{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T08:03:08Z","timestamp":1763798588641,"version":"3.45.0"},"reference-count":96,"publisher":"Association for Computing Machinery (ACM)","issue":"1","funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"crossref","award":["62525209, 62020106005, T2421002, and 623B2071"],"award-info":[{"award-number":["62525209, 62020106005, T2421002, and 623B2071"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Shanghai Pilot Program for Basic Research\u2014Shanghai Jiao Tong University"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":["ACM Trans. Knowl. Discov. Data"],"published-print":{"date-parts":[[2026,1,31]]},"abstract":"<jats:p>\n                    Out-of-Distribution (OOD) generalization is a promising yet challenging goal that guarantees the test performance of Graph Neural Networks (GNNs) in open-world settings. However, due to the intricate internal topology of graph-structured data, redundant information from the spurious topologies severely confuses GNNs to deviate from the labels. Extracting concise and label-relevant subgraphs from the original graphs can alleviate this problem. Unfortunately, existing methods either overlook the global structural distribution or rely heavily on manually predefined assumptions. As a result, they fall short of well capturing the structural distribution changes between input graph and extracted subgraph, thus compromising adaptability of extracted invariant subgraphs to diverse OOD scenarios. This motivates us to propose a framework called\n                    <jats:italic toggle=\"yes\">S<\/jats:italic>\n                    tructural-\n                    <jats:italic toggle=\"yes\">E<\/jats:italic>\n                    ntropy-guided\n                    <jats:italic toggle=\"yes\">I<\/jats:italic>\n                    nformation\n                    <jats:italic toggle=\"yes\">B<\/jats:italic>\n                    ottleneck (OOD-SEIB) that aims to more traceably measure the inherent information changes for better and more flexible OOD generalization. The core of OOD-SEIB lies in concise topology extraction module, where we measure the mutual information flow between input graph and extracted subgraph based on structural entropy, termed Compression Index (CI). Specifically, the CI is a quantifiable metric that calculates the codeword length required to describe entire graph structure via a biased random walk. Under this guidance, OOD-SEIB then launches a structural information bottleneck compression module that jointly optimizes both CI and label-relevance of the subgraph topology by iteratively balancing between informativeness and compression. To further improve GNN\u2019s invariant subgraph identification capability, OOD-SEIB generates multiple augmented environments and distill the invariant subgraphs into GNN as knowledge in an inside-out manner. When iteratively optimizing in above prescribed way, OOD-SEIB progressively reinforce the invariant subgraph extraction, thereby enhancing its generalization capability. Extensive experiments on synthetic and three real-world graph-level OOD benchmarks demonstrate that our proposed OOD-SEIB improves classification accuracy by 4.85%\u201338.03% on average compared to state-of-the-art baselines. Additionally, we extend OOD-SEIB to two node-level benchmarks, achieving average classification accuracy improvements of 14.52% and 13.15%.\n                  <\/jats:p>","DOI":"10.1145\/3767162","type":"journal-article","created":{"date-parts":[[2025,10,3]],"date-time":"2025-10-03T16:44:44Z","timestamp":1759509884000},"page":"1-34","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Graph Out-of-Distribution Generalization Based on Structural-Entropy-Guided Information Bottleneck"],"prefix":"10.1145","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-4214-3712","authenticated-orcid":false,"given":"Zijun","family":"Di","sequence":"first","affiliation":[{"name":"School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, School of Oceanography, Shanghai Jiao Tong University, Shanghai, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, and Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1994-7195","authenticated-orcid":false,"given":"Peng","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, School of Oceanography, Shanghai Jiao Tong University, Shanghai, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, and Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6452-7029","authenticated-orcid":false,"given":"Bin","family":"Lu","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, School of Oceanography, Shanghai Jiao Tong University, Shanghai, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, and Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-3339-1427","authenticated-orcid":false,"given":"Kai","family":"Guan","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, School of Oceanography, Shanghai Jiao Tong University, Shanghai, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, and Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7796-9168","authenticated-orcid":false,"given":"Luoyi","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, School of Oceanography, Shanghai Jiao Tong University, Shanghai, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, and Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-4341-2929","authenticated-orcid":false,"given":"Ningdi","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, School of Oceanography, Shanghai Jiao Tong University, Shanghai, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, and Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-0688-8825","authenticated-orcid":false,"given":"Ye","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, School of Oceanography, Shanghai Jiao Tong University, Shanghai, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, and Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5200-1409","authenticated-orcid":false,"given":"Xiaoying","family":"Gan","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, School of Oceanography, Shanghai Jiao Tong