{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T12:00:05Z","timestamp":1777896005192,"version":"3.51.4"},"publisher-location":"New York, NY, USA","reference-count":46,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T00:00:00Z","timestamp":1724457600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,25]]},"DOI":"10.1145\/3637528.3671864","type":"proceedings-article","created":{"date-parts":[[2024,8,25]],"date-time":"2024-08-25T00:55:12Z","timestamp":1724547312000},"page":"3222-3232","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":8,"title":["Optimizing Long-tailed Link Prediction in Graph Neural Networks through Structure Representation Enhancement"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5693-3983","authenticated-orcid":false,"given":"Yakun","family":"Wang","sequence":"first","affiliation":[{"name":"Ant Group, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9841-8605","authenticated-orcid":false,"given":"Daixin","family":"Wang","sequence":"additional","affiliation":[{"name":"Ant Group, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-7273-3486","authenticated-orcid":false,"given":"Hongrui","family":"Liu","sequence":"additional","affiliation":[{"name":"Ant Group, Beijing, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2505-1619","authenticated-orcid":false,"given":"Binbin","family":"Hu","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-8629-2160","authenticated-orcid":false,"given":"Yingcui","family":"Yan","sequence":"additional","affiliation":[{"name":"Ant Group, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7011-4842","authenticated-orcid":false,"given":"Qiyang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Ant Group, Shanghai, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2321-7259","authenticated-orcid":false,"given":"Zhiqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Ant Group, Hangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2024,8,24]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"crossref","unstructured":"Lada A Adamic and Eytan Adar. 2003. Friends and neighbors on the web. Social networks 211--230.","DOI":"10.1016\/S0378-8733(03)00009-1"},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"crossref","unstructured":"Albert-L\u00e1szl\u00f3 Barab\u00e1si and R\u00e9ka Albert. 1999. Emergence of scaling in random networks. science 509--512.","DOI":"10.1126\/science.286.5439.509"},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806512"},{"key":"e_1_3_2_2_4_1","unstructured":"Benjamin Paul Chamberlain Sergey Shirobokov Emanuele Rossi Fabrizio Frasca Thomas Markovich Nils Hammerla Michael M Bronstein and Max Hansmire. 2023. Graph Neural Networks for Link Prediction with Subgraph Sketching. In ICLR."},{"key":"e_1_3_2_2_5_1","volume-title":"Thomas Kipf, and JakubMTomczak.","author":"Davidson Tim R","year":"2018","unstructured":"Tim R Davidson, Luca Falorsi, Nicola De Cao, Thomas Kipf, and JakubMTomczak. 2018. Hyperspherical variational auto-encoders. arXiv preprint arXiv:1804.00891 (2018)."},{"key":"e_1_3_2_2_6_1","unstructured":"Jingtao Ding Yuhan Quan Quanming Yao Yong Li and Depeng Jin. 2020. Simplify and robustify negative sampling for implicit collaborative filtering. In NeurIPS. 1094--1105."},{"key":"e_1_3_2_2_7_1","unstructured":"Will Hamilton Zhitao Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In NeurIPS."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/3397271.3401063"},{"key":"e_1_3_2_2_10_1","volume-title":"Wayne Xin Zhao, and Philip S Yu","author":"Hu Binbin","year":"2018","unstructured":"Binbin Hu, Chuan Shi, Wayne Xin Zhao, and Philip S Yu. 2018. Leveraging meta-path based context for top-n recommendation with a neural co-attention model. In SIGKDD. 1531--1540."},{"key":"e_1_3_2_2_11_1","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 NeurIPS. 22118--22133."},{"key":"e_1_3_2_2_12_1","volume-title":"Few-shot link prediction via graph neural networks for covid-19 drug-repurposing. ICML","author":"Ioannidis Vassilis N","year":"2020","unstructured":"Vassilis N Ioannidis, Da Zheng, and George Karypis. 2020. Few-shot link prediction via graph neural networks for covid-19 drug-repurposing. ICML (2020)."},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"crossref","unstructured":"Leo Katz. 1953. A new status index derived from sociometric analysis. Psychometrika 39--43.","DOI":"10.1007\/BF02289026"},{"key":"e_1_3_2_2_14_1","volume-title":"What uncertainties do we need in bayesian deep learning for computer vision? NeurIPS 30","author":"Kendall Alex","year":"2017","unstructured":"Alex Kendall and Yarin Gal. 2017. What uncertainties do we need in bayesian deep learning for computer vision? NeurIPS 30 (2017)."