":"additional","affiliation":[{"name":"College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8197-0903","authenticated-orcid":false,"given":"Siqiang","family":"Luo","sequence":"additional","affiliation":[{"name":"College of Computing and Data Science, Nanyang Technological University, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9775-4241","authenticated-orcid":false,"given":"Laks V.S.","family":"Lakshmanan","sequence":"additional","affiliation":[{"name":"Department of Computer Science, The University of British Columbia, Vancouver, BC, Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,9,23]]},"reference":[{"key":"e_1_2_2_1_1","unstructured":"2025. A Comprehensive Benchmark on Spectral GNNs: The Impact on Efficiency Memory and Effectiveness [Technical Report]. arXiv:2406.09675 [cs] https:\/\/github.com\/gdmnl\/Spectral-GNN-Benchmark\/blob\/main\/Report.pdf"},{"key":"e_1_2_2_2_1","first-page":"9","article-title":"Computing Graph Neural Networks: A Survey from Algorithms to","volume":"54","author":"Abadal Sergi","year":"2022","unstructured":"Sergi Abadal, Akshay Jain, Robert Guirado, Jorge L\u00f3pez-Alonso, and Eduard Alarc\u00f3n. 2022. Computing Graph Neural Networks: A Survey from Algorithms to Accelerators. Comput. Surveys, Vol. 54, 9 (Dec. 2022), 1-38.","journal-title":"Accelerators. Comput. Surveys"},{"key":"e_1_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.14778\/3659437.3659453"},{"key":"e_1_2_2_4_1","volume-title":"On the Bottleneck of Graph Neural Networks and Its Practical Implications. In 9th International Conference on Learning Representations. arXiv:2006","author":"Alon Uri","year":"2021","unstructured":"Uri Alon and Eran Yahav. 2021. On the Bottleneck of Graph Neural Networks and Its Practical Implications. In 9th International Conference on Learning Representations. arXiv:2006.05205 [cs, stat]"},{"key":"e_1_2_2_5_1","volume-title":"29th Advances in Neural Information Processing Systems. 2001-2009.","author":"Atwood James","unstructured":"James Atwood and Don Towsley. 2016. Diffusion-Convolutional Neural Networks. In 29th Advances in Neural Information Processing Systems. 2001-2009."},{"key":"e_1_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3054830"},{"key":"e_1_2_2_7_1","volume-title":"A Survey on Spectral Graph Neural Networks. (Feb","author":"Bo Deyu","year":"2023","unstructured":"Deyu Bo, Xiao Wang, Yang Liu, Yuan Fang, Yawen Li, and Chuan Shi. 2023. A Survey on Spectral Graph Neural Networks. (Feb. 2023). arXiv:2302.05631 [cs]"},{"key":"e_1_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16514"},{"key":"e_1_2_2_9_1","volume-title":"2nd 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 2nd International Conference on Learning Representations."},{"key":"e_1_2_2_10_1","unstructured":"Defu Cao Yujing Wang Juanyong Duan Ce Zhang Xia Zhu Congrui Huang Yunhai Tong Bixiong Xu Jing Bai Jie Tong et al. 2020. Spectral temporal graph neural network for multivariate time-series forecasting. In Advances in neural information processing systems Vol. 33. 17766-17778."},{"key":"e_1_2_2_11_1","volume-title":"NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs. In 11th International Conference on Learning Representations.","author":"Chen Jinsong","year":"2023","unstructured":"Jinsong Chen, Kaiyuan Gao, Gaichao Li, and Kun He. 2023b. NAGphormer: A Tokenized Graph Transformer for Node Classification in Large Graphs. In 11th International Conference on Learning Representations."},{"key":"e_1_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2023.3289002"},{"key":"e_1_2_2_13_1","volume-title":"Scalable graph neural networks via bidirectional propagation. 33rd Advances in Neural Information Processing Systems","author":"Chen Ming","year":"2020","unstructured":"Ming Chen, Zhewei Wei, Bolin Ding, Yaliang Li, Ye Yuan, Xiaoyong Du, and Ji Rong Wen. 2020. Scalable graph neural networks via bidirectional propagation. 33rd Advances in Neural Information Processing Systems (2020)."