University, Shanghai, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, and Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0433-3991","authenticated-orcid":false,"given":"Lei","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, School of Oceanography, Shanghai Jiao Tong University, Shanghai, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, and Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0357-8356","authenticated-orcid":false,"given":"Xinbing","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, School of Oceanography, Shanghai Jiao Tong University, Shanghai, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, and Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3331-2302","authenticated-orcid":false,"given":"Chenghu","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Computer Science, Shanghai Jiao Tong University, Shanghai, China, ZTE Corporation, Shenzhen, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, School of Oceanography, Shanghai Jiao Tong University, Shanghai, China, School of Integrated Circuits (School of Information Science and Electronic Engineering), Shanghai Jiao Tong University, Shanghai, China, and Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2025,11,21]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"Xinbing Wang Luoyi Fu Xiaoying Gan Ying Wen Guanjie Zheng Jiaxin Ding Liyao Xiang Nanyang Ye Meng Jin Shiyu Liang et al. 2024. AceMap: Knowledge discovery through academic graph. arXiv:2403.02576. Retrieved from https:\/\/arxiv.org\/abs\/2403.02576"},{"issue":"3","key":"e_1_3_1_3_2","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1038\/s42256-022-00447-x","article-title":"Molecular contrastive learning of representations via graph neural networks","volume":"4","author":"Wang Yuyang","year":"2022","unstructured":"Yuyang Wang, Jianren Wang, Zhonglin Cao, and Amir Barati Farimani. 2022. Molecular contrastive learning of representations via graph neural networks. Nature Machine Intelligence 4, 3 (2022), 279\u2013287.","journal-title":"Nature Machine Intelligence"},{"issue":"7873","key":"e_1_3_1_4_2","doi-asserted-by":"crossref","first-page":"583","DOI":"10.1038\/s41586-021-03819-2","article-title":"Highly accurate protein structure prediction with AlphaFold","volume":"596","author":"Jumper John","year":"2021","unstructured":"John Jumper, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, Russ Bates, Augustin \u017d\u00eddek, Anna Potapenko, et al. 2021. Highly accurate protein structure prediction with AlphaFold. Nature 596, 7873 (2021), 583\u2013589.","journal-title":"Nature"},{"issue":"6","key":"e_1_3_1_5_2","doi-asserted-by":"crossref","first-page":"8811","DOI":"10.1007\/s11042-021-11857-1","article-title":"The homophily principle in social network analysis: A survey","volume":"82","author":"Khanam Kazi Zainab","year":"2023","unstructured":"Kazi Zainab Khanam, Gautam Srivastava, and Vijay Mago. 2023. The homophily principle in social network analysis: A survey. Multimedia Tools and Applications 82, 6 (2023), 8811\u20138854.","journal-title":"Multimedia Tools and Applications"},{"issue":"1","key":"e_1_3_1_6_2","doi-asserted-by":"crossref","first-page":"17618","DOI":"10.1038\/s41598-021-97100-1","article-title":"Structure and dynamics of financial networks by feature ranking method","volume":"11","author":"Rakib Mahmudul Islam","year":"2021","unstructured":"Mahmudul Islam Rakib, Ashadun Nobi, and Jae Woo Lee. 2021. Structure and dynamics of financial networks by feature ranking method. Scientific Reports 11, 1 (2021), 17618.","journal-title":"Scientific Reports"},{"key":"e_1_3_1_7_2","doi-asserted-by":"crossref","unstructured":"Yongduo Sui Shuyao Wang Jie Sun Zhiyuan Liu Qing Cui Longfei Li Jun Zhou Xiang Wang and Xiangnan He. 2024. A simple data augmentation for graph classification: A perspective of equivariance and invariance. ACM Transactions on Knowledge Discovery from Data 19 2 Article 51 (November 2024) 1\u201324.","DOI":"10.1145\/3706062"},{"key":"e_1_3_1_8_2","first-page":"406","volume-title":"Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD \u201919)","author":"Wu Jun","year":"2019","unstructured":"Jun Wu, Jingrui He, and Jiejun Xu. 2019. DEMO-Net: Degree-specific graph neural networks for node and graph classification. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD \u201919). ACM, New York, NY, 406\u2013415."},{"issue":"9","key":"e_1_3_1_9_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3687468","article-title":"SPORT: A subgraph perspective on graph classification with label noise","volume":"18","author":"Yin Nan","year":"2024","unstructured":"Nan Yin, Li Shen, Chong Chen, Xian-Sheng Hua, and Xiao Luo. 2024. SPORT: A subgraph perspective on graph classification with label noise. ACM Transactions on Knowledge Discovery from Data 18, 9, Article 230 (November 2024), 1\u201320.","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"e_1_3_1_10_2","doi-asserted-by":"crossref","unstructured":"Zhengyang Mao Wei Ju Siyu Yi Yifan Wang Zhiping Xiao Qingqing Long Nan Yin Xinwang Liu and Ming Zhang. 2024. Learning knowledge-diverse experts for long-tailed graph classification. ACM Transactions on Knowledge Discovery from Data 19 11 Article 32 (November 2024) 1\u201324.","DOI":"10.1145\/3705323"},{"issue":"4","key":"e_1_3_1_11_2","first-page":"1","article-title":"Hi-PART: Going beyond graph pooling with hierarchical partition tree for graph-level representation learning","volume":"18","author":"Ren Yuyang","year":"2024","unstructured":"Yuyang Ren, Haonan Zhang, Luoyi Fu, Shiyu Liang, Lei Zhou, Xinbing Wang, Xinde Cao, Fei Long, and Chenghu Zhou. 2024. Hi-PART: Going beyond graph pooling with hierarchical partition tree for graph-level representation learning. ACM Transactions on Knowledge Discovery from Data 18, 4, Article 94 (February 2024), 1\u201320.","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"e_1_3_1_12_2","volume-title":"Proceedings of the 5th International Conference on Learning Representations (ICLR \u201917) Conference Track Proceedings","author":"Kipf Thomas N.","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of the 5th International Conference on Learning Representations (ICLR \u201917), Conference Track Proceedings."