{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T06:24:22Z","timestamp":1757312662041,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":29,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/https:\/\/doi.org\/10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IS2239881"],"award-info":[{"award-number":["IS2239881"]}],"id":[{"id":"10.13039\/https:\/\/doi.org\/10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,21]]},"DOI":"10.1145\/3627673.3679095","type":"proceedings-article","created":{"date-parts":[[2024,10,20]],"date-time":"2024-10-20T19:34:11Z","timestamp":1729452851000},"page":"5534-5537","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Data Quality-aware Graph Machine Learning"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6908-508X","authenticated-orcid":false,"given":"Yu","family":"Wang","sequence":"first","affiliation":[{"name":"University of Oregon, Eugene, OR, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6684-6752","authenticated-orcid":false,"given":"Kaize","family":"Ding","sequence":"additional","affiliation":[{"name":"Northwestern University, Evanston, IL, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8217-5688","authenticated-orcid":false,"given":"Xiaorui","family":"Liu","sequence":"additional","affiliation":[{"name":"North Carolina State University, Raleigh, NC, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3902-7131","authenticated-orcid":false,"given":"Jian","family":"Kang","sequence":"additional","affiliation":[{"name":"University of Rochester, Rochester, NY, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9758-0635","authenticated-orcid":false,"given":"Ryan","family":"Rossi","sequence":"additional","affiliation":[{"name":"Adobe Research, San Jose, CA, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0080-5998","authenticated-orcid":false,"given":"Tyler","family":"Derr","sequence":"additional","affiliation":[{"name":"Vanderbilt University, Nashville, TN, USA"}]}],"member":"320","published-online":{"date-parts":[[2024,10,21]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Josh Achiam Steven Adler Sandhini Agarwal Lama Ahmad Ilge Akkaya et al. 2023. Gpt-4 technical report. arXiv preprint (2023)."},{"key":"e_1_3_2_1_2_1","unstructured":"Deli Chen Yankai Lin Guangxiang Zhao Xuancheng Ren et al. 2021. Topology-imbalance learning for semi-supervised node classification. NeurIPS."},{"key":"e_1_3_2_1_3_1","unstructured":"Yu Chen Lingfei Wu and Mohammed Zaki. 2020. Iterative deep graph learning for graph neural networks: Better and robust node embeddings. NeurIPS."},{"key":"e_1_3_2_1_4_1","volume-title":"Graphs: New Directions in Topological Imbalance. arXiv preprint arXiv:2406.11685","author":"Cheng Xueqi","year":"2024","unstructured":"Xueqi Cheng, Yu Wang, Yuying Zhao, Charu C Aggarwal, Tyler Derr, et al. 2024. Edge Classification on Graphs: New Directions in Topological Imbalance. arXiv preprint arXiv:2406.11685 (2024)."},{"key":"e_1_3_2_1_5_1","doi-asserted-by":"crossref","unstructured":"Enyan Dai and Suhang Wang. 2021. Say no to the discrimination: Learning fair graph neural networks with limited sensitive attribute information. In WSDM.","DOI":"10.1109\/TKDE.2022.3197554"},{"key":"e_1_3_2_1_6_1","unstructured":"Hanjun Dai Hui Li Tian Tian Xin Huang Lin Wang Jun Zhu and Le Song. 2018. Adversarial attack on graph structured data. In ICML."},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1145\/3575637.3575646"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"crossref","unstructured":"Yushun Dong Jian Kang Hanghang Tong and Jundong Li. 2021. Individual fairness for graph neural networks: A ranking based approach. In KDD.","DOI":"10.1145\/3447548.3467266"},{"key":"e_1_3_2_1_9_1","volume-title":"Robust Graph Neural Networks via Unbiased Aggregation. arXiv preprint arXiv:2311.14934","author":"Feng Ruiqi","year":"2023","unstructured":"Ruiqi Feng, Zhichao Hou, Tyler Derr, and Xiaorui Liu. 2023. Robust Graph Neural Networks via Unbiased Aggregation. arXiv preprint arXiv:2311.14934 (2023)."},{"key":"e_1_3_2_1_10_1","first-page":"7637","article-title":"Robustness of graph neural networks at scale","volume":"34","author":"Geisler Simon","year":"2021","unstructured":"Simon Geisler, Tobias Schmidt, Hakan cSirin, Daniel Z\u00fcgner, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2021. Robustness of graph neural networks at scale. Advances in Neural Information Processing Systems, Vol. 34 (2021), 7637--7649.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_2_1_11_1","volume-title":"Rawlsgcn: Towards rawlsian difference principle on graph convolutional network. In WWW.","author":"Kang Jian","year":"2022","unstructured":"Jian Kang, Yan Zhu, Yinglong Xia, Jiebo Luo, and Hanghang Tong. 2022. Rawlsgcn: Towards rawlsian difference principle on graph convolutional network. In WWW."},{"key":"e_1_3_2_1_12_1","volume-title":"Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907","author":"Kipf Thomas N","year":"2016","unstructured":"Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)."},{"key":"e_1_3_2_1_13_1","unstructured":"Gang Liu Eric Inae Tong Zhao Jiaxin Xu Tengfei Luo and Meng Jiang. 2024. Data-centric learning from unlabeled graphs with diffusion model. In NeurIPS."},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"crossref","unstructured":"Gang Liu Tong Zhao Eric Inae Tengfei Luo and Meng Jiang. 