{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T01:10:20Z","timestamp":1755825020624,"version":"3.44.0"},"publisher-location":"New York, NY, USA","reference-count":43,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T00:00:00Z","timestamp":1715558400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/501100006374","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62302537"],"award-info":[{"award-number":["62302537"]}],"id":[{"id":"10.13039\/501100006374","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,5,13]]},"DOI":"10.1145\/3589334.3645571","type":"proceedings-article","created":{"date-parts":[[2024,5,8]],"date-time":"2024-05-08T07:08:13Z","timestamp":1715152093000},"page":"780-791","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Exploring Neural Scaling Law and Data Pruning Methods For Node Classification on Large-scale Graphs"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8140-8782","authenticated-orcid":false,"given":"Zhen","family":"Wang","sequence":"first","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4204-6096","authenticated-orcid":false,"given":"Yaliang","family":"Li","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1535-9692","authenticated-orcid":false,"given":"Bolin","family":"Ding","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1978-5544","authenticated-orcid":false,"given":"Yule","family":"Li","sequence":"additional","affiliation":[{"name":"Sun Yat-sen University, Guangzhou, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3620-5086","authenticated-orcid":false,"given":"Zhewei","family":"Wei","sequence":"additional","affiliation":[{"name":"Renmin University of China, Beijing, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,5,13]]},"reference":[{"key":"e_1_3_2_2_1_1","volume-title":"Vincent Wenchen Zheng, and Kevin Chen-Chuan Chang","author":"Cai HongYun","year":"2017","unstructured":"HongYun Cai, Vincent Wenchen Zheng, and Kevin Chen-Chuan Chang. 2017. Active Learning for Graph Embedding. CoRR (2017)."},{"key":"e_1_3_2_2_2_1","unstructured":"Ming Chen Zhewei Wei Bolin Ding Yaliang Li Ye Yuan Xiaoyong Du and Ji-Rong Wen. 2020. Scalable Graph Neural Networks via Bidirectional Propagation. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_3_1","volume-title":"Adaptive Universal Generalized PageRank Graph Neural Network. In International Conference on Learning Representations.","author":"Chien Eli","year":"2021","unstructured":"Eli Chien, Jianhao Peng, Pan Li, and Olgica Milenkovic. 2021. Adaptive Universal Generalized PageRank Graph Neural Network. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/TITS.2021.3133268"},{"key":"e_1_3_2_2_5_1","unstructured":"Vitaly Feldman and Chiyuan Zhang. 2020. What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_6_1","volume-title":"Combining Neural Networks with Personalized PageRank for Classification on Graphs. In International Conference on Learning Representations.","author":"Gasteiger Johannes","year":"2019","unstructured":"Johannes Gasteiger, Aleksandar Bojchevski, and Stephan G\u00fcnnemann. 2019. Combining Neural Networks with Personalized PageRank for Classification on Graphs. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_7_1","volume-title":"Clustering to minimize the maximum intercluster distance. Theoretical computer science","author":"Gonzalez Teofilo F","year":"1985","unstructured":"Teofilo F Gonzalez. 1985. Clustering to minimize the maximum intercluster distance. Theoretical computer science (1985), 293--306."},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/2021.emnlp-main.478"},{"key":"e_1_3_2_2_9_1","unstructured":"William L Hamilton Rex Ying and Jure Leskovec. 2017. Inductive representation learning on large graphs. In Advances in neural information processing systems."},{"key":"e_1_3_2_2_10_1","volume-title":"Scaling Laws for Autoregressive Generative Modeling. CoRR","author":"Henighan Tom","year":"2020","unstructured":"Tom Henighan, Jared Kaplan, Mor Katz, Mark Chen, Christopher Hesse, Jacob Jackson, Heewoo Jun, Tom B. Brown, Prafulla Dhariwal, Scott Gray, Chris Hallacy, Benjamin Mann, Alec Radford, Aditya Ramesh, Nick Ryder, Daniel M. Ziegler, John Schulman, Dario Amodei, and Sam McCandlish. 2020. Scaling Laws for Autoregressive Generative Modeling. CoRR, Vol. abs\/2010.14701 (2020). https:\/\/arxiv.org\/abs\/2010.14701"},{"key":"e_1_3_2_2_11_1","volume-title":"Scaling Laws for Transfer. CoRR","author":"Hernandez Danny","year":"2021","unstructured":"Danny Hernandez, Jared Kaplan, Tom Henighan, and Sam McCandlish. 