},{"key":"e_1_3_1_13_2","first-page":"1024","volume-title":"Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems (NeurIPS\u201917)","author":"Hamilton William L.","year":"2017","unstructured":"William L. Hamilton, Zhitao Ying, and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems (NeurIPS\u201917), 1024\u20131034."},{"key":"e_1_3_1_14_2","volume-title":"Proceedings of the 6th International Conference on Learning Representations (ICLR \u201918), Conference Track Proceedings","author":"Velickovic Petar","year":"2018","unstructured":"Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph attention networks. In Proceedings of the 6th International Conference on Learning Representations (ICLR \u201918), Conference Track Proceedings."},{"key":"e_1_3_1_15_2","volume-title":"Proceedings of the 7th International Conference on Learning Representations (ICLR\u201919)","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 7th International Conference on Learning Representations (ICLR\u201919)."},{"issue":"3","key":"e_1_3_1_16_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3635473","article-title":"Learning hierarchical task structures for few-shot graph classification","volume":"18","author":"Wang Song","year":"2024","unstructured":"Song Wang, Yushun Dong, Xiao Huang, Chen, Chen, and Jundong Li. 2024. Learning hierarchical task structures for few-shot graph classification. ACM Transactions on Knowledge Discovery from Data 18, 3, Article 67 (January 2024), 1\u201320.","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"issue":"5","key":"e_1_3_1_17_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3568165","article-title":"Ada-MIP: Adaptive self-supervised graph representation learning via mutual information and proximity optimization","volume":"17","author":"Ren Yuyang","year":"2023","unstructured":"Yuyang Ren, Haonan Zhang, Peng Yu, Luoyi Fu, Xinde Cao, Xinbing Wang, Guihai Chen, Fei Long, and Chenghu Zhou. 2023. Ada-MIP: Adaptive self-supervised graph representation learning via mutual information and proximity optimization. ACM Transactions on Knowledge Discovery from Data 17, 5, Article 69 (April 2023), 1\u201323.","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"issue":"1","key":"e_1_3_1_18_2","first-page":"1","article-title":"Neural architecture search for GNN-based graph classification","volume":"42","author":"Wei Lanning","year":"2023","unstructured":"Lanning Wei, Huan Zhao, Zhiqiang He, and Quanming Yao. 2023. Neural architecture search for GNN-based graph classification. ACM Transactions on Information Systems 42, 1, Article 1 (August 2023), 1\u201329.","journal-title":"ACM Transactions on Information Systems"},{"key":"e_1_3_1_19_2","first-page":"2067","volume-title":"Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM \u201922)","author":"Wang Yu","year":"2022","unstructured":"Yu Wang, Yuying Zhao, Neil Shah, and Tyler Derr. 2022. Imbalanced graph classification via graph-of-graph neural networks. In Proceedings of the 31st ACM International Conference on Information and Knowledge Management (CIKM \u201922). ACM, 2067\u20132076."},{"key":"e_1_3_1_20_2","first-page":"11828","article-title":"Learning invariant graph representations for out-of-distribution generalization","volume":"35","author":"Li Haoyang","year":"2022","unstructured":"Haoyang Li, Ziwei Zhang, Xin Wang, and Wenwu Zhu. 2022. Learning invariant graph representations for out-of-distribution generalization. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 35, 11828\u201311841.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_21_2","first-page":"22131","article-title":"Learning causally invariant representations for out-of-distribution generalization on graphs","volume":"35","author":"Chen Yongqiang","year":"2022","unstructured":"Yongqiang Chen, Yonggang Zhang, Yatao Bian, Han Yang, M. A. Kaili, Binghui Xie, Tongliang Liu, Bo Han, and James Cheng. 2022. Learning causally invariant representations for out-of-distribution generalization on graphs. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 35, 22131\u201322148.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"issue":"7","key":"e_1_3_1_22_2","first-page":"7328","article-title":"OOD-GNN: Out-of-distribution generalized graph neural network","volume":"35","author":"Li Haoyang","year":"2022","unstructured":"Haoyang Li, Xin Wang, Ziwei Zhang, and Wenwu Zhu. 2022. OOD-GNN: Out-of-distribution generalized graph neural network. IEEE Transactions on Knowledge and Data Engineering 35, 7 (2022), 7328\u20137340.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"issue":"11","key":"e_1_3_1_23_2","article-title":"Graph out-of-distribution generalization with controllable data augmentation","volume":"36","author":"Lu Bin","year":"2024","unstructured":"Bin Lu, Ze Zhao, Xiaoying Gan, Shiyu Liang, Luoyi Fu, Xinbing Wang, and Chenghu Zhou. 2024. Graph out-of-distribution generalization with controllable data augmentation. IEEE Transactions on Knowledge and Data Engineering 36, 11 (2024), 6317\u20136329.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_24_2","first-page":"4204","volume-title":"Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems (NeurIPS)","author":"Knyazev Mohamed R. Amer Boris","year":"2019","unstructured":"Mohamed R. Amer Boris Knyazev and Graham W. Taylor. 2019. Understanding attention and generalization in graph neural networks. In Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems (NeurIPS), 4204\u20134214."},{"key":"e_1_3_1_25_2","volume-title":"Proceedings of the 12th International Conference on Learning Representations (ICLR \u201924)","author":"Lu Bin","year":"2024","unstructured":"Bin Lu, Tingyan Ma, Xiaoying Gan, Xinbing Wang, Yunqiang Zhu, Chenghu Zhou, and Shiyu Liang. 2024. Temporal generalization estimation in evolving graphs. In Proceedings of the 12th International Conference on Learning Representations (ICLR \u201924)."},{"key":"e_1_3_1_26_2","volume-title":"Proceedings of the 37th Conference on Neural Information Processing Systems","author":"Yuan Haonan","year":"2023","unstructured":"Haonan Yuan, Qingyun Sun, Xingcheng Fu, Ziwei Zhang, Cheng Ji, Hao Peng, and Jianxin Li. 2023. Environment-aware dynamic graph learning for out-of-distribution generalization. In Proceedings of the 37th Conference on Neural Information Processing Systems."