2023. Semi-supervised graph imbalanced regression. In KDD.","DOI":"10.1145\/3580305.3599497"},{"key":"e_1_3_2_1_15_1","unstructured":"Xiaorui Liu Jiayuan Ding Wei Jin Han Xu Yao Ma Zitao Liu and Jiliang Tang. 2021. Graph neural networks with adaptive residual. In NeurIPS."},{"key":"e_1_3_2_1_16_1","volume-title":"International Conference on Machine Learning. PMLR, 6837--6849","author":"Liu Xiaorui","year":"2021","unstructured":"Xiaorui Liu, Wei Jin, Yao Ma, Yaxin Li, Hua Liu, et al. 2021. Elastic graph neural networks. In International Conference on Machine Learning. PMLR, 6837--6849."},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"crossref","unstructured":"Zemin Liu Trung-Kien Nguyen and Yuan Fang. 2023. On Generalized Degree Fairness in Graph Neural Networks. In AAAI. 4525--4533.","DOI":"10.1609\/aaai.v37i4.25574"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467276"},{"key":"e_1_3_2_1_19_1","unstructured":"Haitao Mao Zhikai Chen Wei Jin Haoyu Han et al. 2024. Demystifying Structural Disparity in Graph Neural Networks: Can One Size Fit All? NeurIPS."},{"key":"e_1_3_2_1_20_1","volume-title":"Are defenses for graph neural networks robust? NeurIPS","author":"Mujkanovic Felix","year":"2022","unstructured":"Felix Mujkanovic, Simon Geisler, Stephan G\u00fcnnemann, and Aleksandar Bojchevski. 2022. Are defenses for graph neural networks robust? NeurIPS (2022)."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"crossref","unstructured":"Yu Rong Tingyang Xu Junzhou Huang Wenbing Huang Hong Cheng Yao Ma Yiqi Wang Tyler Derr Lingfei Wu and Tengfei Ma. 2020. Deep graph learning: Foundations advances and applications. In KDD.","DOI":"10.1145\/3394486.3406474"},{"key":"e_1_3_2_1_22_1","volume-title":"Xiaowen Dong, and Michael M Bronstein.","author":"Rossi Emanuele","year":"2022","unstructured":"Emanuele Rossi, Henry Kenlay, Maria I Gorinova, Benjamin Paul Chamberlain, Xiaowen Dong, and Michael M Bronstein. 2022. On the unreasonable effectiveness of feature propagation in learning on graphs with missing node features. In LOG."},{"key":"e_1_3_2_1_23_1","volume-title":"Cross-Domain Graph Data Scaling: A Showcase with Diffusion Models. arXiv preprint arXiv:2406.01899","author":"Tang Wenzhuo","year":"2024","unstructured":"Wenzhuo Tang, Haitao Mao, Danial Dervovic, Ivan Brugere, Saumitra Mishra, Yuying Xie, and Jiliang Tang. 2024. Cross-Domain Graph Data Scaling: A Showcase with Diffusion Models. arXiv preprint arXiv:2406.01899 (2024)."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"crossref","unstructured":"Xianfeng Tang Huaxiu Yao Yiwei Sun Yiqi Wang Jiliang Tang Charu Aggarwal Prasenjit Mitra and Suhang Wang. 2020. Investigating and mitigating degree-related biases in graph convoltuional networks. In CIKM.","DOI":"10.1145\/3340531.3411872"},{"key":"e_1_3_2_1_25_1","volume-title":"Graph neural networks: Self-supervised learning. Graph Neural Networks: Foundations, Frontiers, and Applications","author":"Wang Yu","year":"2022","unstructured":"Yu Wang, Wei Jin, and Tyler Derr. 2022. Graph neural networks: Self-supervised learning. Graph Neural Networks: Foundations, Frontiers, and Applications (2022)."},{"key":"e_1_3_2_1_26_1","doi-asserted-by":"crossref","unstructured":"Yu Wang Yuying Zhao Neil Shah and Tyler Derr. 2022. Imbalanced graph classification via graph-of-graph neural networks. In CIKM.","DOI":"10.1145\/3511808.3557356"},{"key":"e_1_3_2_1_27_1","volume-title":"Net: Degree-specific graph neural networks for node and graph classification. In KDD. 406--415.","author":"Wu Jun","year":"2019","unstructured":"Jun Wu, Jingrui He, and Jiejun Xu. 2019. Net: Degree-specific graph neural networks for node and graph classification. In KDD. 406--415."},{"key":"e_1_3_2_1_28_1","volume-title":"Kwei-Herng Lai, Fan Yang, Zhimeng Jiang, Shaochen Zhong, and Xia Hu.","author":"Zha Daochen","year":"2023","unstructured":"Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai, Fan Yang, Zhimeng Jiang, Shaochen Zhong, and Xia Hu. 2023. Data-centric artificial intelligence: A survey. arXiv preprint arXiv:2303.10158 (2023)."},{"key":"e_1_3_2_1_29_1","volume-title":"Graphsmote: Imbalanced node classification on graphs with graph neural networks. In WSDM.","author":"Zhao Tianxiang","year":"2021","unstructured":"Tianxiang Zhao, Xiang Zhang, and Suhang Wang. 2021. Graphsmote: Imbalanced node classification on graphs with graph neural networks. In WSDM."}],"event":{"name":"CIKM '24: The 33rd ACM International Conference on Information and Knowledge Management","sponsor":["SIGIR ACM Special Interest Group on Information Retrieval"],"location":"Boise ID USA","acronym":"CIKM '24"},"container-title":["Proceedings of the 33rd ACM International Conference on Information and Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679095","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3627673.3679095","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:03:28Z","timestamp":1750291408000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3627673.3679095"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,21]]},"references-count":29,"alternative-id":["10.1145\/3627673.3679095","10.1145\/3627673"],"URL":"https:\/\/doi.org\/10.1145\/3627673.3679095","relation":{},"subject":[],"published":{"date-parts":[[2024,10,21]]},"assertion":[{"value":"2024-10-21","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}