{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,6]],"date-time":"2026-02-06T00:09:50Z","timestamp":1770336590926,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":53,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T00:00:00Z","timestamp":1650844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"the Agency for Science, Technology and Research (A*STAR) under its AME Programmatic Funds","award":["A20H6b0151"],"award-info":[{"award-number":["A20H6b0151"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,4,25]]},"DOI":"10.1145\/3485447.3512197","type":"proceedings-article","created":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T05:11:23Z","timestamp":1650863483000},"page":"1506-1516","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":17,"title":["On Size-Oriented Long-Tailed Graph Classification of Graph Neural Networks"],"prefix":"10.1145","author":[{"given":"Zemin","family":"Liu","sequence":"first","affiliation":[{"name":"Singapore Management University, Singapore"}]},{"given":"Qiheng","family":"Mao","sequence":"additional","affiliation":[{"name":"Zhejiang University, China and Alibaba-Zhejiang University Joint Institute of Frontier Technologies, China"}]},{"given":"Chenghao","family":"Liu","sequence":"additional","affiliation":[{"name":"Salesforce Research Asia, Singapore"}]},{"given":"Yuan","family":"Fang","sequence":"additional","affiliation":[{"name":"Singapore Management University, Singapore"}]},{"given":"Jianling","family":"Sun","sequence":"additional","affiliation":[{"name":"Zhejiang University, China and Alibaba-Zhejiang University Joint Institute of Frontier Technologies, China"}]}],"member":"320","published-online":{"date-parts":[[2022,4,25]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"crossref","unstructured":"Daniel Beck Gholamreza Haffari and Trevor Cohn. 2018. Graph-to-Sequence Learning using Gated Graph Neural Networks. In ACL. 273\u2013283.","DOI":"10.18653\/v1\/P18-1026"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/bti1007"},{"key":"e_1_3_2_1_3_1","first-page":"1616","article-title":"A comprehensive survey of graph embedding: Problems, techniques, and applications","volume":"30","author":"Cai Hongyun","year":"2018","unstructured":"Hongyun Cai, Vincent\u00a0W Zheng, and Kevin Chen-Chuan Chang. 2018. A comprehensive survey of graph embedding: Problems, techniques, and applications. IEEE TKDE 30, 9 (2018), 1616\u20131637.","journal-title":"IEEE TKDE"},{"key":"e_1_3_2_1_4_1","doi-asserted-by":"publisher","DOI":"10.1145\/1961189.1961199"},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401043"},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"crossref","unstructured":"Kaize Ding Jianling Wang Jundong Li Kai Shu Chenghao Liu and Huan Liu. 2020. Graph prototypical networks for few-shot learning on attributed networks. In CIKM. 295\u2013304.","DOI":"10.1145\/3340531.3411922"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0022-2836(03)00628-4"},{"key":"e_1_3_2_1_8_1","unstructured":"David\u00a0K Duvenaud Dougal Maclaurin Jorge Iparraguirre Rafael Bombarell Timothy Hirzel Alan Aspuru-Guzik and Ryan\u00a0P Adams. 2015. Convolutional Networks on Graphs for Learning Molecular Fingerprints. In NeurIPS. 2224\u20132232."},{"key":"e_1_3_2_1_9_1","unstructured":"Hongyang Gao and Shuiwang Ji. 2019. Graph u-nets. In ICML. 2083\u20132092."},{"key":"e_1_3_2_1_10_1","unstructured":"Justin Gilmer Samuel\u00a0S Schoenholz Patrick\u00a0F Riley Oriol Vinyals and George\u00a0E Dahl. 2017. Neural message passing for quantum chemistry. In ICML. 1263\u20131272."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"Aditya Grover and Jure Leskovec. 2016. node2vec: Scalable feature learning for networks. In KDD. 855\u2013864.","DOI":"10.1145\/2939672.2939754"},{"key":"e_1_3_2_1_12_1","unstructured":"David Ha Andrew Dai and Quoc\u00a0V Le. 2017. Hypernetworks. In ICLR."},{"key":"e_1_3_2_1_13_1","unstructured":"William\u00a0L Hamilton Rex Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NeurIPS. 1025\u20131035."},{"key":"e_1_3_2_1_14_1","unstructured":"Bingyi Kang Saining Xie Marcus Rohrbach Zhicheng Yan Albert Gordo Jiashi Feng and Yannis Kalantidis. 2020. Decoupling representation and classifier for long-tailed recognition. In ICLR."