},{"key":"e_1_3_1_27_2","doi-asserted-by":"crossref","unstructured":"Wei Ju Siyu Yi Yifan Wang Zhiping Xiao Zhengyang Mao Hourun Li Yiyang Gu Yifang Qin Nan Yin Senzhang Wang et al. 2024. A survey of graph neural networks in real world: Imbalance noise privacy and OOD challenges. arXiv:2403.04468. Retrieved from https:\/\/arxiv.org\/abs\/2403.04468","DOI":"10.1109\/TPAMI.2025.3630673"},{"key":"e_1_3_1_28_2","first-page":"9263","article-title":"GNNGuard: Defending graph neural networks against adversarial attacks","volume":"33","author":"Zhang Xiang","year":"2020","unstructured":"Xiang Zhang and Marinka Zitnik. 2020. GNNGuard: Defending graph neural networks against adversarial attacks. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 33, 9263\u20139275.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"issue":"2","key":"e_1_3_1_29_2","doi-asserted-by":"crossref","first-page":"682","DOI":"10.1109\/TKDE.2023.3290792","article-title":"Individual and structural graph information bottlenecks for out-of-distribution generalization","volume":"36","author":"Yang Ling","year":"2023","unstructured":"Ling Yang, Jiayi Zheng, Heyuan Wang, Zhongyi Liu, Zhilin Huang, Shenda Hong, Wentao Zhang, and Bin Cui. 2023. Individual and structural graph information bottlenecks for out-of-distribution generalization. IEEE Transactions on Knowledge and Data Engineering 36, 2 (2023), 682\u2013693.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_30_2","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Chen Yongqiang","year":"2024","unstructured":"Yongqiang Chen, Yatao Bian, Bo Han, and James Cheng. 2024. How interpretable are interpretable graph neural networks? In Proceedings of the International Conference on Machine Learning."},{"key":"e_1_3_1_31_2","unstructured":"Kexin Zhang Shuhan Liu Song Wang Weili Shi Chen Chen Pan Li Sheng Li Jundong Li and Kaize Ding. 2024. A survey of deep graph learning under distribution shifts: From graph out-of-distribution generalization to adaptation. arXiv:2410.19265. Retrieved from https:\/\/arxiv.org\/abs\/2410.19265"},{"key":"e_1_3_1_32_2","first-page":"19620","article-title":"Parameterized explainer for graph neural network","volume":"33","author":"Luo Dongsheng","year":"2020","unstructured":"Dongsheng Luo, Wei Cheng, Dongkuan Xu, Wenchao Yu, Bo Zong, Haifeng Chen, and Xiang Zhang. 2020. Parameterized explainer for graph neural network. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 33, 19620\u201319631.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_33_2","unstructured":"Tien-Cuong Bui Van-Duc Le Wen-Syan Li and Sang Kyun Cha. 2022. INGREX: An interactive explanation framework for graph neural networks. arXiv:2211.01548. Retrieved from https:\/\/arxiv.org\/abs\/2211.01548"},{"issue":"5","key":"e_1_3_1_34_2","doi-asserted-by":"crossref","first-page":"058301","DOI":"10.1103\/PhysRevLett.108.058301","article-title":"Fast and accurate modeling of molecular atomization energies with machine learning","volume":"108","author":"Rupp Matthias","year":"2012","unstructured":"Matthias Rupp, Alexandre Tkatchenko, Klaus-Robert M\u00fcller, and O. Anatole Von Lilienfeld. 2012. Fast and accurate modeling of molecular atomization energies with machine learning. Physical Review Letters 108, 5 (2012), 058301.","journal-title":"Physical Review Letters"},{"key":"e_1_3_1_35_2","article-title":"GNNExplainer: Generating explanations for graph neural networks","volume":"32","author":"Ying Zhitao","year":"2019","unstructured":"Zhitao Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, and Jure Leskovec. 2019. GNNExplainer: Generating explanations for graph neural networks. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 32.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_36_2","volume-title":"Proceedings of the 10th International Conference on Learning Representations (ICLR \u201922)","author":"Wu Yingxin","year":"2022","unstructured":"Yingxin Wu, Xiang Wang, An Zhang, Xiangnan He, and Tat-Seng Chua. 2022. Discovering invariant rationales for graph neural networks. In Proceedings of the 10th International Conference on Learning Representations (ICLR \u201922), Virtual Event."},{"key":"e_1_3_1_37_2","unstructured":"Yu Wang Yongqiang Chen Jinfa Huang Zijing Liu Jiying Zhang Xuxin Cheng Yu Li and Zhun Zhong. 2024. Learning invariant graph representations via virtual environment inference. In International Conference on Learning Representations. OpenReview. Retrieved from https:\/\/openreview.net\/forum?id=YQaf8Mcsrr"},{"key":"e_1_3_1_38_2","unstructured":"Bowen Liu Haoyang Li Shuning Wang Shuo Nie and Shanghang Zhang. 2024. Subgraph aggregation for out-of-distribution generalization on graphs. arXiv:2410.22228. Retrieved from https:\/\/arxiv.org\/abs\/2410.22228"},{"key":"e_1_3_1_39_2","unstructured":"Fangyu Ding Haiyang Wang Zhixuan Chu Tianming Li Zhaoping Hu and Junchi Yan. 2024. GSINA: Improving subgraph extraction for graph invariant learning via graph Sinkhorn attention. arXiv:2402.07191. Retrieved from https:\/\/arxiv.org\/abs\/2402.07191"},{"key":"e_1_3_1_40_2","first-page":"20437","article-title":"Graph information bottleneck","volume":"33","author":"Wu Tailin","year":"2020","unstructured":"Tailin Wu, Hongyu Ren, Pan Li, and Jure Leskovec. 2020. Graph information bottleneck. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 33, 20437\u201320448.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_41_2","volume-title":"Proceedings of the 9th International Conference on Learning Representations (ICLR \u201921)","author":"Yu Junchi","year":"2021","unstructured":"Junchi Yu, Tingyang Xu, Yu Rong, Yatao Bian, Junzhou Huang, and Ran He. 2021. Graph information bottleneck for subgraph recognition. In Proceedings of the 9th International Conference on Learning Representations (ICLR \u201921), Virtual Event."},{"key":"e_1_3_1_42_2","first-page":"4165","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"36","author":"Sun Qingyun","year":"2022","unstructured":"Qingyun Sun, Jianxin Li, Hao Peng, Jia Wu, Xingcheng Fu, Cheng Ji, and S. Yu Philip. 2022. Graph structure learning with variational information bottleneck. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 36, 4165\u20134174."},{"key":"e_1_3_1_43_2","first-page":"20407","volume-title":"Proceedings of the Advances in Neural Information Processing Systems","author":"Wei Chunyu","year":"2022","unstructured":"Chunyu Wei, Jian Liang, Di Liu, and Fei Wang. 2022. Contrastive graph structure learning via information bottleneck for recommendation. In Proceedings of the Advances in Neural Information Processing Systems. S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh (Eds.), Vol. 35, Curran Associates, Inc., 20407\u201320420."},{"key":"e_1_3_1_44_2","first-page":"19396","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Yu Junchi","year":"2022","unstructured":"Junchi Yu, Jie Cao, and Ran He. 2022. Improving subgraph recognition with variational graph information bottleneck. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 19396\u201319405."},{"key":"e_1_3_1_45_2","first-page":"11620","volume-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Yu Junchi","year":"2023","unstructured":"Junchi Yu, Jian Liang, and Ran He. 2023. Mind the label shift of augmentation-based graph OOD generalization. In Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 11620\u201311630."},{"key":"e_1_3_1_46_2","unstructured":"Wenwen Xia Yutong Zhang Caihua Shan Dongsheng Li and Yuchen Li. 2024. Enhancing mutual information estimation in self-interpretable graph neural networks."},{"issue":"6","key":"e_1_3_1_47_2","doi-asserted-by":"crossref","first-page":"3290","DOI":"10.1109\/TIT.2016.2555904","article-title":"Structural information and dynamical complexity of networks","volume":"62","author":"Li Angsheng","year":"2016","unstructured":"Angsheng Li and Yicheng Pan. 2016. Structural information and dynamical complexity of networks. IEEE Transactions on Information Theory 62, 6 (2016), 3290\u20133339.","journal-title":"IEEE Transactions on Information Theory"},{"issue":"1","key":"e_1_3_1_48_2","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1145\/602382.602397","article-title":"Three great challenges for half-century-old computer science","volume":"50","author":"Brooks Frederick P.","year":"2003","unstructured":"Frederick P. Brooks. 2003. Three great challenges for half-century-old computer science. Journal of the ACM 50, 1 (January 2003), 25\u201326.","journal-title":"Journal of the ACM"},{"issue":"3","key":"e_1_3_1_49_2","first-page":"11","article-title":"Multi-agent hierarchical graph attention reinforcement learning for grid-aware energy management","volume":"21","author":"Feng Bingyi","year":"2023","unstructured":"Bingyi Feng, Mingxiao Feng, Minrui Wang, Wengang Zhou, and Houqiang Li. 2023. Multi-agent hierarchical graph attention reinforcement learning for grid-aware energy management. ZTE Communications 21, 3 (2023), 11\u201321.","journal-title":"ZTE Communications"},{"key":"e_1_3_1_50_2","first-page":"2083","volume-title":"Proceedings of the 36th International Conference on Machine Learning (ICML \u201919)","volume":"97","author":"Gao Hongyang","year":"2019","unstructured":"Hongyang Gao and Shuiwang Ji. 2019. Graph U-Nets. In Proceedings of the 36th International Conference on Machine Learning (ICML \u201919). Proceedings of Machine Learning Research, Vol. 97. PMLR, 2083\u20132092."},{"key":"e_1_3_1_51_2","first-page":"3734","volume-title":"Proceedings of the 36th International Conference on Machine Learning (ICML \u201919)","volume":"97","author":"Lee Junhyun","year":"2019","unstructured":"Junhyun Lee, Inyeop Lee, and Jaewoo Kang. 2019. Self-attention graph pooling. In Proceedings of the 36th International Conference on Machine Learning (ICML \u201919). Proceedings of Machine Learning Research, Vol. 97. PMLR, 3734\u20133743."},{"key":"e_1_3_1_52_2","first-page":"5470","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"34","author":"Ranjan Ekagra","year":"2020","unstructured":"Ekagra Ranjan, Soumya Sanyal, and Partha Talukdar. 2020. Asap: Adaptive structure aware pooling for learning hierarchical graph representations. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, 5470\u20135477."},{"key":"e_1_3_1_53_2","unstructured":"Naftali Tishby Fernando C. Pereira and William Bialek. T. 2000. The information bottleneck method. arXiv:physics\/0004057. Retrieved from https:\/\/arxiv.org\/abs\/physics\/0004057"},{"issue":"3","key":"e_1_3_1_54_2","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/j.1538-7305.1948.tb01338.x","article-title":"A mathematical theory of communication","volume":"27","author":"Shannon Claude Elwood","year":"1948","unstructured":"Claude Elwood Shannon. 1948. A mathematical theory of communication. Bell System Technical Journal 27, 3 (1948), 379\u2013423.","journal-title":"Bell System Technical Journal"},{"issue":"6","key":"e_1_3_1_55_2","doi-asserted-by":"crossref","first-page":"1191","DOI":"10.1162\/089976603321780272","article-title":"Estimation of entropy and mutual information","volume":"15","author":"Paninski Liam","year":"2003","unstructured":"Liam Paninski. 2003. Estimation of entropy and mutual information. Neural Computation 15, 6 (2003), 1191\u20131253.","journal-title":"Neural Computation"},{"key":"e_1_3_1_56_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Alemi Alexander A.","year":"2017","unstructured":"Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, and Kevin Murphy. 2017. Deep variational information bottleneck. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_1_57_2","volume-title":"Proceedings of the 40th International Conference on Machine Learning (ICML \u201923)","author":"Lee Namkyeong","year":"2023","unstructured":"Namkyeong Lee, Dongmin Hyun, Gyoung S. Na, Sungwon Kim, Junseok Lee, and Chanyoung Park. 2023. Conditional graph information bottleneck for molecular relational learning18871. In Proceedings of the 40th International Conference on Machine Learning (ICML \u201923). JMLR.org."},{"issue":"5","key":"e_1_3_1_58_2","first-page":"4908","article-title":"Graph transfer learning via adversarial domain adaptation with graph convolution","volume":"35","author":"Dai Quanyu","year":"2022","unstructured":"Quanyu Dai, Xiao-Ming Wu, Jiaren Xiao, Xiao Shen, and Dan Wang. 2022. Graph transfer learning via adversarial domain adaptation with graph convolution. IEEE Transactions on Knowledge and Data Engineering 35, 5 (2022), 4908\u20134922.