2021. Scaling Laws for Transfer. CoRR, Vol. abs\/2102.01293 (2021). https:\/\/arxiv.org\/abs\/2102.01293"},{"key":"e_1_3_2_2_12_1","volume-title":"Yang Yang, and Yanqi Zhou.","author":"Hestness Joel","year":"2017","unstructured":"Joel Hestness, Sharan Narang, Newsha Ardalani, Gregory F. Diamos, Heewoo Jun, Hassan Kianinejad, Md. Mostofa Ali Patwary, Yang Yang, and Yanqi Zhou. 2017. Deep Learning Scaling is Predictable, Empirically. CoRR, Vol. abs\/1712.00409 (2017). http:\/\/arxiv.org\/abs\/1712.00409"},{"key":"e_1_3_2_2_13_1","unstructured":"Jordan Hoffmann Sebastian Borgeaud Arthur Mensch Elena Buchatskaya Trevor Cai Eliza Rutherford Diego de Las Casas Lisa Anne Hendricks Johannes Welbl Aidan Clark Tom Hennigan Eric Noland Katie Millican George van den Driessche Bogdan Damoc Aurelia Guy Simon Osindero Karen Simonyan Erich Elsen Jack W. Rae Oriol Vinyals and Laurent Sifre. 2022. Training Compute-Optimal Large Language Models. arxiv: 2203.15556 [cs.CL]"},{"key":"e_1_3_2_2_14_1","volume-title":"Open Graph Benchmark: Datasets for Machine Learning on Graphs. arXiv preprint arXiv:2005.00687","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. arXiv preprint arXiv:2005.00687 (2020)."},{"key":"e_1_3_2_2_15_1","volume-title":"Scaling Laws for Neural Language Models. CoRR","author":"Kaplan Jared","year":"2020","unstructured":"Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B. Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei. 2020. Scaling Laws for Neural Language Models. CoRR, Vol. abs\/2001.08361 (2020). https:\/\/arxiv.org\/abs\/2001.08361"},{"key":"e_1_3_2_2_16_1","volume-title":"The First Learning on Graphs Conference.","author":"Keriven Nicolas","year":"2022","unstructured":"Nicolas Keriven. 2022. Not too little, not too much: a theoretical analysis of graph (over)smoothing. In The First Learning on Graphs Conference."},{"key":"e_1_3_2_2_17_1","volume-title":"Kipf and Max Welling","author":"Thomas","year":"2016","unstructured":"Thomas N. Kipf and Max Welling. 2016. Variational Graph Auto-Encoders. https:\/\/arxiv.org\/abs\/1611.07308"},{"volume-title":"Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations.","author":"Thomas","key":"e_1_3_2_2_18_1","unstructured":"Thomas N. Kipf and Max Welling. 2017. Semi-Supervised Classification with Graph Convolutional Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_19_1","volume-title":"Proceedings of the 29th ACM International Conference on Information & Knowledge Management.","author":"Aggarwal Charu","year":"2020","unstructured":"liang, Charu Aggarwal, Prasenjit Mitra, and Suhang Wang. 2020. Investigating and Mitigating Degree-Related Biases in Graph Convoltuional Networks. In Proceedings of the 29th ACM International Conference on Information & Knowledge Management."},{"key":"e_1_3_2_2_20_1","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467142"},{"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","unstructured":"Sitao Luan Chenqing Hua Qincheng Lu Jiaqi Zhu Mingde Zhao Shuyuan Zhang Xiao-Wen Chang and Doina Precup. 2022. Revisiting Heterophily For Graph Neural Networks. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_23_1","unstructured":"Jiaqi Ma Junwei Deng and Qiaozhu Mei. 2021. Subgroup Generalization and Fairness of Graph Neural Networks. In Advances in Neural Information Processing Systems."},{"volume-title":"International Conference on Learning Representations.","author":"Meding Kristof","key":"e_1_3_2_2_24_1","unstructured":"Kristof Meding, Luca M. Schulze Buschoff, Robert Geirhos, and Felix A. Wichmann. 2022. Trivial or Impossible -- dichotomous data difficulty masks model differences (on ImageNet and beyond). In International Conference on Learning Representations."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.5555\/3524938.3525583"},{"key":"e_1_3_2_2_26_1","volume-title":"International Conference on Learning Representations.","author":"Neyshabur Behnam","year":"2018","unstructured":"Behnam Neyshabur, Srinadh Bhojanapalli, and Nathan Srebro. 2018. A PAC-Bayesian Approach to Spectrally-Normalized Margin Bounds for Neural Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_27_1","volume-title":"The Web Conference.","author":"Page Lawrence","year":"1999","unstructured":"Lawrence Page, Sergey Brin, Rajeev Motwani, and Terry Winograd. 1999. The PageRank Citation Ranking : Bringing Order to the Web. In The Web Conference."},{"key":"e_1_3_2_2_28_1","unstructured":"Mansheej Paul Surya Ganguli and Gintare Karolina Dziugaite. 2021. Deep Learning on a Data Diet: Finding Important Examples Early in Training. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_29_1","volume-title":"International Conference on Learning Representations.","author":"Rosenfeld Jonathan S.","year":"2020","unstructured":"Jonathan S. Rosenfeld, Amir Rosenfeld, Yonatan Belinkov, and Nir Shavit. 