{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T03:13:26Z","timestamp":1784258006690,"version":"3.55.0"},"publisher-location":"New York, NY, USA","reference-count":43,"publisher":"ACM","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62202200, 62402197"],"award-info":[{"award-number":["62202200, 62402197"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Inner Mongolia University High-level Talent Project","award":["10000-23112101\/286"],"award-info":[{"award-number":["10000-23112101\/286"]}]},{"name":"Inner Mongolia Autonomous Region Natural Science Foundation","award":["2025QN06010"],"award-info":[{"award-number":["2025QN06010"]}]},{"name":"fund of Supporting the Reform and Development of Local Universities (Disciplinary Construction) and the special research project of First-class Discipline of Inner Mongolia A. R. of China","award":["YLXKZX-ND-036"],"award-info":[{"award-number":["YLXKZX-ND-036"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,10]]},"DOI":"10.1145\/3746252.3761086","type":"proceedings-article","created":{"date-parts":[[2025,11,10]],"date-time":"2025-11-10T18:37:32Z","timestamp":1762799852000},"page":"3478-3487","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":2,"title":["GraphIAM: Two-Stage Algorithm for Improving Class-Imbalanced Node Classification on Attribute-Missing Graphs"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2184-4360","authenticated-orcid":false,"given":"Riting","family":"Xia","sequence":"first","affiliation":[{"name":"College of Computer Science, Inner Mongolia University, Hohhot, Inner Mongolia Autonomous Region, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0825-872X","authenticated-orcid":false,"given":"Chunxu","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Jilin University, Changchun, Jilin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1790-3751","authenticated-orcid":false,"given":"Xueyan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Jilin University, Changchun, Jilin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9828-6964","authenticated-orcid":false,"given":"Anchen","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Jilin University, Changchun, Jilin, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5522-5851","authenticated-orcid":false,"given":"Yan","family":"Zhang","sequence":"additional","affiliation":[{"name":"College of Computer Science, Inner Mongolia University, Hohhot, Inner Mongolia Autonomous Region, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2025,11,10]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-71249-9_46"},{"key":"e_1_3_2_2_2_1","first-page":"1565","article-title":"Learning imbalanced datasets with label-distribution-aware margin loss","author":"Cao Kaidi","year":"2019","unstructured":"Kaidi Cao, Colin Wei, Adrien Gaidon, Nikos Ar\u00e9chiga, and Tengyu Ma. 2019. Learning imbalanced datasets with label-distribution-aware margin loss. In Proceedings of NeurIPS. 1565-1576.","journal-title":"Proceedings of NeurIPS."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.5555\/1622407.1622416"},{"key":"e_1_3_2_2_4_1","first-page":"29885","article-title":"Topology-imbalance learning for semi-supervised node classification","author":"Chen Deli","year":"2021","unstructured":"Deli Chen, Yankai Lin, Guangxiang Zhao, Xuancheng Ren, Peng Li, Jie Zhou, and Xu Sun. 2021. Topology-imbalance learning for semi-supervised node classification. In Proceedings of NeurIPS. 29885-29897.","journal-title":"Proceedings of NeurIPS."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2020.3032189"},{"key":"e_1_3_2_2_6_1","first-page":"14554","article-title":"CoSSL: Co-learning of representation and classifier for imbalanced semi-supervised learning. In Proceedings of CVPR","author":"Fan Yue","year":"2022","unstructured":"Yue Fan, Dengxin Dai, Anna Kukleva, and Bernt Schiele. 2022. CoSSL: Co-learning of representation and classifier for imbalanced semi-supervised learning. In Proceedings of CVPR, . IEEE, 14554-14564.","journal-title":"IEEE"},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102272"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i11.29112"},{"key":"e_1_3_2_2_9_1","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2022.3149997"},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.108538"},{"key":"e_1_3_2_2_11_1","first-page":"391","article-title":"Heterogeneous graph neural network via attribute completion","author":"Jin Di","year":"2021","unstructured":"Di Jin, Cuiying Huo, Chundong Liang, and Liang Yang. 2021. Heterogeneous graph neural network via attribute completion. In Proceedings of WWW. 391-400.","journal-title":"Proceedings of WWW."