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_59_2","unstructured":"Boshen Shi Yongqing Wang Fangda Guo Bingbing Xu Huawei Shen and Xueqi Cheng. 2024. Graph domain adaptation: Challenges progress and prospects. arXiv:2402.00904. Retrieved from https:\/\/arxiv.org\/abs\/2402.00904"},{"issue":"1","key":"e_1_3_1_60_2","doi-asserted-by":"crossref","first-page":"171308","DOI":"10.1007\/s11704-022-1283-6","article-title":"Self-adaptive label filtering learning for unsupervised domain adaptation","volume":"17","author":"Qing Tian","year":"2023","unstructured":"Tian Qing, Sun Heyang, Peng Shun, and Ma Tinghuai. 2023. Self-adaptive label filtering learning for unsupervised domain adaptation. Frontiers of Computer Science 17, 1 (2023), 171308.","journal-title":"Frontiers of Computer Science"},{"issue":"3","key":"e_1_3_1_61_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3631712","article-title":"Graph domain adaptation: A generative view","volume":"18","author":"Cai Ruichu","year":"2024","unstructured":"Ruichu Cai, Fengzhu Wu, Zijian Li, Pengfei Wei, Lingling Yi, and Kun Zhang. 2024. Graph domain adaptation: A generative view. ACM Transactions on Knowledge Discovery from Data 18, 3, Article 60 (January 2024), 1\u201324.","journal-title":"ACM Transactions on Knowledge Discovery from Data"},{"key":"e_1_3_1_62_2","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1109\/ICDM51629.2021.00024","volume-title":"Proceedings of the 2021 IEEE International Conference on Data Mining (ICDM)","author":"Gritsenko Andrey","year":"2021","unstructured":"Andrey Gritsenko, Yuan Guo, Kimia Shayestehfard, Armin Moharrer, Jennifer Dy, and Stratis Ioannidis. 2021. Graph transfer learning. In Proceedings of the 2021 IEEE International Conference on Data Mining (ICDM), 141\u2013150."},{"issue":"7","key":"e_1_3_1_63_2","doi-asserted-by":"crossref","first-page":"1805","DOI":"10.1109\/TKDE.2013.97","article-title":"Transfer learning with graph co-regularization","volume":"26","author":"Long Mingsheng","year":"2013","unstructured":"Mingsheng Long, Jianmin Wang, Guiguang Ding, Dou Shen, and Qiang Yang. 2013. Transfer learning with graph co-regularization. IEEE Transactions on Knowledge and Data Engineering 26, 7 (2013), 1805\u20131818.","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"e_1_3_1_64_2","first-page":"2189","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Creager Elliot","year":"2021","unstructured":"Elliot Creager, J\u00f6rn-Henrik Jacobsen, and Richard Zemel. 2021. Environment inference for invariant learning. In Proceedings of the International Conference on Machine Learning. PMLR, 2189\u20132200."},{"key":"e_1_3_1_65_2","unstructured":"Martin Arjovsky L\u00e9on Bottou Ishaan Gulrajani and David Lopez-Paz. 2019. Invariant risk minimization. arXiv:1907.02893. Retrieved from https:\/\/arxiv.org\/abs\/1907.02893"},{"key":"e_1_3_1_66_2","volume-title":"Proceedings of the International Conference on Learning Representations (ICLR \u201920","author":"Sagawa Shiori","year":"2020","unstructured":"Shiori Sagawa, Pang Wei Koh, Tatsunori B. Hashimoto, and Percy Liang. 2020. Distributionally robust neural networks. In Proceedings of the International Conference on Learning Representations (ICLR \u201920)."},{"key":"e_1_3_1_67_2","first-page":"5815","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Krueger David","year":"2021","unstructured":"David Krueger, Ethan Caballero, Joern-Henrik Jacobsen, Amy Zhang, Jonathan Binas, Dinghuai Zhang, Remi Le Priol, and Aaron Courville. 2021. Out-of-distribution generalization via risk extrapolation (REx). In Proceedings of the International Conference on Machine Learning. PMLR, 5815\u20135826."},{"key":"e_1_3_1_68_2","first-page":"15524","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Miao Siqi","year":"2022","unstructured":"Siqi Miao, Mia Liu, and Pan Li. 2022. Interpretable and generalizable graph learning via stochastic attention mechanism. In Proceedings of the International Conference on Machine Learning. PMLR, 15524\u201315543."},{"key":"e_1_3_1_69_2","volume-title":"Proceedings of the 41st International Conference on Machine Learning","author":"Li Xiner","year":"2024","unstructured":"Xiner Li, Shurui Gui, Youzhi Luo, and Shuiwang Ji. 2024. Graph structure extrapolation for out-of-distribution generalization. In Proceedings of the 41st International Conference on Machine Learning."},{"issue":"4","key":"e_1_3_1_70_2","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1002\/aaai.12202","article-title":"A survey of out-of-distribution generalization for graph machine learning from a causal view","volume":"45","author":"Ma Jing","year":"2024","unstructured":"Jing Ma. 2024. A survey of out-of-distribution generalization for graph machine learning from a causal view. AI Magazine 45, 4 (2024), 537\u2013548.","journal-title":"AI Magazine"},{"key":"e_1_3_1_71_2","first-page":"1696","volume-title":"Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD \u201922)","author":"Sui Yongduo","year":"2022","unstructured":"Yongduo Sui, Xiang Wang, Jiancan Wu, Min Lin, Xiangnan He, and Tat-Seng Chua. 2022. Causal attention for interpretable and generalizable graph classification. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD \u201922), 1696\u20131705. ACM, New York, NY."},{"key":"e_1_3_1_72_2","volume-title":"Proceedings of the 10th International Conference on Learning Representations (ICLR \u201922)","author":"Wu Qitian","year":"2022","unstructured":"Qitian Wu, Hengrui Zhang, Junchi Yan, and David Wipf. 2022. Handling distribution shifts on graphs: An invariance perspective. In Proceedings of the 10th International Conference on Learning Representations (ICLR \u201922), Virtual Event."