{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,12]],"date-time":"2026-07-12T03:38:24Z","timestamp":1783827504113,"version":"3.55.0"},"reference-count":88,"publisher":"Association for Computing Machinery (ACM)","issue":"2","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62506205"],"award-info":[{"award-number":["62506205"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"crossref","award":["2025T180426"],"award-info":[{"award-number":["2025T180426"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Postdoctoral Fellowship Program of CPSF","award":["GZB20250393"],"award-info":[{"award-number":["GZB20250393"]}]},{"name":"National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Xi\u2019an Jiaotong University","award":["HMHAI-202410"],"award-info":[{"award-number":["HMHAI-202410"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities of China","doi-asserted-by":"crossref","award":["PA2025IISL0114"],"award-info":[{"award-number":["PA2025IISL0114"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Inf. Syst."],"published-print":{"date-parts":[[2026,2,28]]},"abstract":"<jats:p>\n                    Graph Neural Networks (GNNs) have been widely used across various fields under the homophily assumption that connected nodes are similar. However, in heterophilic graphs, where connected nodes tend to have dissimilar features, existing GNNs still face some limitations. From the perspective of structure, shallow GNNs could not capture the high-order node information, whereas deep GNNs may suffer from the over-smoothing problem. From the perspective of feature, the useful information of high-order similar nodes is often weakened by low-order dissimilar nodes in the feature update phase. To address the above problems, we propose a Global Structure-aware and Feature-augmented Graph Neural Network (GSF-GNN) to alleviate the limitations from the perspectives of structure and feature. Specifically, from the structure perspective, we design a Structure-based Global Propagation (SGP) module to establish global connections among nodes and adaptively adjust edge weights for message propagation. From the feature perspective, we introduce a Feature-augmented Compensatory Update (FCU) module, which employs a multi-view feature updating mechanism to enhance node features from different perspectives. Our theoretical analysis formally demonstrates the effectiveness of GSF-GNN in heterophilic graphs. Experiments on heterophilic and homophilic benchmark datasets validate the effectiveness of GSF-GNN across various graph structures. Moreover, GSF-GNN achieves stable performance across multiple layers and effectively alleviates the over-smoothing problem. Our codes are available on\n                    <jats:ext-link xmlns:xlink=\"http:\/\/www.w3.org\/1999\/xlink\" ext-link-type=\"uri\" xlink:href=\"https:\/\/github.com\/huijieliu2023\/GSF-GNN\">https:\/\/github.com\/huijieliu2023\/GSF-GNN<\/jats:ext-link>\n                    .\n                  <\/jats:p>","DOI":"10.1145\/3775057","type":"journal-article","created":{"date-parts":[[2025,11,11]],"date-time":"2025-11-11T14:45:36Z","timestamp":1762872336000},"page":"1-28","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["Global Structure-aware and Feature-augmented Graph Neural Network for Heterophilic Graphs"],"prefix":"10.1145","volume":"44","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6090-9895","authenticated-orcid":false,"given":"Huijie","family":"Liu","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7466-8909","authenticated-orcid":false,"given":"Shulan","family":"Ruan","sequence":"additional","affiliation":[{"name":"Shenzhen International Graduate School, Tsinghua University, Shenzhen, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6956-5550","authenticated-orcid":false,"given":"Qi","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Data Science, University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9873-7681","authenticated-orcid":false,"given":"Mingyue","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1661-0420","authenticated-orcid":false,"given":"Zhenya","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5216-3181","authenticated-orcid":false,"given":"Yu","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4835-4102","authenticated-orcid":false,"given":"Enhong","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, University of Science and Technology of China, Hefei, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6111-340X","authenticated-orcid":false,"given":"You","family":"He","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, Tsinghua University, Beijing, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,12,23]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1145\/3694784"},{"key":"e_1_3_3_3_2","first-page":"1341","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Azabou Mehdi","year":"2023","unstructured":"Mehdi Azabou, Venkataramana Ganesh, Shantanu Thakoor, Chi-Heng Lin, Lakshmi Sathidevi, Ran Liu, Michal Valko, Petar Veli\u010dkovi\u0107, and Eva L. Dyer. 2023. Half-hop: A graph upsampling approach for slowing down message passing. In Proceedings of the International Conference on Machine Learning. PMLR, 1341\u20131360."