{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:33:23Z","timestamp":1763202803456,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":57,"publisher":"ACM","license":[{"start":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T00:00:00Z","timestamp":1691107200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"Alibaba Group through Alibaba Innovative Research Program"},{"name":"major key project of PCL","award":["PCL2021A12"],"award-info":[{"award-number":["PCL2021A12"]}]},{"name":"National Natural Science Foundation of China","award":["No. U2241212 No. 61972401 No. 61932001 No. 61832017"],"award-info":[{"award-number":["No. U2241212 No. 61972401 No. 61932001 No. 61832017"]}]},{"name":"Beijing Natural Science Foundation","award":["No. 4222028"],"award-info":[{"award-number":["No. 4222028"]}]},{"name":"Beijing Outstanding Young Scientist Program","award":["No.BJJWZYJH012019100020098"],"award-info":[{"award-number":["No.BJJWZYJH012019100020098"]}]},{"name":"Huawei-Renmin University joint program on Information Retrieval"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2023,8,6]]},"DOI":"10.1145\/3580305.3599275","type":"proceedings-article","created":{"date-parts":[[2023,8,4]],"date-time":"2023-08-04T18:10:58Z","timestamp":1691172658000},"page":"614-625","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":9,"title":["Clenshaw Graph Neural Networks"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-0065-418X","authenticated-orcid":false,"given":"Yuhe","family":"Guo","sequence":"first","affiliation":[{"name":"Renmin University of China, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3620-5086","authenticated-orcid":false,"given":"Zhewei","family":"Wei","sequence":"additional","affiliation":[{"name":"Renmin University of China, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2023,8,4]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"international conference on machine learning. PMLR, 21--29","author":"Abu-El-Haija Sami","year":"2019","unstructured":"Sami Abu-El-Haija , Bryan Perozzi , Amol Kapoor , Nazanin Alipourfard , Kristina Lerman , Hrayr Harutyunyan , Greg Ver Steeg , and Aram Galstyan . 2019 . Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing . In international conference on machine learning. PMLR, 21--29 . Sami Abu-El-Haija, Bryan Perozzi, Amol Kapoor, Nazanin Alipourfard, Kristina Lerman, Hrayr Harutyunyan, Greg Ver Steeg, and Aram Galstyan. 2019. Mixhop: Higher-order graph convolutional architectures via sparsified neighborhood mixing. In international conference on machine learning. PMLR, 21--29."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1145\/3292500.3330701"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3054830"},{"key":"e_1_3_2_2_4_1","volume-title":"International Conference on Machine Learning. PMLR, 1407--1418","author":"Chamberlain Ben","year":"2021","unstructured":"Ben Chamberlain , James Rowbottom , Maria I Gorinova , Michael Bronstein , Stefan Webb , and Emanuele Rossi . 2021 . Grand: Graph neural diffusion . In International Conference on Machine Learning. PMLR, 1407--1418 . Ben Chamberlain, James Rowbottom, Maria I Gorinova, Michael Bronstein, Stefan Webb, and Emanuele Rossi. 2021. Grand: Graph neural diffusion. In International Conference on Machine Learning. PMLR, 1407--1418."},{"key":"e_1_3_2_2_5_1","unstructured":"Ming Chen Zhewei Wei Zengfeng Huang Bolin Ding and Yaliang Li. 2020. Simple and deep graph convolutional networks. In ICML. PMLR 1725--1735.  Ming Chen Zhewei Wei Zengfeng Huang Bolin Ding and Yaliang Li. 2020. Simple and deep graph convolutional networks. In ICML. PMLR 1725--1735."},{"key":"e_1_3_2_2_6_1","unstructured":"Eli Chien Jianhao Peng Pan Li and Olgica Milenkovic. 