},{"key":"e_1_3_1_73_2","volume-title":"Proceedings of the 13th International Conference on Learning Representations","author":"Tianjun Yao, Yongqiang Chen, Tongliang Liu, Le Song, Eric P. Xing, and Zhiqiang Shen","year":"2024","unstructured":"Tianjun Yao, Yongqiang Chen, Tongliang Liu, Le Song, Eric P. Xing, and Zhiqiang Shen. 2024. Diversifying spurious subgraphs for graph out-of-distribution generalization. In Proceedings of the 13th International Conference on Learning Representations. OpenReview. Retrieved from https:\/\/openreview.net\/forum?id=XWaI6FLVgi"},{"key":"e_1_3_1_74_2","volume-title":"Proceedings of the International Conference on Learning Representations","author":"Ahuja Kartik","year":"2021","unstructured":"Kartik Ahuja, Jun Wang, Amit Dhurandhar, Karthikeyan Shanmugam, and Kush R. Varshney. 2021. Empirical or invariant risk minimization? A sample complexity perspective. In Proceedings of the International Conference on Learning Representations."},{"key":"e_1_3_1_75_2","first-page":"3438","article-title":"Invariance principle meets information bottleneck for out-of-distribution generalization","volume":"34","author":"Ahuja Kartik","year":"2021","unstructured":"Kartik Ahuja, Ethan Caballero, Dinghuai Zhang, Jean-Christophe Gagnon-Audet, Yoshua Bengio, Ioannis Mitliagkas, and Irina Rish. 2021. Invariance principle meets information bottleneck for out-of-distribution generalization. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 34, 3438\u20133450.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_76_2","article-title":"Understanding and improving feature learning for out-of-distribution generalization","volume":"36","author":"Chen Yongqiang","year":"2024","unstructured":"Yongqiang Chen, Wei Huang, Kaiwen Zhou, Yatao Bian, Bo Han, and James Cheng. 2024. Understanding and improving feature learning for out-of-distribution generalization. In Proceedings of the Advances in Neural Information Processing Systems, Vol. 36.","journal-title":"Proceedings of the Advances in Neural Information Processing Systems"},{"key":"e_1_3_1_77_2","first-page":"1548","volume-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD \u201923)","author":"Liu Yang","year":"2023","unstructured":"Yang Liu, Xiang Ao, Fuli Feng, Yunshan Ma, Kuan Li, Tat-Seng Chua, and Qing He. 2023. FLOOD: A flexible invariant learning framework for out-of-distribution generalization on graphs. In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD \u201923). ACM, New York, NY, 1548\u20131558."},{"key":"e_1_3_1_78_2","first-page":"37293","volume-title":"Proceedings of the International Conference on Machine Learning","author":"Wu Junran","year":"2023","unstructured":"Junran Wu, Xueyuan Chen, Bowen Shi, Shangzhe Li, and Ke Xu. 2023. Sega: Structural entropy guided anchor view for graph contrastive learning. In Proceedings of the International Conference on Machine Learning. PMLR, 37293\u201337312."},{"issue":"1","key":"e_1_3_1_79_2","doi-asserted-by":"crossref","first-page":"3265","DOI":"10.1038\/s41467-018-05691-7","article-title":"Decoding topologically associating domains with ultra-low resolution hi-c data by graph structural entropy","volume":"9","author":"Li Angsheng","year":"2018","unstructured":"Angsheng Li, Xianchen Yin, Bingxiang Xu, Danyang Wang, Jimin Han, Yi Wei, Yun Deng, Ying Xiong, and Zhihua Zhang. 2018. Decoding topologically associating domains with ultra-low resolution hi-c data by graph structural entropy. Nature Communications 9, 1 (2018), 3265.","journal-title":"Nature Communications"},{"key":"e_1_3_1_80_2","first-page":"114","volume-title":"Proceedings of the 16th ACM International Conference on Web Search and Data Mining (WSDM \u201923)","author":"Yang Zhenyu","year":"2023","unstructured":"Zhenyu Yang, Ge Zhang, Jia Wu, Jian Yang, Quan Z. Sheng, Hao Peng, Angsheng Li, Shan Xue, and Jianlin Su. 2023. Minimum entropy principle guided graph neural networks. In Proceedings of the 16th ACM International Conference on Web Search and Data Mining (WSDM \u201923). ACM, New York, NY, 114\u2013122."},{"key":"e_1_3_1_81_2","first-page":"8372","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"38","author":"Duan Liang","year":"2024","unstructured":"Liang Duan, Xiang Chen, Wenjie Liu, Daliang Liu, Kun Yue, and Angsheng Li. 2024. Structural entropy based graph structure learning for node classification. Proceedings of the AAAI Conference on Artificial Intelligence 38, 8 (2024), 8372\u20138379."},{"key":"e_1_3_1_82_2","first-page":"2767","volume-title":"Proceedings of the 13th International Joint Conference on Artificial Intelligence (IJCAI \u201921), Main Track","author":"Luo Gongxu","year":"2021","unstructured":"Gongxu Luo, Jianxin Li, Hao Peng, Carl Yang, Lichao Sun, Philip S. Yu, and Lifang He. 2021. Graph entropy guided node embedding dimension selection for graph neural networks. In Proceedings of the 13th International Joint Conference on Artificial Intelligence (IJCAI \u201921), Main Track. Zhi-Hua Zhou (Ed.), International Joint Conferences on Artificial Intelligence Organization, 2767\u20132774."},{"key":"e_1_3_1_83_2","unstructured":"Ziheng Sun Chris Ding and Jicong Fan. 2024. Learning graph representations via graph entropy maximization. In Proceedings of the 41st International Conference on Machine Learning Vol. 235 PMLR 47133\u201347158."},{"key":"e_1_3_1_84_2","first-page":"3210","volume-title":"Proceedings of the 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP \u201921)","author":"Dasoulas George","year":"2021","unstructured":"George Dasoulas, Giannis Nikolentzos, Kevin Seaman, Aladin Virmaux, and Michalis Vazirgiannis. 2021. Ego-based entropy measures for structural representations on graphs. In Proceedings of the 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP \u201921), 3210\u20133214."},{"key":"e_1_3_1_85_2","doi-asserted-by":"crossref","first-page":"108951","DOI":"10.1016\/j.patcog.2022.108951","article-title":"Alleviating the over-smoothing of graph neural computing by a data augmentation strategy with entropy preservation","volume":"132","author":"Liu Xue","year":"2022","unstructured":"Xue Liu, Dan Sun, and Wei Wei. 2022. Alleviating the over-smoothing of graph neural computing by a data augmentation strategy with entropy preservation. Pattern Recognition 132, C (December 2022), 108951.","journal-title":"Pattern Recognition"},{"key":"e_1_3_1_86_2","volume-title":"Proceedings of the 5th International Conference on Learning Representations (ICLR \u201917), Conference Track Proceedings","author":"Maddison Chris J.","year":"2017","unstructured":"Chris J. Maddison, Andriy Mnih, and Yee Whye Teh. 2017. The concrete distribution: A continuous relaxation of discrete random variables. In Proceedings of the 5th International Conference on Learning Representations (ICLR \u201917), Conference Track Proceedings."