},{"key":"e_1_3_3_4_2","first-page":"599","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Balcilar Muhammet","year":"2021","unstructured":"Muhammet Balcilar, Pierre H\u00e9roux, Benoit Gauzere, Pascal Vasseur, S\u00e9bastien Adam, and Paul Honeine. 2021. Breaking the limits of message passing graph neural networks. In Proceedings of the International Conference on Machine Learning. PMLR, 599\u2013608."},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i5.16514"},{"key":"e_1_3_3_6_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Brody Shaked","year":"2022","unstructured":"Shaked Brody, Uri Alon, and Eran Yahav. 2022. How attentive are graph attention networks. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_3_7_2","volume-title":"Proceedings of the 2nd International Conference on Learning Representations (ICLR \u201914)","author":"Bruna Joan","year":"2014","unstructured":"Joan Bruna, Wojciech Zaremba, Arthur Szlam, and Yann LeCun. 2014. Spectral networks and deep locally connected networks on graphs. In Proceedings of the 2nd International Conference on Learning Representations (ICLR \u201914)."},{"key":"e_1_3_3_8_2","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Cai Chen","year":"2023","unstructured":"Chen Cai, Truong Son Hy, Rose Yu, and Yusu Wang. 2023. On the connection between MPNN and graph transformer. In Proceedings of the International Conference on Machine Learning. PMLR."},{"key":"e_1_3_3_9_2","volume-title":"Proceedings of the 11th International Conference on Learning Representations","author":"Chen Jinsong","year":"2023","unstructured":"Jinsong Chen, Kaiyuan Gao, Gaichao Li, and Kun He. 2023. NAGphormer: A tokenized graph transformer for node classification in large graphs. In Proceedings of the 11th International Conference on Learning Representations."},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2021.3064092"},{"key":"e_1_3_3_11_2","first-page":"1725","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Chen Ming","year":"2020","unstructured":"Ming Chen, Zhewei Wei, Zengfeng Huang, Bolin Ding, and Yaliang Li. 2020. Simple and deep graph convolutional networks. In Proceedings of the International Conference on Machine Learning. PMLR, 1725\u20131735."},{"key":"e_1_3_3_12_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Chien Eli","year":"2020","unstructured":"Eli Chien, Jianhao Peng, Pan Li, and Olgica Milenkovic. 2020. Adaptive universal generalized pagerank graph neural network. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_3_13_2","first-page":"1","article-title":"Convolutional neural networks on graphs with fast localized spectral filtering","volume":"29","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. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 29, 1\u20139.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1145\/3485447.3512201"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.52202\/079017-2966"},{"key":"e_1_3_3_16_2","first-page":"9224","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Eliasof Moshe","year":"2023","unstructured":"Moshe Eliasof, Lars Ruthotto, and Eran Treister. 2023. Improving graph neural networks with learnable propagation operators. In Proceedings of the International Conference on Machine Learning. PMLR, 9224\u20139245."},{"key":"e_1_3_3_17_2","doi-asserted-by":"publisher","DOI":"10.1145\/3649311"},{"key":"e_1_3_3_18_2","volume-title":"Proceedings of the 12th International Conference on Learning Representations","author":"Fu Dongqi","year":"2024","unstructured":"Dongqi Fu, Zhigang Hua, Yan Xie, Jin Fang, Si Zhang, Kaan Sancak, Hao Wu, Andrey Malevich, Jingrui He, and Bo Long. 2024. VCR-Graphormer: A mini-batch graph transformer via virtual connections. In Proceedings of the 12th International Conference on Learning Representations."},{"key":"e_1_3_3_19_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Gasteiger Johannes","year":"2018","unstructured":"Johannes Gasteiger, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2018. Predict then propagate: Graph neural networks meet personalized PageRank. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_3_20_2","first-page":"1263","volume-title":"Proceedings of the 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 Proceedings of the International Conference on Machine Learning. PMLR, 1263\u20131272."},{"key":"e_1_3_3_21_2","first-page":"1","article-title":"Inductive representation learning on large graphs","volume":"30","author":"Hamilton Will","year":"2017","unstructured":"Will Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 30, 1\u201311.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2016.90"},{"key":"e_1_3_3_23_2","first-page":"1","article-title":"OGB-LSC: A large-scale challenge for machine learning on graphs","volume":"34","author":"Hu Weihua","year":"2021","unstructured":"Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, and Jure Leskovec. 2021. OGB-LSC: A large-scale challenge for machine learning on graphs. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 34, 1\u201315.