The 3rd Workshop on Graph Learning Benchmarks (GLB 2023)
Authors: 
Jiaqi Ma, Jiong Zhu, Yuxiao Dong, Danai Koutra, 
Jingrui He, Qiaozhu Mei, Anton Tsitsulin, Xingjian Zhang, Marinka ZitnikAuthors Info & Claims
Jiaqi Ma, Jiong Zhu, Yuxiao Dong, Danai Koutra, Jingrui He, Qiaozhu Mei, Anton Tsitsulin, Xingjian Zhang, Marinka ZitnikAuthors Info & ClaimsPages 5870 - 5871
Abstract
Recent years have witnessed a surge of research interest in graph machine learning. However, the benchmark datasets available to the field are rather limited in both quantity and diversity, an issue particularly notable given the immense potential applications of graph learning. The lack of diverse benchmark datasets may have biased the development of graph machine learning techniques towards narrow directions. By crowdsourcing novel tasks and datasets, this workshop aims to increase the diversity of graph learning benchmarks, identify new demands of graph machine learning in general, and gain a better synergy of how concrete techniques perform on these benchmarks. Moreover, this workshop offers a platform for discussions of best practices in curating graph learning benchmarks and data-centric approaches for graph learning.
References
[1]
Emmanuel Abbe. 2017. Community detection and stochastic block models: recent developments. The Journal of Machine Learning Research, Vol. 18, 1 (2017), 6446--6531.
[2]
Yuxiao Dong, Jie Tang, Sen Wu, Jilei Tian, Nitesh V Chawla, Jinghai Rao, and Huanhuan Cao. 2012. Link prediction and recommendation across heterogeneous social networks. In 2012 IEEE 12th International conference on data mining. IEEE, 181--190.
[3]
Junteng Jia and Austion R Benson. 2020. Residual Correlation in Graph Neural Network Regression. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 588--598.
[4]
Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).
[5]
Qimai Li, Zhichao Han, and Xiao-Ming Wu. 2018. Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning. In Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18). Association for the Advancement of Artificial Intelligence, 3538--3545.
[6]
Jiaqi Ma, Bo Chang, Xuefei Zhang, and Qiaozhu Mei. 2020. CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks. arXiv preprint arXiv:2010.02089 (2020).
[7]
Jiaqi Ma, Weijing Tang, Ji Zhu, and Qiaozhu Mei. 2019. A Flexible Generative Framework for Graph-based Semi-supervised Learning. In Advances in Neural Information Processing Systems. 3281--3290.
[8]
Christopher Morris, Martin Ritzert, Matthias Fey, William L Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe. 2019. Weisfeiler and leman go neural: Higher-order graph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence, Vol. 33. 4602--4609.
[9]
Hoang NT and Takanori Maehara. 2019. Revisiting graph neural networks: All we have is low-pass filters. arXiv preprint arXiv:1905.09550 (2019).
[10]
Meng Qu, Yoshua Bengio, and Jian Tang. 2019. GMNN: Graph Markov Neural Networks. In International Conference on Machine Learning. 5241--5250.
[11]
Yu Rong, Wenbing Huang, Tingyang Xu, and Junzhou Huang. 2019. Dropedge: Towards deep graph convolutional networks on node classification. In International Conference on Learning Representations.
[12]
Yiye Ruan, David Fuhry, and Srinivasan Parthasarathy. 2013. Efficient community detection in large networks using content and links. In Proceedings of the 22nd international conference on World Wide Web. 1089--1098.
[13]
Keyulu Xu, Weihua Hu, Jure Leskovec, and Stefanie Jegelka. 2018. How Powerful are Graph Neural Networks?. In International Conference on Learning Representations.
[14]
Bing Yu, Haoteng Yin, and Zhanxing Zhu. 2017. Spatio-temporal graph convolutional networks: A deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875 (2017).
[15]
Lingxiao Zhao and Leman Akoglu. 2019. PairNorm: Tackling Oversmoothing in GNNs. In International Conference on Learning Representations.
[16]
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, Vol. 33 (2020). io
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Information & Contributors
Information
Published In
August 2023
5996 pages
ISBN:9798400701030
DOI:10.1145/3580305
- General Chairs:
- Ambuj Singh,
- Yizhou Sun,
- Program Chairs:
- Leman Akoglu,
- Dimitrios Gunopulos,
- Xifeng Yan,
- Ravi Kumar,
- Fatma Ozcan,
- Jieping Ye
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Association for Computing Machinery
New York, NY, United States
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Published: 04 August 2023
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KDD '23: The 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
August 6 - 10, 2023
CA, Long Beach, USA
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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%
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