2021. Adaptive Universal Generalized PageRank Graph Neural Network. In ICLR.  Eli Chien Jianhao Peng Pan Li and Olgica Milenkovic. 2021. Adaptive Universal Generalized PageRank Graph Neural Network. In ICLR."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1090\/S0025-5718-1955-0071856-0"},{"key":"e_1_3_2_2_9_1","volume-title":"Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. (6","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. (6 2016 ). http:\/\/arxiv.org\/abs\/1606.09375 Micha\u00ebl Defferrard, Xavier Bresson, and Pierre Vandergheynst. 2016. Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering. (6 2016). http:\/\/arxiv.org\/abs\/1606.09375"},{"key":"e_1_3_2_2_10_1","volume-title":"Convolutional networks on graphs for learning molecular fingerprints. Advances in neural information processing systems","author":"Duvenaud David K","year":"2015","unstructured":"David K Duvenaud , Dougal Maclaurin , Jorge Iparraguirre , Rafael Bombarell , Timothy Hirzel , Al\u00e1n Aspuru-Guzik , and Ryan P Adams . 2015. Convolutional networks on graphs for learning molecular fingerprints. Advances in neural information processing systems , Vol. 28 ( 2015 ). David K Duvenaud, Dougal Maclaurin, Jorge Iparraguirre, Rafael Bombarell, Timothy Hirzel, Al\u00e1n Aspuru-Guzik, and Ryan P Adams. 2015. Convolutional networks on graphs for learning molecular fingerprints. Advances in neural information processing systems, Vol. 28 (2015)."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"crossref","unstructured":"Wenqi Fan Yao Ma Qing Li Yuan He Eric Zhao Jiliang Tang and Dawei Yin. 2019. Graph neural networks for social recommendation. In The world wide web conference. 417--426.  Wenqi Fan Yao Ma Qing Li Yuan He Eric Zhao Jiliang Tang and Dawei Yin. 2019. Graph neural networks for social recommendation. In The world wide web conference. 417--426.","DOI":"10.1145\/3308558.3313488"},{"volume-title":"Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds.","author":"Fey Matthias","key":"e_1_3_2_2_12_1","unstructured":"Matthias Fey and Jan E. Lenssen . 2019 . Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds. Matthias Fey and Jan E. Lenssen. 2019. Fast Graph Representation Learning with PyTorch Geometric. In ICLR Workshop on Representation Learning on Graphs and Manifolds."},{"key":"e_1_3_2_2_13_1","volume-title":"Wavelets on Graphs via Spectral Graph Theory. CoRR","author":"Hammond David K.","year":"2009","unstructured":"David K. Hammond , Pierre Vandergheynst , and R\u00e9mi Gribonval . 2009. Wavelets on Graphs via Spectral Graph Theory. CoRR , Vol. abs\/ 0912 .3848 ( 2009 ). showeprint[arXiv]0912.3848 http:\/\/arxiv.org\/abs\/0912.3848 David K. Hammond, Pierre Vandergheynst, and R\u00e9mi Gribonval. 2009. Wavelets on Graphs via Spectral Graph Theory. CoRR, Vol. abs\/0912.3848 (2009). showeprint[arXiv]0912.3848 http:\/\/arxiv.org\/abs\/0912.3848"},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_2_2_15_1","volume-title":"Advances in Neural Information Processing Systems","volume":"34","author":"He Mingguo","year":"2021","unstructured":"Mingguo He , Zhewei Wei , Zengfeng Huang , and Hongteng Xu . 2021 . BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation . Advances in Neural Information Processing Systems , Vol. 34 (6 2021), 14239--14251. http:\/\/arxiv.org\/abs\/2106.10994 Mingguo He, Zhewei Wei, Zengfeng Huang, and Hongteng Xu. 2021. BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation. Advances in Neural Information Processing Systems, Vol. 34 (6 2021), 14239--14251. http:\/\/arxiv.org\/abs\/2106.10994"},{"key":"e_1_3_2_2_16_1","volume-title":"arXiv preprint arXiv:2202.03580","author":"He Mingguo","year":"2022","unstructured":"Mingguo He , Zhewei Wei , and Ji-Rong Wen . 