},{"key":"e_1_3_2_2_15_1","unstructured":"Thomas N Kipf and MaxWelling. 2016. Semi-supervised classification with graph convolutional networks. In ICLR."},{"key":"e_1_3_2_2_16_1","volume-title":"Variational graph auto-encoders. arXiv preprint arXiv:1611.07308","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling. 2016. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016)."},{"key":"e_1_3_2_2_17_1","volume-title":"Kuldeep Singh, and Bhaskar Biswas.","author":"Kumar Ajay","year":"2020","unstructured":"Ajay Kumar, Shashank Sheshar Singh, Kuldeep Singh, and Bhaskar Biswas. 2020. Link prediction techniques, applications, and performance: A survey. Physica A: Statistical Mechanics and its Applications (2020), 124289."},{"key":"e_1_3_2_2_18_1","volume-title":"Evaluating graph neural networks for link prediction: Current pitfalls and new benchmarking. NeurIPS 36","author":"Li Juanhui","year":"2024","unstructured":"Juanhui Li, Harry Shomer, Haitao Mao, Shenglai Zeng, Yao Ma, Neil Shah, Jiliang Tang, and Dawei Yin. 2024. Evaluating graph neural networks for link prediction: Current pitfalls and new benchmarking. NeurIPS 36 (2024)."},{"key":"e_1_3_2_2_19_1","first-page":"628","article-title":"Fairlp: Towards fair link prediction on social network graphs","volume":"16","author":"Li Yanying","year":"2022","unstructured":"Yanying Li, Xiuling Wang, Yue Ning, and Hui Wang. 2022. Fairlp: Towards fair link prediction on social network graphs. In AAAI, Vol. 16. 628--639.","journal-title":"AAAI"},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"crossref","unstructured":"Zemin Liu Qiheng Mao Chenghao Liu Yuan Fang and Jianling Sun. 2022. On size-oriented long-tailed graph classification of graph neural networks. InWWW. 1506--1516.","DOI":"10.1145\/3485447.3512197"},{"key":"e_1_3_2_2_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467276"},{"key":"e_1_3_2_2_22_1","doi-asserted-by":"crossref","unstructured":"Zemin Liu Wentao Zhang Yuan Fang Xinming Zhang and Steven CH Hoi. 2020. Towards locality-aware meta-learning of tail node embeddings on networks. In CIKM. 975--984.","DOI":"10.1145\/3340531.3411910"},{"key":"e_1_3_2_2_23_1","volume-title":"Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications","author":"L\u00fc Linyuan","year":"2011","unstructured":"Linyuan L\u00fc and Tao Zhou. 2011. Link prediction in complex networks: A survey. Physica A: statistical mechanics and its applications (2011), 1150--1170."},{"key":"e_1_3_2_2_24_1","volume-title":"Omkar Bhalerao, Horace He, Ser- Nam Lim, and Austin R Benson.","author":"Singh Abhay","year":"2021","unstructured":"Abhay Singh, Qian Huang, Sijia Linda Huang, Omkar Bhalerao, Horace He, Ser- Nam Lim, and Austin R Benson. 2021. Edge proposal sets for link prediction. arXiv preprint arXiv:2106.15810 (2021)."},{"key":"e_1_3_2_2_25_1","unstructured":"Ben Taskar Ming-Fai Wong Pieter Abbeel and Daphne Koller. 2003. Link prediction in relational data. In NeurIPS."},{"key":"e_1_3_2_2_26_1","unstructured":"Komal Teru Etienne Denis and Will Hamilton. 2020. Inductive relation prediction by subgraph reasoning. In ICML. 9448--9457."},{"key":"e_1_3_2_2_27_1","first-page":"32465","article-title":"Uncovering the Structural Fairness in Graph Contrastive Learning","volume":"35","author":"Shi Chuan","year":"2022","unstructured":"RuijiaWang, XiaoWang, Chuan Shi, and Le Song. 2022. Uncovering the Structural Fairness in Graph Contrastive Learning. NeurIPS 35 (2022), 32465--32473.","journal-title":"NeurIPS"},{"key":"e_1_3_2_2_28_1","volume-title":"Neural Common Neighbor with Completion for Link Prediction. arXiv preprint arXiv:2302.00890","author":"Yang Haotong","year":"2023","unstructured":"XiyuanWang, Haotong Yang, and Muhan Zhang. 2023. Neural Common Neighbor with Completion for Link Prediction. arXiv preprint arXiv:2302.00890 (2023)."},{"key":"e_1_3_2_2_29_1","unstructured":"XiyuanWang Haotong Yang and Muhan Zhang. 2024. Neural common neighbor with completion for link prediction. (2024)."},{"key":"e_1_3_2_2_30_1","volume-title":"Not All Negatives AreWorth Attending to: Meta-Bootstrapping Negative Sampling Framework for Link Prediction. WSDM","author":"Wang Yakun","year":"2024","unstructured":"Yakun Wang, Binbin Hu, Shuo Yang, Meiqi Zhu, Zhiqiang Zhang, Qiyang Zhang, Jun Zhou, Guo Ye, and Huimei He. 2024. Not All Negatives AreWorth Attending to: Meta-Bootstrapping Negative Sampling Framework for Link Prediction. WSDM (2024)."},{"key":"e_1_3_2_2_31_1","volume-title":"Molecular contrastive learning of representations via graph neural networks. Nature Machine Intelligence","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 (2022), 279--287."