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_24_2","first-page":"22118","article-title":"Open graph benchmark: Datasets for machine learning on graphs","volume":"33","author":"Hu Weihua","year":"2020","unstructured":"Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. 2020. Open graph benchmark: Datasets for machine learning on graphs. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 33, 22118\u201322133.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_25_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2024.3396413"},{"key":"e_1_3_3_26_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.neunet.2024.106207"},{"key":"e_1_3_3_27_2","volume-title":"Proceedings of the 11th International Conference on Learning Representations","author":"Karhadkar Kedar","year":"2023","unstructured":"Kedar Karhadkar, Pradeep Kr Banerjee, and Guido Montufar. 2023. FoSR: First-order spectral rewiring for addressing oversquashing in GNNs. In Proceedings of the 11th International Conference on Learning Representations."},{"key":"e_1_3_3_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3633518"},{"key":"e_1_3_3_29_2","first-page":"500","volume-title":"Proceedings of the 3rd International Conference for Learning Representations","author":"Kingma D.","year":"2015","unstructured":"D. Kingma and J. Ba. 2015. Adam: A method for stochastic optimization. In Proceedings of the 3rd International Conference for Learning Representations, 500."},{"key":"e_1_3_3_30_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Kipf Thomas N.","year":"2016","unstructured":"Thomas N. Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_3_31_2","unstructured":"T. Konstantin Rusch Michael M. Bronstein and Siddhartha Mishra. 2023. A survey on oversmoothing in graph neural networks. arXiv:2303.10993. Retrieved from https:\/\/arxiv.org\/abs\/2303.10993"},{"key":"e_1_3_3_32_2","doi-asserted-by":"publisher","DOI":"10.1145\/3717830"},{"key":"e_1_3_3_33_2","doi-asserted-by":"publisher","DOI":"10.1145\/3727882"},{"key":"e_1_3_3_34_2","first-page":"13242","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Li Xiang","year":"2022","unstructured":"Xiang Li, Renyu Zhu, Yao Cheng, Caihua Shan, Siqiang Luo, Dongsheng Li, Weining Qian. 2022. Finding global homophily in graph neural networks when meeting heterophily. In Proceedings of the International Conference on Machine Learning. PMLR, 13242\u201313256."},{"key":"e_1_3_3_35_2","first-page":"10909","article-title":"Predicting global label relationship matrix for graph neural networks under heterophily","volume":"36","author":"Liang Langzhang","year":"2023","unstructured":"Langzhang Liang, Xiangjing Hu, Zenglin Xu, Zixing Song, and Irwin King. 2023. Predicting global label relationship matrix for graph neural networks under heterophily. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 36, 10909\u201310921.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_36_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBDATA.2025.3527216"},{"key":"e_1_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-022-01697-2"},{"key":"e_1_3_3_38_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM51629.2021.00050"},{"key":"e_1_3_3_39_2","first-page":"1","article-title":"MuSe-GNN: Learning unified gene representation from multimodal biological graph data","volume":"36","author":"Liu Tianyu","year":"2024","unstructured":"Tianyu Liu, Yuge Wang, Rex Ying, and Hongyu Zhao. 2024. MuSe-GNN: Learning unified gene representation from multimodal biological graph data. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 36, 1\u201317.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_40_2","doi-asserted-by":"publisher","DOI":"10.1145\/3690624.3709302"},{"key":"e_1_3_3_41_2","unstructured":"Sitao Luan Chenqing Hua Qincheng Lu Liheng Ma Lirong Wu Xinyu Wang Minkai Xu Xiao-Wen Chang Doina Precup Rex Ying et al. 2024. The heterophilic graph learning handbook: Benchmarks models theoretical analysis applications and challenges. arXiv:2407.09618. Retrieved from https:\/\/arxiv.org\/abs\/2407.09618"},{"key":"e_1_3_3_42_2","first-page":"1362","article-title":"Revisiting heterophily for graph neural networks","volume":"35","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. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 35, 1362\u20131375.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_43_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jocs.2022.101695"},{"key":"e_1_3_3_44_2","first-page":"1","article-title":"Attending to graph transformers","author":"M\u00fcller Luis","year":"2024","unstructured":"Luis M\u00fcller, Mikhail Galkin, Christopher Morris, and Ladislav Ramp\u00e1\u0161ek. 2024. Attending to graph transformers. Transactions on Machine Learning Research (2024), 1\u201326.","journal-title":"Transactions on Machine Learning Research"},{"key":"e_1_3_3_45_2","doi-asserted-by":"publisher","DOI":"10.1145\/3690624.3709270"},{"key":"e_1_3_3_46_2","first-page":"1","volume-title":"Proceedings of the 10th International Workshop on Mining and Learning with Graphs","volume":"8","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 Proceedings of the 10th International Workshop on Mining and Learning with Graphs, Vol. 8, 1."