2022. Convolutional Neural Networks on Graphs with Chebyshev Approximation , Revisited. arXiv preprint arXiv:2202.03580 ( 2022 ). Mingguo He, Zhewei Wei, and Ji-Rong Wen. 2022. Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited. arXiv preprint arXiv:2202.03580 (2022)."},{"key":"e_1_3_2_2_17_1","doi-asserted-by":"publisher","DOI":"10.1098\/rstl.1819.0023"},{"key":"e_1_3_2_2_18_1","volume-title":"Densenet: Implementing efficient convnet descriptor pyramids. arXiv preprint arXiv:1404.1869","author":"Iandola Forrest","year":"2014","unstructured":"Forrest Iandola , Matt Moskewicz , Sergey Karayev , Ross Girshick , Trevor Darrell , and Kurt Keutzer . 2014 . Densenet: Implementing efficient convnet descriptor pyramids. arXiv preprint arXiv:1404.1869 (2014). Forrest Iandola, Matt Moskewicz, Sergey Karayev, Ross Girshick, Trevor Darrell, and Kurt Keutzer. 2014. Densenet: Implementing efficient convnet descriptor pyramids. arXiv preprint arXiv:1404.1869 (2014)."},{"key":"e_1_3_2_2_19_1","volume-title":"Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980","author":"Kingma Diederik P","year":"2014","unstructured":"Diederik P Kingma and Jimmy Ba . 2014 . Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014). Diederik P Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)."},{"key":"e_1_3_2_2_20_1","unstructured":"Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR.  Thomas N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR."},{"key":"e_1_3_2_2_21_1","unstructured":"Johannes Klicpera Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2019a. Predict then propagate: Graph neural networks meet personalized pagerank. In ICLR.  Johannes Klicpera Aleksandar Bojchevski and Stephan G\u00fcnnemann. 2019a. Predict then propagate: Graph neural networks meet personalized pagerank. In ICLR."},{"key":"e_1_3_2_2_22_1","volume-title":"Diffusion improves graph learning. arXiv preprint arXiv:1911.05485","author":"Klicpera Johannes","year":"2019","unstructured":"Johannes Klicpera , Stefan Wei\u00dfenberger , and Stephan G\u00fcnnemann . 2019b. Diffusion improves graph learning. arXiv preprint arXiv:1911.05485 ( 2019 ). Johannes Klicpera, Stefan Wei\u00dfenberger, and Stephan G\u00fcnnemann. 2019b. Diffusion improves graph learning. arXiv preprint arXiv:1911.05485 (2019)."},{"key":"e_1_3_2_2_23_1","volume-title":"DeepGCNs: Can GCNs Go as Deep as CNNs? (4","author":"Li Guohao","year":"2019","unstructured":"Guohao Li , Matthias M\u00fcller , Ali Thabet , and Bernard Ghanem . 2019. DeepGCNs: Can GCNs Go as Deep as CNNs? (4 2019 ). http:\/\/arxiv.org\/abs\/1904.03751 Guohao Li, Matthias M\u00fcller, Ali Thabet, and Bernard Ghanem. 2019. DeepGCNs: Can GCNs Go as Deep as CNNs? (4 2019). http:\/\/arxiv.org\/abs\/1904.03751"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11604"},{"key":"e_1_3_2_2_25_1","volume-title":"Finding Global Homophily in Graph Neural Networks When Meeting Heterophily. In International Conference on Machine Learning, ICML 2022","volume":"13256","author":"Li Xiang","year":"2022","unstructured":"Xiang Li , Renyu Zhu , Yao Cheng , Caihua Shan , Siqiang Luo , Dongsheng Li , and Weining Qian . 2022 . Finding Global Homophily in Graph Neural Networks When Meeting Heterophily. In International Conference on Machine Learning, ICML 2022 , 17-23 July 2022, Baltimore, Maryland, USA (Proceedings of Machine Learning Research , Vol. 162), Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesv\u00e1ri, Gang Niu, and Sivan Sabato (Eds.). PMLR, 13242-- 13256 . Xiang Li, Renyu Zhu, Yao Cheng, Caihua Shan, Siqiang Luo, Dongsheng Li, and Weining Qian. 2022. Finding Global Homophily in Graph Neural Networks When Meeting Heterophily. In International Conference on Machine Learning, ICML 2022, 17-23 July 2022, Baltimore, Maryland, USA (Proceedings of Machine Learning Research, Vol. 162), Kamalika Chaudhuri, Stefanie Jegelka, Le Song, Csaba Szepesv\u00e1ri, Gang Niu, and Sivan Sabato (Eds.). PMLR, 13242--13256."