},{"key":"e_1_3_2_2_12_1","volume-title":"Amer: A new attribute-missing network embedding approach","author":"Jin Di","year":"2022","unstructured":"Di Jin, Rui Wang, Tao Wang, Dongxiao He, Weiping Ding, Yuxiao Huang, Longbiao Wang, and Witold Pedrycz. 2022. Amer: A new attribute-missing network embedding approach. IEEE Transactions on Cybernetics (2022), 1-14. Issue 2168-2267."},{"key":"e_1_3_2_2_13_1","volume-title":"Proceedings of ICLR, .","author":"Kang Bingyi","year":"2020","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 Proceedings of ICLR, ."},{"key":"e_1_3_2_2_14_1","first-page":"1","article-title":"Semi-supervised classification with graph convolutional networks","author":"Kipf Thomas N.","year":"2017","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-supervised classification with graph convolutional networks. In Proceedings of ICLR. 1-1.","journal-title":"Proceedings of ICLR."},{"key":"e_1_3_2_2_15_1","first-page":"1","article-title":"AdapterGNN: Efficient delta tuning improves generalization ability in graph neural networks","author":"Li Shengrui","year":"2024","unstructured":"Shengrui Li, Xueting Han, and Jing Bai. 2024. AdapterGNN: Efficient delta tuning improves generalization ability in graph neural networks. In Proceedings of AAAI. 1-9.","journal-title":"Proceedings of AAAI."},{"key":"e_1_3_2_2_16_1","first-page":"1328","article-title":"GraphSHA: synthesizing harder samples for class-imbalanced node classification","author":"Li Wen-Zhi","year":"2023","unstructured":"Wen-Zhi Li, Chang-Dong Wang, Hui Xiong, and Jian-Huang Lai. 2023. GraphSHA: synthesizing harder samples for class-imbalanced node classification. In Proceedings of KDD. 1328-1340.","journal-title":"Proceedings of KDD."},{"key":"e_1_3_2_2_17_1","volume-title":"Graph neural network with curriculum learning for imbalanced node classification. arXiv preprint arXiv:2202.02529","author":"Li Xiaohe","year":"2022","unstructured":"Xiaohe Li, Lijie Wen, Yawen Deng, Fuli Feng, Xuming Hu, Lei Wang, and Zide Fan. 2022. Graph neural network with curriculum learning for imbalanced node classification. arXiv preprint arXiv:2202.02529 (2022)."},{"key":"e_1_3_2_2_18_1","first-page":"10988","article-title":"Overcoming classifier imbalance for long-tail object detection with balanced group softmax","author":"Li Yu","year":"2020","unstructured":"Yu Li, Tao Wang, Bingyi Kang, Sheng Tang, Chunfeng Wang, Jintao Li, and Jiashi Feng. 2020. Overcoming classifier imbalance for long-tail object detection with balanced group softmax. In Proceedings of CVPR. 10988-10997.","journal-title":"Proceedings of CVPR."},{"key":"e_1_3_2_2_19_1","volume-title":"A survey of imbalanced learning on graphs: problems, techniques, and future directions. arXiv preprint arXiv:2308.13821","author":"Liu Zemin","year":"2023","unstructured":"Zemin Liu, Yuan Li, Nan Chen, Qian Wang, Bryan Hooi, and Bingsheng He. 2023. A survey of imbalanced learning on graphs: problems, techniques, and future directions. arXiv preprint arXiv:2308.13821 (2023)."},{"key":"e_1_3_2_2_20_1","volume-title":"Chawla","author":"Ma Yihong","year":"2023","unstructured":"Yihong Ma, Yijun Tian, Nuno Moniz, and Nitesh V. Chawla. 2023. Class-imbalanced learning on graphs: A survey. arXiv preprint arXiv:2304.04300 (2023)."},{"key":"e_1_3_2_2_21_1","volume-title":"Proceedings of PAKDD.","author":"Mavromatis Costas","unstructured":"Costas Mavromatis and G. Karypis. 2021. Graph infoclust: Maximizing coarse-grain mutual information in graphs. In Proceedings of PAKDD."},{"key":"e_1_3_2_2_22_1","first-page":"1","article-title":"GraphENS: neighbor-aware ego network synthesis for class-imbalanced node classification","author":"Park Joonhyung","year":"2022","unstructured":"Joonhyung Park, Jaeyun Song, and Eunho Yang. 2022. GraphENS: neighbor-aware ego network synthesis for class-imbalanced node classification. In Proceedings of ICLR. 1-1.","journal-title":"Proceedings of ICLR."},{"key":"e_1_3_2_2_23_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467334"},{"key":"e_1_3_2_2_24_1","doi-asserted-by":"publisher","DOI":"10.1609\/aimag.v29i3.2157"},{"key":"e_1_3_2_2_25_1","first-page":"2879","article-title":"Multi-class imbalanced graph convolutional network learning","author":"Shi Min","year":"2020","unstructured":"Min Shi, Yufei Tang, Xingquan Zhu, David A. Wilson, and Jianxun Liu. 2020. Multi-class imbalanced graph convolutional network learning. In Proceedings of IJCAI. 2879-2885.","journal-title":"Proceedings of IJCAI."},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"publisher","DOI":"10.1145\/3589335.3651544"},{"key":"e_1_3_2_2_27_1","volume-title":"IAAI","author":"Tan Yue","unstructured":"Yue Tan, Yixin Liu, Guodong Long, Jing Jiang, Qinghua Lu, and Chengqi Zhang. 