},{"key":"e_1_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.14778\/3632093.3632106"},{"key":"e_1_2_2_15_1","doi-asserted-by":"publisher","DOI":"10.1145\/3627816"},{"key":"e_1_2_2_16_1","volume-title":"BA-GNN: On Learning Bias-Aware Graph Neural Network. In 2022 IEEE 38th International Conference on Data Engineering (ICDE). 3012-3024","author":"Chen Zhengyu","year":"2022","unstructured":"Zhengyu Chen, Teng Xiao, and Kun Kuang. 2022. BA-GNN: On Learning Bias-Aware Graph Neural Network. In 2022 IEEE 38th International Conference on Data Engineering (ICDE). 3012-3024."},{"key":"e_1_2_2_17_1","volume-title":"Adaptive Universal Generalized PageRank Graph Neural Network. In 9th International Conference on Learning Representations. arXiv:2006","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. arXiv:2006.07988 [cs, stat]"},{"key":"e_1_2_2_18_1","volume-title":"Convolutional neural networks on graphs with fast localized spectral filtering. Advances in neural information processing systems","author":"Defferrard Micha\u00ebl","year":"2016","unstructured":"Micha\u00ebl Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional neural networks on graphs with fast localized spectral filtering. Advances in neural information processing systems, Vol. 29 (2016)."},{"key":"e_1_2_2_19_1","volume-title":"GraphZoom: A Multi-Level Spectral Approach for Accurate and Scalable Graph Embedding. In 8th International Conference on Learning Representations.","author":"Deng Chenhui","year":"2020","unstructured":"Chenhui Deng, Zhiqiang Zhao, Yongyu Wang, Zhiru Zhang, and Zhuo Feng. 2020. GraphZoom: A Multi-Level Spectral Approach for Accurate and Scalable Graph Embedding. In 8th International Conference on Learning Representations."},{"key":"e_1_2_2_20_1","volume-title":"Graph signal processing for machine learning: A review and new perspectives","author":"Dong Xiaowen","year":"2020","unstructured":"Xiaowen Dong, Dorina Thanou, Laura Toni, Michael Bronstein, and Pascal Frossard. 2020. Graph signal processing for machine learning: A review and new perspectives. IEEE Signal processing magazine, Vol. 37, 6 (2020), 117-127."},{"key":"e_1_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482226"},{"key":"e_1_2_2_22_1","first-page":"5376","article-title":"A comprehensive study on large-scale graph training: Benchmarking and rethinking","volume":"35","author":"Duan Keyu","year":"2022","unstructured":"Keyu Duan, Zirui Liu, Peihao Wang, Wenqing Zheng, Kaixiong Zhou, Tianlong Chen, Xia Hu, and Zhangyang Wang. 2022. A comprehensive study on large-scale graph training: Benchmarking and rethinking. Advances in Neural Information Processing Systems, Vol. 35 (2022), 5376-5389.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_2_23_1","article-title":"Benchmarking Graph Neural Networks","volume":"23","author":"Dwivedi Vijay Prakash","year":"2022","unstructured":"Vijay Prakash Dwivedi, Chaitanya K Joshi, Anh Tuan Luu, Thomas Laurent, Yoshua Bengio, and Xavier Bresson. 2022. Benchmarking Graph Neural Networks. Journal of Machine Learning Research, Vol. 23 (Dec. 2022), 1-48.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_2_2_24_1","volume-title":"Arun Iyer, Ramakrishna Bairi, and Sundararajan Sellamanickam.","author":"Ekbote Chanakya","year":"2023","unstructured":"Chanakya Ekbote, Ajinkya Pankaj Deshpande, Arun Iyer, Ramakrishna Bairi, and Sundararajan Sellamanickam. 2023. FiGURe: Simple and Efficient Unsupervised Node Representations with Filter Augmentations. In 36th Advances in Neural Information Processing Systems."},{"key":"e_1_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.1145\/3487553.3524203"},{"key":"e_1_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512044"},{"key":"e_1_2_2_27_1","volume-title":"Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds.","author":"Fey Matthias","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."},{"key":"e_1_2_2_28_1","volume-title":"SIGN: Scalable Inception Graph Neural Networks. In ICML 2020 Workshop on Graph Representation Learning and Beyond.","author":"Frasca Fabrizio","year":"2020","unstructured":"Fabrizio Frasca, Emanuele Rossi, Davide Eynard, Ben Chamberlain, Michael Bronstein, and Federico Monti. 