@inproceedings{6c4f2bf7880a4921a2ad41459d3f23fa,
title = "The 3rd Workshop on Graph Learning Benchmarks (GLB 2023)",
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.",
keywords = "benchmarks, data-centric ai, datasets, graph machine learning",
author = "Jiaqi Ma and Jiong Zhu and Yuxiao Dong and Danai Koutra and Jingrui He and Qiaozhu Mei and Anton Tsitsulin and Xingjian Zhang and Marinka Zitnik",
note = "Publisher Copyright: {\textcopyright} 2023 Owner/Author.; 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2023 ; Conference date: 06-08-2023 Through 10-08-2023",
year = "2023",
month = aug,
day = "6",
doi = "10.1145/3580305.3599224",
language = "English (US)",
series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
publisher = "Association for Computing Machinery",
pages = "5870--5871",
booktitle = "KDD 2023 - Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining",
address = "United States",
}