GSLB: The Graph Structure Learning Benchmark Article Swipe
Zhixun Li
,
Liang Wang
,
Xin Sun
,
Yifan Luo
,
Yanqiao Zhu
,
Dingshuo Chen
,
Yingtao Luo
,
Xiangxin Zhou
,
Qiang Liu
,
Shu Wu
,
Liang Wang
,
Jeffrey Xu Yu
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.05174
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2310.05174
Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://github.com/GSL-Benchmark/GSLB.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2310.05174
- https://arxiv.org/pdf/2310.05174
- OA Status
- green
- Cited By
- 3
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4387559766
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4387559766Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2310.05174Digital Object Identifier
- Title
-
GSLB: The Graph Structure Learning BenchmarkWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-10-08Full publication date if available
- Authors
-
Zhixun Li, Liang Wang, Xin Sun, Yifan Luo, Yanqiao Zhu, Dingshuo Chen, Yingtao Luo, Xiangxin Zhou, Qiang Liu, Shu Wu, Liang Wang, Jeffrey Xu YuList of authors in order
- Landing page
-
https://arxiv.org/abs/2310.05174Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2310.05174Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2310.05174Direct OA link when available
- Concepts
-
Computer science, Benchmark (surveying), Machine learning, Graph, Robustness (evolution), Artificial intelligence, Theoretical computer science, Biochemistry, Chemistry, Geography, Gene, GeodesyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 2, 2024: 1Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.offering | 174 |
| abstract_inverted_index.optimize | 14 |
| abstract_inverted_index.progress | 59 |
| abstract_inverted_index.recently | 5 |
| abstract_inverted_index.standard | 42 |
| abstract_inverted_index.Benchmark | 84 |
| abstract_inverted_index.Empirical | 154 |
| abstract_inverted_index.Structure | 1, 82 |
| abstract_inverted_index.attention | 8 |
| abstract_inverted_index.available | 186 |
| abstract_inverted_index.developed | 35, 142 |
| abstract_inverted_index.different | 75, 151 |
| abstract_inverted_index.extensive | 158 |
| abstract_inverted_index.potential | 168 |
| abstract_inverted_index.research, | 139 |
| abstract_inverted_index.research. | 180 |
| abstract_inverted_index.scenarios | 76 |
| abstract_inverted_index.structure | 27 |
| abstract_inverted_index.training, | 147 |
| abstract_inverted_index.algorithms | 119 |
| abstract_inverted_index.comparison | 47 |
| abstract_inverted_index.downstream | 172 |
| abstract_inverted_index.facilitate | 137 |
| abstract_inverted_index.parameters | 17 |
| abstract_inverted_index.algorithms. | 96 |
| abstract_inverted_index.complexity. | 113, 134 |
| abstract_inverted_index.computation | 25 |
| abstract_inverted_index.demonstrate | 160 |
| abstract_inverted_index.dimensions: | 109 |
| abstract_inverted_index.easy-to-use | 144 |
| abstract_inverted_index.evaluating, | 148 |
| abstract_inverted_index.evaluation, | 50 |
| abstract_inverted_index.experiments | 159 |
| abstract_inverted_index.graph-level | 123 |
| abstract_inverted_index.performance | 49, 71, 128 |
| abstract_inverted_index.robustness, | 111 |
| abstract_inverted_index.visualizing | 150 |
| abstract_inverted_index.considerable | 7 |
| abstract_inverted_index.experimental | 43 |
| abstract_inverted_index.investigates | 100 |
| abstract_inverted_index.reproducible | 138 |
| abstract_inverted_index.Specifically, | 97 |
| abstract_inverted_index.comprehensive | 80 |
| abstract_inverted_index.opportunities | 177 |
| abstract_inverted_index.proliferation | 31 |
| abstract_inverted_index.understanding | 57 |
| abstract_inverted_index.effectiveness, | 110 |
| abstract_inverted_index.systematically | 68, 99 |
| abstract_inverted_index.characteristics | 102 |
| abstract_inverted_index.comprehensively | 115 |
| abstract_inverted_index.simultaneously. | 28 |
| abstract_inverted_index.state-of-the-art | 117 |
| abstract_inverted_index.https://github.com/GSL-Benchmark/GSLB. | 188 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 12 |
| citation_normalized_percentile |