2020. SIGN: Scalable Inception Graph Neural Networks. In ICML 2020 Workshop on Graph Representation Learning and Beyond."},{"key":"e_1_2_2_29_1","first-page":"551","volume-title":"15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21)","author":"Gandhi Swapnil","year":"2021","unstructured":"Swapnil Gandhi and Anand Padmanabha Iyer. 2021. P3 : Distributed Deep Graph Learning at Scale. In 15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21). 551-568."},{"key":"e_1_2_2_30_1","volume-title":"Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagation. In 2024 IEEE 40th International Conference on Data Engineering (ICDE).","author":"Gao Xinyi","year":"2024","unstructured":"Xinyi Gao, Wentao Zhang, Junliang Yu, Yingxia Shao, Quoc Viet Hung Nguyen, Bin Cui, and Hongzhi Yin. 2024. Accelerating Scalable Graph Neural Network Inference with Node-Adaptive Propagation. In 2024 IEEE 40th International Conference on Data Engineering (ICDE)."},{"key":"e_1_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583268"},{"key":"e_1_2_2_32_1","volume-title":"32nd Advances in Neural Information Processing Systems. arXiv:1911.05485","author":"Gasteiger Johannes","unstructured":"Johannes Gasteiger, Stefan Wei\u00dfenberger, and Stephan G\u00fcnnemann. 2019. Diffusion Improves Graph Learning. In 32nd Advances in Neural Information Processing Systems. arXiv:1911.05485"},{"key":"e_1_2_2_33_1","volume-title":"Towards Learning from Graphs with Heterophily: Progress and Future. (Jan","author":"Gong Chenghua","year":"2024","unstructured":"Chenghua Gong, Yao Cheng, Xiang Li, Caihua Shan, Siqiang Luo, and Chuan Shi. 2024. Towards Learning from Graphs with Heterophily: Progress and Future. (Jan. 2024). arXiv:2401.09769 [cs]"},{"key":"e_1_2_2_34_1","doi-asserted-by":"publisher","DOI":"10.14778\/3681954.3681961"},{"key":"e_1_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599275"},{"key":"e_1_2_2_36_1","volume-title":"Graph Neural Networks with Learnable and Optimal Polynomial Bases. In 40th International Conference on Machine Learning","volume":"202","author":"Guo Yuhe","year":"2023","unstructured":"Yuhe Guo and Zhewei Wei. 2023b. Graph Neural Networks with Learnable and Optimal Polynomial Bases. In 40th International Conference on Machine Learning, Vol. 202. PMLR, Honolulu, Hawaii, USA. arXiv:2302.12432 [cs]"},{"key":"e_1_2_2_37_1","volume-title":"Proceedings of the 31st International Conference on Neural Information Processing Systems. 1025-1035","author":"Hamilton William L","year":"2017","unstructured":"William L Hamilton, Rex Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems. 1025-1035."},{"key":"e_1_2_2_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.acha.2010.04.005"},{"key":"e_1_2_2_39_1","volume-title":"34th Advances in Neural Information Processing Systems.","author":"He Mingguo","unstructured":"Mingguo He, Zhewei Wei, Zengfeng Huang, and Hongteng Xu. 2021. BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation. In 34th Advances in Neural Information Processing Systems."},{"key":"e_1_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.52202\/068431-0527"},{"key":"e_1_2_2_41_1","volume-title":"OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs. arXiv preprint arXiv:2103.09430","author":"Hu Weihua","year":"2021","unstructured":"Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, and Jure Leskovec. 2021a. OGB-LSC: A Large-Scale Challenge for Machine Learning on Graphs. arXiv preprint arXiv:2103.09430 (2021)."},{"key":"e_1_2_2_42_1","volume-title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs. 33rd 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, Jure Leskovec, Regina Barzilay, Peter Battaglia, Yoshua Bengio, Michael Bronstein, Stephan G\u00fcnnemann, Will Hamilton, Tommi Jaakkola, Stefanie Jegelka, Maximilian Nickel, Chris Re, Le Song, Jian Tang, Max Welling, and Rich Zemel. 2020. Open Graph Benchmark: Datasets for Machine Learning on Graphs. 33rd Advances in Neural Information Processing Systems (2020)."