},{"key":"e_1_3_2_2_26_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. Advances in Neural Information Processing Systems , Vol. 34 (10 2021), 20887--20902. http:\/\/arxiv.org\/abs\/2110.14446 Derek Lim, Felix Hohne, Xiuyu Li, Sijia Linda Huang, Vaishnavi Gupta, Omkar Bhalerao, and Ser-Nam Lim. 2021. Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods. Advances in Neural Information Processing Systems, Vol. 34 (10 2021), 20887--20902. http:\/\/arxiv.org\/abs\/2110.14446"},{"key":"e_1_3_2_2_27_1","volume-title":"Graph Neural Networks with Adaptive Residual. NIPS","author":"Liu Xiaorui","year":"2021","unstructured":"Xiaorui Liu , Jiayuan Ding , Wei Jin , Han Xu , Yao Ma , Zitao Liu , and Jiliang Tang . 2021. Graph Neural Networks with Adaptive Residual. NIPS ( 2021 ). https:\/\/github.com\/lxiaorui\/AirGNN. Xiaorui Liu, Jiayuan Ding, Wei Jin, Han Xu, Yao Ma, Zitao Liu, and Jiliang Tang. 2021. Graph Neural Networks with Adaptive Residual. NIPS (2021). https:\/\/github.com\/lxiaorui\/AirGNN."},{"key":"e_1_3_2_2_28_1","volume-title":"Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification? (9","author":"Luan Sitao","year":"2021","unstructured":"Sitao Luan , Chenqing Hua , Qincheng Lu , Jiaqi Zhu , Mingde Zhao , Shuyuan Zhang , Xiao-Wen Chang , and Doina Precup . 2021a. Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification? (9 2021 ). http:\/\/arxiv.org\/abs\/2109.05641 Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, and Doina Precup. 2021a. Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification? (9 2021). http:\/\/arxiv.org\/abs\/2109.05641"},{"key":"e_1_3_2_2_29_1","volume-title":"Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification? arXiv preprint arXiv:2109.05641","author":"Luan Sitao","year":"2021","unstructured":"Sitao Luan , Chenqing Hua , Qincheng Lu , Jiaqi Zhu , Mingde Zhao , Shuyuan Zhang , Xiao-Wen Chang , and Doina Precup . 2021b. Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification? arXiv preprint arXiv:2109.05641 ( 2021 ). Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, and Doina Precup. 2021b. Is Heterophily A Real Nightmare For Graph Neural Networks To Do Node Classification? arXiv preprint arXiv:2109.05641 (2021)."},{"key":"e_1_3_2_2_30_1","volume-title":"Revisiting heterophily for graph neural networks. arXiv preprint arXiv:2210.07606","author":"Luan Sitao","year":"2022","unstructured":"Sitao Luan , Chenqing Hua , Qincheng Lu , Jiaqi Zhu , Mingde Zhao , Shuyuan Zhang , Xiao-Wen Chang , and Doina Precup . 2022. Revisiting heterophily for graph neural networks. arXiv preprint arXiv:2210.07606 ( 2022 ). Sitao Luan, Chenqing Hua, Qincheng Lu, Jiaqi Zhu, Mingde Zhao, Shuyuan Zhang, Xiao-Wen Chang, and Doina Precup. 2022. Revisiting heterophily for graph neural networks. arXiv preprint arXiv:2210.07606 (2022)."},{"volume-title":"Chebyshev polynomials","author":"Mason John C","key":"e_1_3_2_2_31_1","unstructured":"John C Mason and David C Handscomb . 2002. Chebyshev polynomials . Chapman and Hall\/CRC. John C Mason and David C Handscomb. 2002. Chebyshev polynomials. Chapman and Hall\/CRC."},{"key":"e_1_3_2_2_32_1","volume-title":"Birds of a feather: Homophily in social networks. Annual review of sociology","author":"McPherson Miller","year":"2001","unstructured":"Miller McPherson , Lynn Smith-Lovin , and James M Cook . 2001. Birds of a feather: Homophily in social networks. Annual review of sociology ( 2001 ), 415--444. Miller McPherson, Lynn Smith-Lovin, and James M Cook. 2001. Birds of a feather: Homophily in social networks. Annual review of sociology (2001), 415--444."