},{"key":"e_1_3_3_47_2","first-page":"1","article-title":"PyTorch: An imperative style, high-performance deep learning library","volume":"32","author":"Paszke Adam","year":"2019","unstructured":"Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019. PyTorch: An imperative style, high-performance deep learning library. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 32, 1\u201312.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_48_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Pei Hongbin","year":"2019","unstructured":"Hongbin Pei, Bingzhe Wei, Kevin Chen-Chuan Chang, Yu Lei, and Bo Yang. 2019. Geom-GCN: Geometric graph convolutional networks. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_3_49_2","doi-asserted-by":"publisher","DOI":"10.1145\/3632751"},{"key":"e_1_3_3_50_2","unstructured":"Trang Pham Truyen Tran Hoa Dam and Svetha Venkatesh. 2017. Graph classification via deep learning with virtual nodes. arXiv:1708.04357. Retrieved from https:\/\/arxiv.org\/abs\/1708.04357"},{"key":"e_1_3_3_51_2","first-page":"1","article-title":"Characterizing graph datasets for node classification: Homophily-heterophily dichotomy and beyond","volume":"36","author":"Platonov Oleg","year":"2024","unstructured":"Oleg Platonov, Denis Kuznedelev, Artem Babenko, and Liudmila Prokhorenkova. 2024. Characterizing graph datasets for node classification: Homophily-heterophily dichotomy and beyond. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 36, 1\u201326.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_52_2","volume-title":"Proceedings of the 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 the evaluation of GNNs under heterophily: Are we really making progress? In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_3_53_2","first-page":"14501","article-title":"Recipe for a general, powerful, scalable graph transformer","volume":"35","author":"Ramp\u00e1\u0161ek Ladislav","year":"2022","unstructured":"Ladislav Ramp\u00e1\u0161ek, Michael Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini. 2022. Recipe for a general, powerful, scalable graph transformer. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 35, 14501\u201314515.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_54_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Rong Yu","year":"2019","unstructured":"Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. 2019. DropEdge: Towards deep graph convolutional networks on node classification. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_3_55_2","doi-asserted-by":"publisher","DOI":"10.1145\/3725730"},{"key":"e_1_3_3_56_2","unstructured":"Shulan Ruan Rongwei Wang Xuchen Shen Huijie Liu Baihui Xiao Jun Shi Kun Zhang Zhenya Huang Yu Liu Enhong Chen et al. 2025. A survey of multi-sensor fusion perception for embodied AI: Background methods challenges and prospects. arXiv:2506.19769. Retrieved from https:\/\/arxiv.org\/abs\/2506.19769"},{"key":"e_1_3_3_57_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2021\/214"},{"key":"e_1_3_3_58_2","volume-title":"Proceedings of the 11th International Conference on Learning Representations","author":"Song Yunchong","year":"2023","unstructured":"Yunchong Song, Chenghu Zhou, Xinbing Wang, and Zhouhan Lin. 2023. Ordered GNN: Ordering message passing to deal with heterophily and over-smoothing. In Proceedings of the 11th International Conference on Learning Representations."},{"key":"e_1_3_3_59_2","doi-asserted-by":"publisher","DOI":"10.1109\/81.273922"},{"key":"e_1_3_3_60_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467373"},{"issue":"11","key":"e_1_3_3_61_2","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten Laurens","year":"2008","unstructured":"Laurens Van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of Machine Learning Research 9, 11 (2008), 2579\u20132605.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_3_62_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph attention networks. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_3_63_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2024.110738"},{"key":"e_1_3_3_64_2","unstructured":"Minjie Wang Da Zheng Zihao Ye Quan Gan Mufei Li Xiang Song Jinjing Zhou Chao Ma Lingfan Yu Yu Gai et al. 2019. Deep graph library: A graph-centric highly-performant package for graph neural networks. arXiv:1909.01315. Retrieved from https:\/\/arxiv.org\/abs\/1909.01315"},{"key":"e_1_3_3_65_2","first-page":"23341","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Wang Xiyuan","year":"2022","unstructured":"Xiyuan Wang and Muhan Zhang. 2022. How powerful are spectral graph neural networks. In Proceedings of the International Conference on Machine Learning. PMLR, 23341\u201323362."},{"key":"e_1_3_3_66_2","doi-asserted-by":"publisher","DOI":"10.1145\/3459637.3482487"},{"key":"e_1_3_3_67_2","doi-asserted-by":"publisher","DOI":"10.1145\/3535101"},{"key":"e_1_3_3_68_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_3_69_2","volume-title":"Proceedings of the 41st International Conference on Machine Learning","author":"Xing Yujie","year":"2024","unstructured":"Yujie Xing, Xiao Wang, Yibo Li, Hai Huang, and Chuan Shi. 2024. Less is more: On the over-globalizing problem in graph transformers. In Proceedings of the 41st International Conference on Machine Learning."