},{"key":"e_1_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMM.2021.3111440"},{"key":"e_1_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.14778\/3648160.3648184"},{"key":"e_1_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i6.20664"},{"key":"e_1_2_2_46_1","volume-title":"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."},{"key":"e_1_2_2_47_1","first-page":"1","volume-title":"7th International Conference on Learning Representations","author":"Klicpera Johannes","year":"2019","unstructured":"Johannes Klicpera, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2019. Predict then propagate: Graph neural networks meet personalized PageRank. 7th International Conference on Learning Representations (2019), 1-15."},{"key":"e_1_2_2_48_1","doi-asserted-by":"publisher","DOI":"10.1145\/3637528.3671765"},{"key":"e_1_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482049"},{"key":"e_1_2_2_50_1","doi-asserted-by":"publisher","DOI":"10.52202\/075280-0169"},{"key":"e_1_2_2_51_1","volume-title":"39th International Conference on Machine Learning (Proceedings of Machine Learning Research","volume":"12796","author":"Li Mingjie","year":"2022","unstructured":"Mingjie Li, Xiaojun Guo, Yifei Wang, Yisen Wang, and Zhouchen Lin. 2022. G$^2$CN: Graph Gaussian Convolution Networks with Concentrated Graph Filters. In 39th International Conference on Machine Learning (Proceedings of Machine Learning Research, Vol. 162), Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesvari, Gang Niu, and Sivan Sabato (Eds.). PMLR, 12782-12796."},{"key":"e_1_2_2_52_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"e_1_2_2_53_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00981"},{"key":"e_1_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589334.3645450"},{"key":"e_1_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.14778\/3617838.3617842"},{"key":"e_1_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV48922.2021.00373"},{"key":"e_1_2_2_57_1","doi-asserted-by":"publisher","DOI":"10.14778\/3583140.3583150"},{"key":"e_1_2_2_58_1","doi-asserted-by":"publisher","DOI":"10.1145\/3583780.3615045"},{"key":"e_1_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.52202\/075280-0446"},{"key":"e_1_2_2_60_1","doi-asserted-by":"publisher","DOI":"10.14778\/3551793.3551866"},{"key":"e_1_2_2_61_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-023-00829-6"},{"key":"e_1_2_2_62_1","volume-title":"Unifews: You Need Fewer Operations for Efficient Graph Neural Networks. In 42nd International Conference on Machine Learning","volume":"267","author":"Liao Ningyi","year":"2025","unstructured":"Ningyi Liao, Zihao Yu, Ruixiao Zeng, and Siqiang Luo. 2025. Unifews: You Need Fewer Operations for Efficient Graph Neural Networks. In 42nd International Conference on Machine Learning (Vancouver, Canada), Vol. 267. PMLR."},{"key":"e_1_2_2_63_1","volume-title":"Vaishnavi Gupta, Omkar Bhalerao, and Ser-Nam Lim.","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. In 34th Advances in Neural Information Processing Systems."},{"key":"e_1_2_2_64_1","volume-title":"SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation. In 2025 IEEE 41st International Conference on Data Engineering (ICDE). IEEE Computer Society","author":"Liu Haoyu","year":"2025","unstructured":"Haoyu Liu, Ningyi Liao, and Siqiang Luo. 2025. SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation. In 2025 IEEE 41st International Conference on Data Engineering (ICDE). IEEE Computer Society, 1924-1937."},{"key":"e_1_2_2_65_1","doi-asserted-by":"publisher","DOI":"10.14778\/3415478.3415482"},{"key":"e_1_2_2_66_1","doi-asserted-by":"publisher","DOI":"10.14778\/3665844.3665855"},{"key":"e_1_2_2_67_1","doi-asserted-by":"publisher","DOI":"10.1145\/3394486.3403076"},{"key":"e_1_2_2_68_1","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2022\/772"},{"key":"e_1_2_2_69_1","volume-title":"Advances in neural information processing systems","author":"Liu Yijing","year":"1941","unstructured":"Yijing Liu, Qinxian Liu, Jian-Wei Zhang, Haozhe Feng, Zhongwei Wang, Zihan Zhou, and Wei Chen. 2022a. Multivariate time-series forecasting with temporal polynomial graph neural networks. In Advances in neural information processing systems, Vol. 35. 