2020. A Constructive Prediction of the Generalization Error Across Scales. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_30_1","unstructured":"Ben Sorscher Robert Geirhos Shashank Shekhar Surya Ganguli and Ari Morcos. 2022. Beyond neural scaling laws: beating power law scaling via data pruning. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"crossref","unstructured":"Jiliang Tang Charu C. Aggarwal and Huan Liu. 2016. Node Classification in Signed Social Networks. In SDM.","DOI":"10.1137\/1.9781611974348.7"},{"key":"e_1_3_2_2_32_1","volume":"201","author":"Toneva Mariya","unstructured":"Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J. Gordon. 2019. An Empirical Study of Example Forgetting during Deep Neural Network Learning. In International Conference on Learning Representations.","journal-title":"Geoffrey J. Gordon."},{"key":"e_1_3_2_2_33_1","volume-title":"Graph Attention Networks. In International Conference on Learning Representations.","author":"Petar Velivc","year":"2018","unstructured":"Petar Velivc kovi\u0107, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li\u00f2, and Yoshua Bengio. 2018. Graph Attention Networks. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_34_1","volume-title":"Proceedings of the 36th International Conference on Machine Learning. 6861--6871","author":"Wu Felix","year":"2019","unstructured":"Felix Wu, Amauri Souza, Tianyi Zhang, Christopher Fifty, Tao Yu, and Kilian Weinberger. 2019. Simplifying Graph Convolutional Networks. In Proceedings of the 36th International Conference on Machine Learning. 6861--6871."},{"key":"e_1_3_2_2_35_1","volume-title":"The Eleventh International Conference on Learning Representations.","author":"Wu Xinyi","year":"2023","unstructured":"Xinyi Wu, Zhengdao Chen, William Wei Wang, and Ali Jadbabaie. 2023. A Non-Asymptotic Analysis of Oversmoothing in Graph Neural Networks. In The Eleventh International Conference on Learning Representations."},{"key":"e_1_3_2_2_36_1","volume-title":"Active Learning for Graph Neural Networks via Node Feature Propagation. arxiv","author":"Wu Yuexin","year":"1910","unstructured":"Yuexin Wu, Yichong Xu, Aarti Singh, Yiming Yang, and Artur Dubrawski. 2021. Active Learning for Graph Neural Networks via Node Feature Propagation. arxiv: 1910.07567 [cs.LG]"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3580305.3599510"},{"key":"e_1_3_2_2_38_1","volume-title":"Proceedings of the 38th International Conference on Machine Learning. 11592--11602","author":"Xu Keyulu","year":"2021","unstructured":"Keyulu Xu, Mozhi Zhang, Stefanie Jegelka, and Kenji Kawaguchi. 2021. Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth. In Proceedings of the 38th International Conference on Machine Learning. 11592--11602."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR52688.2022.01179"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1145\/3543507.3583530"},{"volume-title":"Proceedings of the AAAI Conference on Artificial Intelligence ([n.,d.]).","author":"Zhang Yuheng","key":"e_1_3_2_2_41_1","unstructured":"Yuheng Zhang, Hanghang Tong, Yinglong Xia, Yan Zhu, Yuejie Chi, and Lei Ying. [n.,d.]. Batch Active Learning with Graph Neural Networks via Multi-Agent Deep Reinforcement Learning. Proceedings of the AAAI Conference on Artificial Intelligence ([n.,d.])."},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v35i12.17332"},{"key":"e_1_3_2_2_43_1","volume-title":"Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs. Advances in Neural Information Processing Systems","author":"Zhu Jiong","year":"2020","unstructured":"Jiong Zhu, Yujun Yan, Lingxiao Zhao, Mark Heimann, Leman Akoglu, and Danai Koutra. 2020. Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs. Advances in Neural Information Processing Systems (2020)."}],"event":{"name":"WWW '24: The ACM Web Conference 2024","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web"],"location":"Singapore Singapore","acronym":"WWW '24"},"container-title":["Proceedings of the ACM Web Conference 2024"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589334.3645571","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3589334.3645571","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,22]],"date-time":"2025-08-22T00:36:38Z","timestamp":1755822998000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3589334.3645571"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,13]]},"references-count":43,"alternative-id":["10.1145\/3589334.3645571","10.1145\/3589334"],"URL":"https:\/\/doi.org\/10.1145\/3589334.3645571","relation":{},"subject":[],"published":{"date-parts":[[2024,5,13]]},"assertion":[{"value":"2024-05-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}