},{"key":"e_1_3_3_70_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Xu Keyulu","year":"2019","unstructured":"Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2019. How powerful are graph neural networks? In Proceedings of the International Conference on Learning Representations. Retrieved from https:\/\/openreview.net\/forum?id=ryGs6iA5Km"},{"key":"e_1_3_3_71_2","first-page":"5453","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Xu Keyulu","year":"2018","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 Proceedings of the International Conference on Machine Learning. PMLR, 5453\u20135462."},{"key":"e_1_3_3_72_2","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599446"},{"key":"e_1_3_3_73_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i20.35496"},{"key":"e_1_3_3_74_2","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM54844.2022.00169"},{"key":"e_1_3_3_75_2","first-page":"4751","article-title":"Diverse message passing for attribute with heterophily","volume":"34","author":"Yang Liang","year":"2021","unstructured":"Liang Yang, Mengzhe Li, Liyang Liu, Chuan Wang, Xiaochun Cao, and Yuanfang Guo. 2021. Diverse message passing for attribute with heterophily. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 34, 4751\u20134763.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_76_2","doi-asserted-by":"publisher","DOI":"10.1145\/3627673.3679991"},{"key":"e_1_3_3_77_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i8.28778"},{"key":"e_1_3_3_78_2","doi-asserted-by":"publisher","DOI":"10.1145\/3729224"},{"key":"e_1_3_3_79_2","first-page":"2515","volume-title":"Proceedings of the 33rd International Joint Conference on Artificial Intelligence","author":"Yu Zhizhi","year":"2024","unstructured":"Zhizhi Yu, Bin Feng, Dongxiao He, Zizhen Wang, Yuxiao Huang, and Zhiyong Feng. 2024. LG-GNN: Local-global adaptive graph neural network for modeling both homophily and heterophily. In Proceedings of the 33rd International Joint Conference on Artificial Intelligence, 2515\u20132523."},{"issue":"2","key":"e_1_3_3_80_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3466641","article-title":"Multi-graph heterogeneous interaction fusion for social recommendation","volume":"40","author":"Zhang Chengyuan","year":"2021","unstructured":"Chengyuan Zhang, Yang Wang, Lei Zhu, Jiayu Song, and Hongzhi Yin. 2021. Multi-graph heterogeneous interaction fusion for social recommendation. ACM Transactions on Information Systems 40, 2 (2021), 1\u201326.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_3_81_2","volume-title":"Proceedings of the Tiny Papers@ ICLR","author":"Zhang Sisi","year":"2023","unstructured":"Sisi Zhang, Lun Du, Fan Li, Ge Yu, and Mengyuan Chen. 2023. Propagate deeper and adaptive graph convolutional networks. In Proceedings of the Tiny Papers@ ICLR."},{"key":"e_1_3_3_82_2","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btae164"},{"key":"e_1_3_3_83_2","doi-asserted-by":"publisher","DOI":"10.1145\/3573385"},{"key":"e_1_3_3_84_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Zhao Lingxiao","year":"2019","unstructured":"Lingxiao Zhao and Leman Akoglu. 2019. PairNorm: Tackling oversmoothing in GNNs. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_3_85_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v39i12.33461"},{"key":"e_1_3_3_86_2","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:2202.07082. Retrieved from https:\/\/arxiv.org\/abs\/2202.07082"},{"key":"e_1_3_3_87_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.aiopen.2021.01.001"},{"key":"e_1_3_3_88_2","first-page":"10","article-title":"Heterophily and graph neural networks: Past, present and future","author":"Zhu Jiong","year":"2023","unstructured":"Jiong Zhu, Yujun Yan, Mark Heimann, Lingxiao Zhao, Leman Akoglu, and Danai Koutra. 2023. Heterophily and graph neural networks: Past, present and future. IEEE Data Engineering Bulletin (2023), 10\u201332.","journal-title":"IEEE Data Engineering Bulletin"},{"key":"e_1_3_3_89_2","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. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 33, 7793\u20137804.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"}],"container-title":["ACM Transactions on Information Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3775057","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,23]],"date-time":"2025-12-23T14:07:27Z","timestamp":1766498847000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3775057"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,23]]},"references-count":88,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2026,2,28]]}},"alternative-id":["10.1145\/3775057"],"URL":"https:\/\/doi.org\/10.1145\/3775057","relation":{},"ISSN":["1046-8188","1558-2868"],"issn-type":[{"value":"1046-8188","type":"print"},{"value":"1558-2868","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,12,23]]},"assertion":[{"value":"2025-04-15","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-10-30","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-12-23","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}