19414-19426."},{"key":"e_1_2_2_70_1","unstructured":"Sitao Luan Chenqing Hua Qincheng Lu Liheng Ma Lirong Wu Xinyu Wang Minkai Xu Xiao-Wen Chang Doina Precup Rex Ying Stan Z. Li Jian Tang Guy Wolf and Stefanie Jegelka. 2024. The Heterophilic Graph Learning Handbook: Benchmarks Models Theoretical Analysis Applications and Challenges. arXiv:2407.09618 [cs]"},{"key":"e_1_2_2_71_1","volume-title":"36th Advances in Neural Information Processing Systems.","author":"Luan Sitao","unstructured":"Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, and Doina Precup. 2022a. Revisiting Heterophily For Graph Neural Networks. In 36th Advances in Neural Information Processing Systems."},{"key":"e_1_2_2_72_1","volume-title":"Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks. In NeurIPS 2022 Workshop: New Frontiers in Graph Learning.","author":"Luan Sitao","year":"2022","unstructured":"Sitao Luan, Mingde Zhao, Chenqing Hua, Xiao-Wen Chang, and Doina Precup. 2022b. Complete the Missing Half: Augmenting Aggregation Filtering with Diversification for Graph Convolutional Networks. In NeurIPS 2022 Workshop: New Frontiers in Graph Learning."},{"key":"e_1_2_2_73_1","doi-asserted-by":"publisher","DOI":"10.1145\/3177732.3177736"},{"key":"e_1_2_2_74_1","doi-asserted-by":"publisher","DOI":"10.52202\/079017-3098"},{"key":"e_1_2_2_75_1","volume-title":"Acceleration Algorithms in GNNs: A Survey. (May","author":"Ma Lu","year":"2024","unstructured":"Lu Ma, Zeang Sheng, Xunkai Li, Xinyi Gao, Zhezheng Hao, Ling Yang, Wentao Zhang, and Bin Cui. 2024b. Acceleration Algorithms in GNNs: A Survey. (May 2024). arXiv:2405.04114 [cs]"},{"key":"e_1_2_2_76_1","doi-asserted-by":"publisher","DOI":"10.14778\/3681954.3681968"},{"key":"e_1_2_2_77_1","doi-asserted-by":"publisher","DOI":"10.52202\/068431-0402"},{"key":"e_1_2_2_78_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2022.101695"},{"key":"e_1_2_2_79_1","doi-asserted-by":"publisher","DOI":"10.14778\/3489496.3489511"},{"key":"e_1_2_2_80_1","first-page":"1703","volume-title":"Simple and Deep Graph Convolutional Networks. In 37th International Conference on Machine Learning","volume":"119","author":"Ming Chen","year":"2020","unstructured":"Chen Ming, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li. 2020. Simple and Deep Graph Convolutional Networks. In 37th International Conference on Machine Learning, Vol. 119. PMLR, 1703-1713. arXiv:2007.02133"},{"key":"e_1_2_2_81_1","unstructured":"Hoang Nt and Takanori Maehara. 2019. Revisiting graph neural networks: All we have is low-pass filters. (2019). arXiv:1905.09550 [cs]"},{"key":"e_1_2_2_82_1","unstructured":"Lawrence Page Sergey Brin Rajeev Motwani and Terry Winograd. 1999. The PageRank Citation Ranking: Bringing Order to the Web. Technical Report."},{"key":"e_1_2_2_83_1","doi-asserted-by":"publisher","DOI":"10.14778\/3551793.3551819"},{"key":"e_1_2_2_84_1","volume-title":"Geom-GCN: Geometric Graph Convolutional Networks. 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. International Conference on Learning Representations (2020)."},{"key":"e_1_2_2_85_1","volume-title":"11th International Conference on Learning Representations.","author":"Platonov Oleg","year":"2023","unstructured":"Oleg Platonov, Denis Kuznedelev, Michael Diskin, Artem Babenko, and Liudmila Prokhorenkova. 2023. A Critical Look at Evaluation of GNNs under Heterophily: Are We Really Making Progress?. In 11th International Conference on Learning Representations."},{"key":"e_1_2_2_86_1","doi-asserted-by":"publisher","DOI":"10.1145\/3164135.3164139"},{"key":"e_1_2_2_87_1","volume-title":"The 3rd Learning on Graphs Conference.","author":"Sancak Kaan","year":"2024","unstructured":"Kaan Sancak, Muhammed Fatih Balin, and Umit Catalyurek. 2024. Do We Really Need Complicated Graph Learning Models? - A Simple but Effective Baseline. In The 3rd Learning on Graphs Conference."