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2013.6638704"},{"key":"e_1_3_2_2_34_1","volume-title":"Revisiting graph neural networks: All we have is low-pass filters. arXiv preprint arXiv:1905.09550","author":"Nt Hoang","year":"2019","unstructured":"Hoang Nt and Takanori Maehara . 2019. Revisiting graph neural networks: All we have is low-pass filters. arXiv preprint arXiv:1905.09550 ( 2019 ). Hoang Nt and Takanori Maehara. 2019. Revisiting graph neural networks: All we have is low-pass filters. arXiv preprint arXiv:1905.09550 (2019)."},{"key":"e_1_3_2_2_36_1","volume-title":"Yu Lei, and Bo Yang.","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 ICLR. Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. 2020. Geom-GCN: Geometric Graph Convolutional Networks. In ICLR."},{"key":"e_1_3_2_2_37_1","unstructured":"Yu Rong Wenbing Huang Tingyang Xu and Junzhou Huang. 2020. DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. In ICLR.  Yu Rong Wenbing Huang Tingyang Xu and Junzhou Huang. 2020. DropEdge: Towards Deep Graph Convolutional Networks on Node Classification. In ICLR."},{"key":"e_1_3_2_2_38_1","volume-title":"International conference on machine learning. PMLR, 9323--9332","author":"Satorras Victor Garcia","year":"2021","unstructured":"Victor Garcia Satorras , Emiel Hoogeboom , and Max Welling . 2021 . E (n) equivariant graph neural networks . In International conference on machine learning. PMLR, 9323--9332 . Victor Garcia Satorras, Emiel Hoogeboom, and Max Welling. 2021. E (n) equivariant graph neural networks. In International conference on machine learning. PMLR, 9323--9332."},{"key":"e_1_3_2_2_39_1","volume-title":"Nicola De Cao, and Ivan Titov","author":"Schlichtkrull Michael Sejr","year":"2020","unstructured":"Michael Sejr Schlichtkrull , Nicola De Cao, and Ivan Titov . 2020 . Interpreting graph neural networks for nlp with differentiable edge masking. arXiv preprint arXiv:2010.00577 (2020). Michael Sejr Schlichtkrull, Nicola De Cao, and Ivan Titov. 2020. Interpreting graph neural networks for nlp with differentiable edge masking. arXiv preprint arXiv:2010.00577 (2020)."},{"key":"e_1_3_2_2_40_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_41_1","volume-title":"Vertex-Frequency Analysis on Graphs. (7","author":"Shuman David I","year":"2013","unstructured":"David I Shuman , Benjamin Ricaud , and Pierre Vandergheynst . 2013. Vertex-Frequency Analysis on Graphs. (7 2013 ). http:\/\/arxiv.org\/abs\/1307.5708 David I Shuman, Benjamin Ricaud, and Pierre Vandergheynst. 2013. Vertex-Frequency Analysis on Graphs. (7 2013). http:\/\/arxiv.org\/abs\/1307.5708"},{"key":"e_1_3_2_2_42_1","volume-title":"International conference on machine learning. PMLR, 1139--1147","author":"Sutskever Ilya","year":"2013","unstructured":"Ilya Sutskever , James Martens , George Dahl , and Geoffrey Hinton . 2013 . On the importance of initialization and momentum in deep learning . In International conference on machine learning. PMLR, 1139--1147 . Ilya Sutskever, James Martens, George Dahl, and Geoffrey Hinton. 2013. On the importance of initialization and momentum in deep learning. In International conference on machine learning. PMLR, 1139--1147."},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/1557019.1557108"},{"key":"e_1_3_2_2_44_1","volume-title":"Graph attention networks. arXiv preprint arXiv:1710.10903","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. arXiv preprint arXiv:1710.10903 ( 2017 ). Petar Veli\u010dkovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2017. Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)."