},{"key":"e_1_3_2_2_32_1","unstructured":"Yu Wang Tong Zhao Yuying Zhao Yunchao Liu Xueqi Cheng Neil Shah and Tyler Derr. 2024. A Topological Perspective on Demystifying GNN-Based Link Prediction Performance. (2024)."},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"e_1_3_2_2_34_1","unstructured":"Keyulu Xu Weihua Hu Jure Leskovec and Stefanie Jegelka. 2018. How powerful are graph neural networks?. In ICLR."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"crossref","unstructured":"Shuo Yang Binbin Hu Zhiqiang Zhang Wang Sun Yang Wang Jun Zhou Hongyu Shan Yuetian Cao Borui Ye Yanming Fang et al. 2021. Inductive Link Prediction with Interactive Structure Learning on Attributed Graph. In ECML PKDD. 383--398.","DOI":"10.1007\/978-3-030-86520-7_24"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"crossref","unstructured":"Shuo Yang Zhiqiang Zhang Jun Zhou Yang Wang Wang Sun Xingyu Zhong Yanming Fang Quan Yu and Yuan Qi. 2021. Financial risk analysis for SMEs with graph-based supply chain mining. In IJCAI. 4661--4667.","DOI":"10.24963\/ijcai.2020\/643"},{"key":"e_1_3_2_2_37_1","unstructured":"Zhilin Yang William Cohen and Ruslan Salakhudinov. 2016. Revisiting semisupervised learning with graph embeddings. In ICML. 40--48."},{"key":"e_1_3_2_2_38_1","doi-asserted-by":"crossref","unstructured":"Rex Ying Ruining He Kaifeng Chen Pong Eksombatchai William L Hamilton and Jure Leskovec. 2018. Graph convolutional neural networks for web-scale recommender systems. In SIGKDD. 974--983.","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"crossref","unstructured":"Rex Ying Ruining He Kaifeng Chen Pong Eksombatchai William L Hamilton and Jure Leskovec. 2018. Graph convolutional neural networks for web-scale recommender systems. In SIGKDD. 974--983.","DOI":"10.1145\/3219819.3219890"},{"key":"e_1_3_2_2_40_1","volume-title":"Neo-gnns: Neighborhood overlap-aware graph neural networks for link prediction. NeurIPS, 13683--13694.","author":"Yun Seongjun","year":"2021","unstructured":"Seongjun Yun, Seoyoon Kim, Junhyun Lee, Jaewoo Kang, and Hyunwoo J Kim. 2021. Neo-gnns: Neighborhood overlap-aware graph neural networks for link prediction. NeurIPS, 13683--13694."},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"crossref","unstructured":"Xiaoling Zang Binbin Hu Jun Chu Zhiqiang Zhang Guannan Zhang Jun Zhou and Wenliang Zhong. 2023. Commonsense Knowledge Graph towards Super APP and Its Applications in Alipay. In SIGKDD. 5509--5519.","DOI":"10.1145\/3580305.3599791"},{"key":"e_1_3_2_2_42_1","unstructured":"Muhan Zhang and Yixin Chen. 2018. Link prediction based on graph neural networks. In NeurIPS."},{"key":"e_1_3_2_2_43_1","unstructured":"Tong Zhao Gang Liu DahengWang Wenhao Yu and Meng Jiang. 2022. Learning from counterfactual links for link prediction. In ICML. 26911--26926."},{"key":"e_1_3_2_2_44_1","volume-title":"Cold brew: Distilling graph node representations with incomplete or missing neighborhoods. arXiv preprint arXiv:2111.04840","author":"Zheng Wenqing","year":"2021","unstructured":"Wenqing Zheng, Edward W Huang, Nikhil Rao, Sumeet Katariya, Zhangyang Wang, and Karthik Subbian. 2021. Cold brew: Distilling graph node representations with incomplete or missing neighborhoods. arXiv preprint arXiv:2111.04840 (2021)."},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"crossref","unstructured":"Tao Zhou Linyuan L\u00fc and Yi-Cheng Zhang. 2009. Predicting missing links via local information. The European Physical Journal B 623--630.","DOI":"10.1140\/epjb\/e2009-00335-8"},{"key":"e_1_3_2_2_46_1","unstructured":"Zhaocheng Zhu Zuobai Zhang Louis-Pascal Xhonneux and Jian Tang. 2021. Neural bellman-ford networks: A general graph neural network framework for link prediction. In NeurIPS. 29476--29490."}],"event":{"name":"KDD '24: The 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","location":"Barcelona Spain","acronym":"KDD '24","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data"]},"container-title":["Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671864","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3637528.3671864","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,21]],"date-time":"2025-08-21T17:46:28Z","timestamp":1755798388000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3637528.3671864"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,24]]},"references-count":46,"alternative-id":["10.1145\/3637528.3671864","10.1145\/3637528"],"URL":"https:\/\/doi.org\/10.1145\/3637528.3671864","relation":{},"subject":[],"published":{"date-parts":[[2024,8,24]]},"assertion":[{"value":"2024-08-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}