2023. Federated learning on non-iid graphs via structural knowledge sharing. In IAAI, . AAAI Press, 9953-9961."},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i14.29464"},{"key":"e_1_3_2_2_29_1","first-page":"3494","article-title":"Initializing then refining: A simple graph attribute imputation network","author":"Tu Wenxuan","year":"2022","unstructured":"Wenxuan Tu, Sihang Zhou, Xinwang Liu, Yue Liu, Zhiping Cai, En Zhu, Changwang Zhang, and Jieren Cheng. 2022. Initializing then refining: A simple graph attribute imputation network. In Proceedings of IJCAI. 3494-3500.","journal-title":"Proceedings of IJCAI."},{"key":"e_1_3_2_2_30_1","volume-title":"Proceedings of ICLR, .","author":"Velickovic Petar","unstructured":"Petar Velickovic, William Fedus, William L. Hamilton, Pietro Li\u00f2, Yoshua Bengio, and R. Devon Hjelm. 2019. Deep graph infomax. In Proceedings of ICLR, ."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/JIOT.2022.3200964"},{"key":"e_1_3_2_2_32_1","first-page":"475","article-title":"RSDNE: exploring relaxed similarity and dissimilarity from completely-imbalanced labels for network embedding","author":"Wang Zheng","year":"2018","unstructured":"Zheng Wang, Xiaojun Ye, Chaokun Wang, Yuexin Wu, Changping Wang, and Kaiwen Liang. 2018. RSDNE: exploring relaxed similarity and dissimilarity from completely-imbalanced labels for network embedding. In Proceedings of AAAI. 475-482.","journal-title":"Proceedings of AAAI."},{"key":"e_1_3_2_2_33_1","volume-title":"Li","author":"Wu Lirong","year":"2022","unstructured":"Lirong Wu, Jun Xia, Zhangyang Gao, Haitao Lin, Cheng Tan, and Stan Z. Li. 2022. GraphMixup: improving class-imbalanced node classification by reinforcement mixup and self-supervised context prediction. In proceedings of ECML PKDD (Lecture Notes in Computer Science, Vol. 13716). 519-535."},{"key":"e_1_3_2_2_34_1","volume-title":"Incomplete Graph Learning: A Comprehensive Survey. CoRR","author":"Xia Riting","year":"2025","unstructured":"Riting Xia, Huibo Liu, Anchen Li, Xueyan Liu, Yan Zhang, Chunxu Zhang, and Bo Yang. 2025. Incomplete Graph Learning: A Comprehensive Survey. CoRR, Vol. abs\/2502.12412 (2025)."},{"key":"e_1_3_2_2_35_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.111583"},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11432-023-3897-2"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v38i1.27788"},{"key":"e_1_3_2_2_38_1","first-page":"2336","article-title":"Accurate node feature estimation with structured variational graph autoencoder","author":"Yoo Jaemin","year":"2022","unstructured":"Jaemin Yoo, Hyunsik Jeon, Jinhong Jung, and U Kang. 2022. Accurate node feature estimation with structured variational graph autoencoder. In Proceedings of KDD. 2336-2346.","journal-title":"Proceedings of KDD."},{"key":"e_1_3_2_2_39_1","first-page":"5812","article-title":"Graph contrastive learning with augmentations","volume":"33","author":"You Yuning","year":"2020","unstructured":"Yuning You, Tianlong Chen, Yongduo Sui, Ting Chen, Zhangyang Wang, and Yang Shen. 2020. Graph contrastive learning with augmentations. In Proceedings of NeurIPS, Vol. 33. 5812-5823.","journal-title":"Proceedings of NeurIPS"},{"key":"e_1_3_2_2_40_1","first-page":"1","article-title":"Sampling reweighting: Boosting the performance of AdaBoost on imbalanced datasets","author":"Yuan Bo","year":"2012","unstructured":"Bo Yuan and Xiaoli Ma. 2012. Sampling reweighting: Boosting the performance of AdaBoost on imbalanced datasets. In Proceedings of IJCNN. IEEE, 1-6.","journal-title":"Proceedings of IJCNN. IEEE"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i9.26319"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1145\/3437963.3441720"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v37i4.25622"}],"event":{"name":"CIKM '25: The 34th ACM International Conference on Information and Knowledge Management","location":"Seoul Republic of Korea","acronym":"CIKM '25","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"]},"container-title":["Proceedings of the 34th ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3746252.3761086","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,12]],"date-time":"2025-12-12T00:42:22Z","timestamp":1765500142000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3746252.3761086"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,10]]},"references-count":43,"alternative-id":["10.1145\/3746252.3761086","10.1145\/3746252"],"URL":"https:\/\/doi.org\/10.1145\/3746252.3761086","relation":{},"subject":[],"published":{"date-parts":[[2025,11,10]]},"assertion":[{"value":"2025-11-10","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}