},{"key":"e_1_3_2_2_45_1","unstructured":"Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio Yoshua Bengio etal 2017. Graph attention networks. stat Vol. 1050 20 (2017) 10--48550.  Petar Velickovic Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio Yoshua Bengio et al. 2017. Graph attention networks. stat Vol. 1050 20 (2017) 10--48550."},{"key":"e_1_3_2_2_46_1","volume-title":"Improving graph attention networks with large margin-based constraints. arXiv preprint arXiv:1910.11945","author":"Wang Guangtao","year":"2019","unstructured":"Guangtao Wang , Rex Ying , Jing Huang , and Jure Leskovec . 2019. Improving graph attention networks with large margin-based constraints. arXiv preprint arXiv:1910.11945 ( 2019 ). Guangtao Wang, Rex Ying, Jing Huang, and Jure Leskovec. 2019. Improving graph attention networks with large margin-based constraints. arXiv preprint arXiv:1910.11945 (2019)."},{"key":"e_1_3_2_2_47_1","volume-title":"How Powerful are Spectral Graph Neural Networks. (5","author":"Wang Xiyuan","year":"2022","unstructured":"Xiyuan Wang and Muhan Zhang . 2022. How Powerful are Spectral Graph Neural Networks. (5 2022 ). http:\/\/arxiv.org\/abs\/2205.11172 Xiyuan Wang and Muhan Zhang. 2022. How Powerful are Spectral Graph Neural Networks. (5 2022). http:\/\/arxiv.org\/abs\/2205.11172"},{"volume-title":"The Free Encyclopedia","key":"e_1_3_2_2_48_1","unstructured":"Wikipedia. 2023 a. Clenshaw algorithm - Wikipedia , The Free Encyclopedia . http:\/\/en.wikipedia.org\/w\/index.php?title=Clenshaw%20algorithm&oldid=1089015914. [Online; accessed 03-February-2023]. Wikipedia. 2023 a. Clenshaw algorithm - Wikipedia, The Free Encyclopedia. http:\/\/en.wikipedia.org\/w\/index.php?title=Clenshaw%20algorithm&oldid=1089015914. [Online; accessed 03-February-2023]."},{"volume-title":"The Free Encyclopedia","key":"e_1_3_2_2_49_1","unstructured":"Wikipedia. 2023 b. Horner's method - Wikipedia , The Free Encyclopedia . http:\/\/en.wikipedia.org\/w\/index.php?title=Horner's%20method&oldid=1135871092. [Online; accessed 03-February-2023]. Wikipedia. 2023 b. Horner's method - Wikipedia, The Free Encyclopedia. http:\/\/en.wikipedia.org\/w\/index.php?title=Horner's%20method&oldid=1135871092. [Online; accessed 03-February-2023]."},{"key":"e_1_3_2_2_50_1","volume-title":"Graph neural networks for natural language processing: A survey. arXiv preprint arXiv:2106.06090","author":"Wu Lingfei","year":"2021","unstructured":"Lingfei Wu , Yu Chen , Kai Shen , Xiaojie Guo , Hanning Gao , Shucheng Li , Jian Pei , and Bo Long . 2021. Graph neural networks for natural language processing: A survey. arXiv preprint arXiv:2106.06090 ( 2021 ). Lingfei Wu, Yu Chen, Kai Shen, Xiaojie Guo, Hanning Gao, Shucheng Li, Jian Pei, and Bo Long. 2021. Graph neural networks for natural language processing: A survey. arXiv preprint arXiv:2106.06090 (2021)."},{"key":"e_1_3_2_2_51_1","volume-title":"Graph neural networks in recommender systems: a survey. ACM Computing Surveys (CSUR)","author":"Wu Shiwen","year":"2020","unstructured":"Shiwen Wu , Fei Sun , Wentao Zhang , Xu Xie , and Bin Cui . 2020. Graph neural networks in recommender systems: a survey. ACM Computing Surveys (CSUR) ( 2020 ). Shiwen Wu, Fei Sun, Wentao Zhang, Xu Xie, and Bin Cui. 2020. Graph neural networks in recommender systems: a survey. ACM Computing Surveys (CSUR) (2020)."},{"key":"e_1_3_2_2_52_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.  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_2_53_1","volume-title":"International conference on machine learning. PMLR, 40--48","author":"Yang Zhilin","year":"2016","unstructured":"Zhilin Yang , William Cohen , and Ruslan Salakhudinov . 