},{"key":"e_1_2_2_88_1","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"e_1_2_2_89_1","doi-asserted-by":"publisher","DOI":"10.14778\/3685800.3685844"},{"key":"e_1_2_2_90_1","doi-asserted-by":"publisher","DOI":"10.14778\/3681954.3681976"},{"key":"e_1_2_2_91_1","doi-asserted-by":"publisher","DOI":"10.1145\/3626716"},{"key":"e_1_2_2_92_1","doi-asserted-by":"publisher","DOI":"10.1145\/3340531.3411872"},{"key":"e_1_2_2_93_1","volume-title":"8th International Conference on Learning Representations.","author":"Veli\u010dkovi\u0107 Petar","year":"2017","unstructured":"Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. In 8th International Conference on Learning Representations."},{"key":"e_1_2_2_94_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589288"},{"key":"e_1_2_2_95_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467243"},{"key":"e_1_2_2_96_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2019.00177"},{"key":"e_1_2_2_97_1","volume-title":"Highly-Performant Package for Graph Neural Networks.","author":"Wang Minjie","year":"2019","unstructured":"Minjie Wang, Da Zheng, Zihao Ye, Quan Gan, Mufei Li, Xiang Song, Jinjing Zhou, Chao Ma, Lingfan Yu, Yu Gai, Tianjun Xiao, Tong He, George Karypis, Jinyang Li, and Zheng Zhang. 2019b. Deep Graph Library: A Graph-Centric, Highly-Performant Package for Graph Neural Networks. (2019). arXiv:1909.01315"},{"key":"e_1_2_2_98_1","first-page":"23341","volume-title":"Proceedings of the 39th International Conference on Machine Learning","volume":"162","author":"Wang Xiyuan","year":"2022","unstructured":"Xiyuan Wang and Muhan Zhang. 2022. How Powerful are Spectral Graph Neural Networks. In Proceedings of the 39th International Conference on Machine Learning, Vol. 162. PMLR, 23341-23362."},{"key":"e_1_2_2_99_1","first-page":"5758","article-title":"Dissecting the Diffusion Process in Linear Graph Convolutional Networks","volume":"34","author":"Wang Yifei","year":"2021","unstructured":"Yifei Wang, Yisen Wang, Jiansheng Yang, and Zhouchen Lin. 2021b. Dissecting the Diffusion Process in Linear Graph Convolutional Networks. In Advances in Neural Information Processing Systems, Vol. 34. 5758-5769.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_2_100_1","doi-asserted-by":"publisher","DOI":"10.1145\/3503043"},{"key":"e_1_2_2_101_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM54844.2022.00158"},{"key":"e_1_2_2_102_1","first-page":"6861","volume-title":"Proceedings of the 36th International Conference on Machine Learning, Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.)","volume":"97","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 Proceedings of the 36th International Conference on Machine Learning, Kamalika Chaudhuri and Ruslan Salakhutdinov (Eds.), Vol. 97. 6861-6871."},{"key":"e_1_2_2_103_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_2_2_104_1","volume-title":"7th International Conference on Learning Representations. 1-17","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu, Stefanie Jegelka, Weihua Hu, and Jure Leskovec. 2019. How Powerful Are Graph Neural Networks?. In 7th International Conference on Learning Representations. 1-17. arXiv:1810.00826"},{"key":"e_1_2_2_105_1","volume-title":"Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks. In 22nd IEEE International Conference on Data Mining. arXiv:2102","author":"Yan Yujun","year":"2022","unstructured":"Yujun Yan, Milad Hashemi, Kevin Swersky, Yaoqing Yang, and Danai Koutra. 2022. Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks. In 22nd IEEE International Conference on Data Mining. arXiv:2102.06462 [cs]"},{"key":"e_1_2_2_106_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16588"},{"key":"e_1_2_2_107_1","volume-title":"Proceedings of the 39th International Conference on Machine Learning. PMLR, 25261-25279","author":"Yang Mingqi","year":"2022","unstructured":"Mingqi Yang, Yanming Shen, Rui Li, Heng Qi, Qiang Zhang, and Baocai Yin. 2022. A New Perspective on the Effects of Spectrum in Graph Neural Networks. In Proceedings of the 39th International Conference on Machine Learning. PMLR, 25261-25279."