},{"key":"e_1_3_1_87_2","first-page":"4292","volume-title":"Proceedings of the 29th AAAI Conference on Artificial Intelligence","author":"Rossi Ryan A.","year":"2015","unstructured":"Ryan A. Rossi and Nesreen K. Ahmed. 2015. The network data repository with interactive graph analytics and visualization. In Proceedings of the 29th AAAI Conference on Artificial Intelligence. AAAI Press, 4292\u20134293."},{"issue":"5","key":"e_1_3_1_88_2","first-page":"5782","article-title":"Explainability in graph neural networks: A taxonomic survey","volume":"45","author":"Yuan Hao","year":"2022","unstructured":"Hao Yuan, Haiyang Yu, Shurui Gui, and Shuiwang Ji. 2022. Explainability in graph neural networks: A taxonomic survey. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 5 (2022), 5782\u20135799.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_1_89_2","doi-asserted-by":"crossref","first-page":"1631","DOI":"10.18653\/v1\/D13-1170","volume-title":"Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing","author":"Socher Richard","year":"2013","unstructured":"Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D. Manning, Andrew Ng, and Christopher Potts. 2013. Recursive deep models for semantic compositionality over a sentiment treebank. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 1631\u20131642."},{"key":"e_1_3_1_90_2","volume-title":"Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020 (NeurIPS \u201920)","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 33: Annual Conference on Neural Information Processing Systems 2020 (NeurIPS \u201920), Virtual Event."},{"issue":"2","key":"e_1_3_1_91_2","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1039\/C7SC02664A","article-title":"MoleculeNet: A benchmark for molecular machine learning","volume":"9","author":"Wu Zhenqin","year":"2018","unstructured":"Zhenqin Wu, Bharath Ramsundar, Evan N. Feinberg, Joseph Gomes, Caleb Geniesse, Aneesh S. Pappu, Karl Leswing, and Vijay Pande. 2018. MoleculeNet: A benchmark for molecular machine learning. Chemical Science 9, 2 (2018), 513\u2013530.","journal-title":"Chemical Science"},{"issue":"3","key":"e_1_3_1_92_2","doi-asserted-by":"crossref","first-page":"1650","DOI":"10.1109\/TPAMI.2021.3112205","article-title":"Recognizing predictive substructures with subgraph information bottleneck","volume":"46","author":"Yu Junchi","year":"2024","unstructured":"Junchi Yu, Tingyang Xu, Yu Rong, Yatao Bian, Junzhou Huang, and Ran He. 2024. Recognizing predictive substructures with subgraph information bottleneck. IEEE Transactions on Pattern Analysis and Machine Intelligence 46, 3 (2024), 1650\u20131663.","journal-title":"IEEE Transactions on Pattern Analysis and Machine Intelligence"},{"key":"e_1_3_1_93_2","volume-title":"Proceedings of the 41st International Conference on Machine Learning","author":"Haoyang Li","year":"2024","unstructured":"Li Haoyang, Wang Xin, Zhang Zeyang, Chen Haibo, Zhang Ziwei, and Zhu Wenwu. 2024. Disentangled graph self-supervised learning for out-of-distribution generalization. In Proceedings of the 41st International Conference on Machine Learning."},{"key":"e_1_3_1_94_2","first-page":"5363","volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence","volume":"34","author":"Pareja Aldo","year":"2020","unstructured":"Aldo Pareja, Giacomo Domeniconi, Jie Chen, Tengfei Ma, Toyotaro Suzumura, Hiroki Kanezashi, Tim Kaler, Tao Schardl, and Charles Leiserson. 2020. EvolveGCN: Evolving graph convolutional networks for dynamic graphs. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 34, 5363\u20135370."},{"key":"e_1_3_1_95_2","first-page":"6861","volume-title":"Proceedings of the 36th International Conference on Machine Learning (ICML)","volume":"97","author":"Wu Felix","year":"2019","unstructured":"Felix Wu, Amauri H. Souza, Jr., Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger. 2019. Simplifying graph convolutional networks. In Proceedings of the 36th International Conference on Machine Learning (ICML). Proceedings of Machine Learning Research, Vol. 97, PMLR, 6861\u20136871."},{"key":"e_1_3_1_96_2","volume-title":"Proceedings of the 7th International Conference on Learning Representations (ICLR \u201919)","author":"Klicpera Johannes","year":"2019","unstructured":"Johannes Klicpera, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2019. Predict then propagate: Graph neural networks meet personalized PageRank. In Proceedings of the 7th International Conference on Learning Representations (ICLR \u201919)."},{"key":"e_1_3_1_97_2","volume-title":"Proceedings of the 9th International Conference on Learning Representations (ICLR \u201921)","author":"Chien Eli","year":"2021","unstructured":"Eli Chien, Jianhao Peng, Pan Li, and Olgica Milenkovic. 2021. Adaptive universal generalized PageRank graph neural network. In Proceedings of the 9th International Conference on Learning Representations (ICLR \u201921), Virtual Event."}],"container-title":["ACM Transactions on Knowledge Discovery from Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3767162","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,22]],"date-time":"2025-11-22T07:59:37Z","timestamp":1763798377000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3767162"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,21]]},"references-count":96,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2026,1,31]]}},"alternative-id":["10.1145\/3767162"],"URL":"https:\/\/doi.org\/10.1145\/3767162","relation":{},"ISSN":["1556-4681","1556-472X"],"issn-type":[{"type":"print","value":"1556-4681"},{"type":"electronic","value":"1556-472X"}],"subject":[],"published":{"date-parts":[[2025,11,21]]},"assertion":[{"value":"2025-01-24","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-08-23","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-11-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}