},{"key":"e_1_3_2_1_15_1","unstructured":"Thomas\u00a0N Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In ICLR."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/s41109-019-0195-3"},{"key":"e_1_3_2_1_17_1","unstructured":"Junhyun Lee Inyeop Lee and Jaewoo Kang. 2019. Self-attention graph pooling. In ICML. 3734\u20133743."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1109\/ACCESS.2021.3070690"},{"key":"e_1_3_2_1_19_1","unstructured":"Jialun Liu Yifan Sun Chuchu Han Zhaopeng Dou and Wenhui Li. 2020. Deep Representation Learning on Long-tailed Data: A Learnable Embedding Augmentation Perspective. In CVPR. 2970\u20132979."},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"crossref","unstructured":"Siyi Liu and Yujia Zheng. 2020. Long-tail session-based recommendation. In RecSys. 509\u2013514.","DOI":"10.1145\/3383313.3412222"},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Zemin Liu Yuan Fang Chenghao Liu and Steven\u00a0C.H. Hoi. 2021. Node-wise Localization of Graph Neural Networks. In IJCAI. 1520\u20131526.","DOI":"10.24963\/ijcai.2021\/210"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"crossref","unstructured":"Zemin Liu Yuan Fang Chenghao Liu and Steven\u00a0CH Hoi. 2021. Relative and Absolute Location Embedding for Few-Shot Node Classification on Graph. In AAAI.","DOI":"10.1609\/aaai.v35i5.16551"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2021.3087970"},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Zemin Liu Trung-Kien Nguyen and Yuan Fang. 2021. Tail-GNN: Tail-Node Graph Neural Networks. In KDD. 1109\u20131119.","DOI":"10.1145\/3447548.3467276"},{"key":"e_1_3_2_1_25_1","doi-asserted-by":"crossref","unstructured":"Zemin Liu Wentao Zhang Yuan Fang Xinming Zhang and Steven\u00a0CH Hoi. 2020. Towards locality-aware meta-learning of tail node embeddings on networks. In CIKM. 975\u2013984.","DOI":"10.1145\/3340531.3411910"},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"crossref","unstructured":"Yao Ma Suhang Wang Charu\u00a0C Aggarwal and Jiliang Tang. 2019. Graph convolutional networks with eigenpooling. In KDD. 723\u2013731.","DOI":"10.1145\/3292500.3330982"},{"key":"e_1_3_2_1_27_1","unstructured":"Diego Mesquita Amauri Souza and Samuel Kaski. 2020. Rethinking pooling in graph neural networks. In NeurIPS."},{"key":"e_1_3_2_1_28_1","unstructured":"Francesco Orsini Paolo Frasconi and Luc De\u00a0Raedt. 2015. Graph invariant kernels. In IJCAI. 3756\u20133762."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"crossref","unstructured":"Ethan Perez Florian Strub Harm De\u00a0Vries Vincent Dumoulin and Aaron Courville. 2018. FiLM: Visual reasoning with a general conditioning layer. In AAAI. 3942\u20133951.","DOI":"10.1609\/aaai.v32i1.11671"},{"key":"e_1_3_2_1_30_1","doi-asserted-by":"crossref","unstructured":"Bryan Perozzi Rami Al-Rfou and Steven Skiena. 2014. DeepWalk: Online learning of social representations. In KDD. 701\u2013710.","DOI":"10.1145\/2623330.2623732"},{"key":"e_1_3_2_1_31_1","unstructured":"Liang Qu Huaisheng Zhu Ruiqi Zheng Yuhui Shi and Hongzhi Yin. 2021. ImGAGN: Imbalanced Network Embedding via Generative Adversarial Graph Networks. In KDD. 1390\u20131398."},{"key":"e_1_3_2_1_32_1","volume-title":"Asap: Adaptive structure aware pooling for learning hierarchical graph representations. In AAAI. 5470\u20135477.","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 AAAI. 5470\u20135477."},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1108\/eb024706"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"crossref","unstructured":"Aravind Sankar Junting Wang Adit Krishnan and Hari Sundaram. 2021. ProtoCF: Prototypical Collaborative Filtering for Few-shot Recommendation. In RecSys. 166\u2013175.","DOI":"10.1145\/3460231.3474268"},{"key":"e_1_3_2_1_35_1","volume-title":"Weisfeiler-Lehman graph kernels.Journal of Machine Learning Research 12, 9","author":"Shervashidze Nino","year":"2011","unstructured":"Nino Shervashidze, Pascal Schweitzer, Erik\u00a0Jan Van\u00a0Leeuwen, Kurt Mehlhorn, and Karsten\u00a0M Borgwardt. 2011. Weisfeiler-Lehman graph kernels.Journal of Machine Learning Research 12, 9 (2011)."},{"key":"e_1_3_2_1_36_1","unstructured":"Nino Shervashidze SVN Vishwanathan Tobias Petri Kurt Mehlhorn and Karsten Borgwardt. 