},{"key":"e_1_2_2_108_1","doi-asserted-by":"publisher","DOI":"10.14778\/3551793.3551831"},{"key":"e_1_2_2_109_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_2_2_110_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467284"},{"key":"e_1_2_2_111_1","doi-asserted-by":"publisher","DOI":"10.14778\/3665844.3665856"},{"key":"e_1_2_2_112_1","doi-asserted-by":"publisher","DOI":"10.14778\/3648160.3648167"},{"key":"e_1_2_2_113_1","volume-title":"GraphSAINT: Graph Sampling Based Learning Method. In International Conference on Learning Representations.","author":"Zeng Hanqing","year":"2019","unstructured":"Hanqing Zeng, Hongkuan Zhou, Ajitesh Srivastava, Rajgopal Kannan, and Viktor Prasanna. 2019. GraphSAINT: Graph Sampling Based Learning Method. In International Conference on Learning Representations."},{"key":"e_1_2_2_114_1","doi-asserted-by":"publisher","DOI":"10.14778\/3415478.3415539"},{"key":"e_1_2_2_115_1","first-page":"5165","article-title":"Link prediction based on graph neural networks","volume":"31","author":"Zhang Muhan","year":"2018","unstructured":"Muhan Zhang and Yixin Chen. 2018. Link prediction based on graph neural networks. Advances in Neural Information Processing Systems, Vol. 31 (2018), 5165-5175.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_2_116_1","volume-title":"A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware. (June","author":"Zhang Shichang","year":"2023","unstructured":"Shichang Zhang, Atefeh Sohrabizadeh, Cheng Wan, Zijie Huang, Ziniu Hu, Yewen Wang, Yingyan, Lin, Jason Cong, and Yizhou Sun. 2023b. A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware. (June 2023). arXiv:2306.14052 [cs]"},{"key":"e_1_2_2_117_1","doi-asserted-by":"publisher","DOI":"10.14778\/3476249.3476295"},{"key":"e_1_2_2_118_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589311"},{"key":"e_1_2_2_119_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3112746"},{"key":"e_1_2_2_120_1","article-title":"Deep Learning on Graphs: A Survey","volume":"14","author":"Zhang Ziwei","year":"2020","unstructured":"Ziwei Zhang, Peng Cui, and Wenwu Zhu. 2020a. Deep Learning on Graphs: A Survey. IEEE Transactions on Knowledge and Data Engineering, Vol. 14, 8 (2020).","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_2_2_121_1","first-page":"21171","article-title":"Hierarchical graph transformer with adaptive node sampling","volume":"35","author":"Zhang Zaixi","year":"2022","unstructured":"Zaixi Zhang, Qi Liu, Qingyong Hu, and Chee-Kong Lee. 2022. Hierarchical graph transformer with adaptive node sampling. Advances in Neural Information Processing Systems, Vol. 35 (2022), 21171-21183.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_2_122_1","doi-asserted-by":"publisher","DOI":"10.14778\/3514061.3514069"},{"key":"e_1_2_2_123_1","doi-asserted-by":"publisher","DOI":"10.14778\/3598581.3598595"},{"key":"e_1_2_2_124_1","doi-asserted-by":"publisher","DOI":"10.14778\/3461535.3461547"},{"key":"e_1_2_2_125_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"e_1_2_2_126_1","volume-title":"Simple Spectral Graph Convolution. In 9th International Conference on Learning Representations.","author":"Zhu Hao","year":"2021","unstructured":"Hao Zhu and Piotr Koniusz. 2021. Simple Spectral Graph Convolution. In 9th International Conference on Learning Representations."},{"key":"e_1_2_2_127_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449953"},{"key":"e_1_2_2_128_1","doi-asserted-by":"publisher","DOI":"10.14778\/3352063.3352127"}],"container-title":["Proceedings of the ACM on Management of Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3749156","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,13]],"date-time":"2026-06-13T04:37:10Z","timestamp":1781325430000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3749156"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,9,22]]},"references-count":128,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2025,9,22]]}},"alternative-id":["10.1145\/3749156"],"URL":"https:\/\/doi.org\/10.1145\/3749156","relation":{},"ISSN":["2836-6573"],"issn-type":[{"value":"2836-6573","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,9,22]]}}}