2016 . Revisiting semi-supervised learning with graph embeddings . In International conference on machine learning. PMLR, 40--48 . Zhilin Yang, William Cohen, and Ruslan Salakhudinov. 2016. Revisiting semi-supervised learning with graph embeddings. In International conference on machine learning. PMLR, 40--48."},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.1145\/3534678.3539374"},{"key":"e_1_3_2_2_55_1","volume-title":"Graph Attention Multi-Layer Perceptron. CoRR","author":"Zhang Wentao","year":"2021","unstructured":"Wentao Zhang , Ziqi Yin , Zeang Sheng , Wen Ouyang , Xiaosen Li , Yangyu Tao , Zhi Yang , and Bin Cui . 2021. Graph Attention Multi-Layer Perceptron. CoRR , Vol. abs\/ 2108 .10097 ( 2021 ). showeprint[arXiv]2108.10097 https:\/\/arxiv.org\/abs\/2108.10097 Wentao Zhang, Ziqi Yin, Zeang Sheng, Wen Ouyang, Xiaosen Li, Yangyu Tao, Zhi Yang, and Bin Cui. 2021. Graph Attention Multi-Layer Perceptron. CoRR, Vol. abs\/2108.10097 (2021). showeprint[arXiv]2108.10097 https:\/\/arxiv.org\/abs\/2108.10097"},{"key":"e_1_3_2_2_56_1","first-page":"23321","article-title":"Adaptive Diffusion in Graph Neural Networks","volume":"34","author":"Zhao Jialin","year":"2021","unstructured":"Jialin Zhao , Yuxiao Dong , Ming Ding , Evgeny Kharlamov , and Jie Tang . 2021 . Adaptive Diffusion in Graph Neural Networks . Advances in Neural Information Processing Systems , Vol. 34 (2021), 23321 -- 23333 . Jialin Zhao, Yuxiao Dong, Ming Ding, Evgeny Kharlamov, and Jie Tang. 2021. Adaptive Diffusion in Graph Neural Networks. Advances in Neural Information Processing Systems, Vol. 34 (2021), 23321--23333.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_57_1","volume-title":"Graph neural networks for graphs with heterophily: A survey. arXiv preprint arXiv:2202.07082","author":"Zheng Xin","year":"2022","unstructured":"Xin Zheng , Yixin Liu , Shirui Pan , Miao Zhang , Di Jin , and Philip S Yu. 2022. Graph neural networks for graphs with heterophily: A survey. arXiv preprint arXiv:2202.07082 ( 2022 ). Xin Zheng, Yixin Liu, Shirui Pan, Miao Zhang, Di Jin, and Philip S Yu. 2022. Graph neural networks for graphs with heterophily: A survey. arXiv preprint arXiv:2202.07082 (2022)."},{"key":"e_1_3_2_2_58_1","first-page":"7793","article-title":"Beyond homophily in graph neural networks: Current limitations and effective designs","volume":"33","author":"Zhu Jiong","year":"2020","unstructured":"Jiong Zhu , Yujun Yan , Lingxiao Zhao , Mark Heimann , Leman Akoglu , and Danai Koutra . 2020 . Beyond homophily in graph neural networks: Current limitations and effective designs . Advances in Neural Information Processing Systems , Vol. 33 (2020), 7793 -- 7804 . Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. 2020. Beyond homophily in graph neural networks: Current limitations and effective designs. Advances in Neural Information Processing Systems, Vol. 33 (2020), 7793--7804.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_2_59_1","doi-asserted-by":"publisher","DOI":"10.1145\/3442381.3449953"}],"event":{"name":"KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"],"location":"Long Beach CA USA","acronym":"KDD '23"},"container-title":["Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599275","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3580305.3599275","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T17:51:15Z","timestamp":1750182675000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3580305.3599275"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,4]]},"references-count":57,"alternative-id":["10.1145\/3580305.3599275","10.1145\/3580305"],"URL":"https:\/\/doi.org\/10.1145\/3580305.3599275","relation":{},"subject":[],"published":{"date-parts":[[2023,8,4]]},"assertion":[{"value":"2023-08-04","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}