2009. Efficient graphlet kernels for large graph comparison. In AISTATS. 488\u2013495."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"crossref","unstructured":"Min Shi Yufei Tang Xingquan Zhu David Wilson and Jianxun Liu. 2020. Multi-class imbalanced graph convolutional network learning. In IJCAI.","DOI":"10.24963\/ijcai.2020\/398"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.1145\/2736277.2741093"},{"key":"e_1_3_2_1_39_1","unstructured":"Kaihua Tang Jianqiang Huang and Hanwang Zhang. 2020. Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect. In NeurIPS."},{"key":"e_1_3_2_1_40_1","doi-asserted-by":"publisher","DOI":"10.1093\/bioinformatics\/btg130"},{"key":"e_1_3_2_1_41_1","unstructured":"Petar Veli\u010dkovi\u0107 Guillem Cucurull Arantxa Casanova Adriana Romero Pietro Lio and Yoshua Bengio. 2018. Graph attention networks. In ICLR."},{"key":"e_1_3_2_1_42_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10115-007-0103-5"},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"crossref","unstructured":"Yiwei Wang Wei Wang Yuxuan Liang Yujun Cai and Bryan Hooi. 2021. CurGraph: Curriculum Learning for Graph Classification. In TheWebConf. 1238\u20131248.","DOI":"10.1145\/3442381.3450025"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"crossref","unstructured":"Yiwei Wang Wei Wang Yuxuan Liang Yujun Cai and Bryan Hooi. 2021. Mixup for Node and Graph Classification. In TheWebConf. 3663\u20133674.","DOI":"10.1145\/3442381.3449796"},{"key":"e_1_3_2_1_45_1","first-page":"4","article-title":"A comprehensive survey on graph neural networks","volume":"32","author":"Wu Zonghan","year":"2020","unstructured":"Zonghan Wu, Shirui Pan, Fengwen Chen, Guodong Long, Chengqi Zhang, and S\u00a0Yu Philip. 2020. A comprehensive survey on graph neural networks. IEEE TNNLS 32, 1 (2020), 4\u201324.","journal-title":"IEEE TNNLS"},{"key":"e_1_3_2_1_46_1","unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2019. How powerful are graph neural networks?. In ICLR."},{"key":"e_1_3_2_1_47_1","doi-asserted-by":"crossref","unstructured":"Pinar Yanardag and SVN Vishwanathan. 2015. Deep graph kernels. In KDD. 1365\u20131374.","DOI":"10.1145\/2783258.2783417"},{"key":"e_1_3_2_1_48_1","unstructured":"Gilad Yehudai Ethan Fetaya Eli Meirom Gal Chechik and Haggai Maron. 2021. On Size Generalization in Graph Neural Networks. https:\/\/openreview.net\/forum?id=9p2CltauWEY"},{"key":"e_1_3_2_1_49_1","doi-asserted-by":"crossref","unstructured":"Jianwen Yin Chenghao Liu Weiqing Wang Jianling Sun and Steven\u00a0CH Hoi. 2020. Learning transferrable parameters for long-tailed sequential user behavior modeling. In KDD. 359\u2013367.","DOI":"10.1145\/3394486.3403078"},{"key":"e_1_3_2_1_50_1","unstructured":"Zhitao Ying Jiaxuan You Christopher Morris Xiang Ren Will Hamilton and Jure Leskovec. 2018. Hierarchical Graph Representation Learning with Differentiable Pooling. In NeurIPS. 4800\u20134810."},{"key":"e_1_3_2_1_51_1","doi-asserted-by":"crossref","unstructured":"Muhan Zhang Zhicheng Cui Marion Neumann and Yixin Chen. 2018. An end-to-end deep learning architecture for graph classification. In AAAI.","DOI":"10.1609\/aaai.v32i1.11782"},{"key":"e_1_3_2_1_52_1","unstructured":"Tianxiang Zhao Xiang Zhang and Suhang Wang. 2021. GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks. In WSDM. 833\u2013841."},{"key":"e_1_3_2_1_53_1","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219968"}],"event":{"name":"WWW '22: The ACM Web Conference 2022","location":"Virtual Event, Lyon France","acronym":"WWW '22","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the ACM Web Conference 2022"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3485447.3512197","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3485447.3512197","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:31:15Z","timestamp":1750188675000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3485447.3512197"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,25]]},"references-count":53,"alternative-id":["10.1145\/3485447.3512197","10.1145\/3485447"],"URL":"https:\/\/doi.org\/10.1145\/3485447.3512197","relation":{},"subject":[],"published":{"date-parts":[[2022,4,25]]},"assertion":[{"value":"2022-04-25","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}