Temporal Graph Benchmark for Machine Learning on Temporal Graphs Article Swipe
Shenyang Huang
,
Farimah Poursafaei
,
Jacob Danovitch
,
Matthias Fey
,
Weihua Hu
,
Emanuele Rossi
,
Jure Leskovec
,
Michael M. Bronstein
,
Guillaume Rabusseau
,
Reihaneh Rabbany
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.01026
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2307.01026
We present the Temporal Graph Benchmark (TGB), a collection of challenging and diverse benchmark datasets for realistic, reproducible, and robust evaluation of machine learning models on temporal graphs. TGB datasets are of large scale, spanning years in duration, incorporate both node and edge-level prediction tasks and cover a diverse set of domains including social, trade, transaction, and transportation networks. For both tasks, we design evaluation protocols based on realistic use-cases. We extensively benchmark each dataset and find that the performance of common models can vary drastically across datasets. In addition, on dynamic node property prediction tasks, we show that simple methods often achieve superior performance compared to existing temporal graph models. We believe that these findings open up opportunities for future research on temporal graphs. Finally, TGB provides an automated machine learning pipeline for reproducible and accessible temporal graph research, including data loading, experiment setup and performance evaluation. TGB will be maintained and updated on a regular basis and welcomes community feedback. TGB datasets, data loaders, example codes, evaluation setup, and leaderboards are publicly available at https://tgb.complexdatalab.com/.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2307.01026
- https://arxiv.org/pdf/2307.01026
- OA Status
- green
- Cited By
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4383180432
All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4383180432Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2307.01026Digital Object Identifier
- Title
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Temporal Graph Benchmark for Machine Learning on Temporal GraphsWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-07-03Full publication date if available
- Authors
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Shenyang Huang, Farimah Poursafaei, Jacob Danovitch, Matthias Fey, Weihua Hu, Emanuele Rossi, Jure Leskovec, Michael M. Bronstein, Guillaume Rabusseau, Reihaneh RabbanyList of authors in order
- Landing page
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https://arxiv.org/abs/2307.01026Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2307.01026Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2307.01026Direct OA link when available
- Concepts
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Computer science, Benchmark (surveying), Graph, Machine learning, Pipeline (software), Artificial intelligence, Data mining, Set (abstract data type), Database transaction, Benchmarking, Theoretical computer science, Database, Business, Geodesy, Marketing, Programming language, GeographyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
23Total citation count in OpenAlex
- Citations by year (recent)
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2025: 8, 2024: 12, 2023: 3Per-year citation counts (last 5 years)
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(TGB), | 6 |
| abstract_inverted_index.across | 86 |
| abstract_inverted_index.codes, | 167 |
| abstract_inverted_index.common | 81 |
| abstract_inverted_index.design | 63 |
| abstract_inverted_index.future | 120 |
| abstract_inverted_index.models | 24, 82 |
| abstract_inverted_index.robust | 19 |
| abstract_inverted_index.scale, | 33 |
| abstract_inverted_index.setup, | 169 |
| abstract_inverted_index.simple | 99 |
| abstract_inverted_index.tasks, | 61, 95 |
| abstract_inverted_index.trade, | 54 |
| abstract_inverted_index.achieve | 102 |
| abstract_inverted_index.believe | 112 |
| abstract_inverted_index.dataset | 74 |
| abstract_inverted_index.diverse | 12, 48 |
| abstract_inverted_index.domains | 51 |
| abstract_inverted_index.dynamic | 91 |
| abstract_inverted_index.example | 166 |
| abstract_inverted_index.graphs. | 27, 124 |
| abstract_inverted_index.machine | 22, 130 |
| abstract_inverted_index.methods | 100 |
| abstract_inverted_index.models. | 110 |
| abstract_inverted_index.present | 1 |
| abstract_inverted_index.regular | 156 |
| abstract_inverted_index.social, | 53 |
| abstract_inverted_index.updated | 153 |
| abstract_inverted_index.Finally, | 125 |
| abstract_inverted_index.Temporal | 3 |
| abstract_inverted_index.compared | 105 |
| abstract_inverted_index.datasets | 14, 29 |
| abstract_inverted_index.existing | 107 |
| abstract_inverted_index.findings | 115 |
| abstract_inverted_index.learning | 23, 131 |
| abstract_inverted_index.loaders, | 165 |
| abstract_inverted_index.loading, | 142 |
| abstract_inverted_index.pipeline | 132 |
| abstract_inverted_index.property | 93 |
| abstract_inverted_index.provides | 127 |
| abstract_inverted_index.publicly | 173 |
| abstract_inverted_index.research | 121 |
| abstract_inverted_index.spanning | 34 |
| abstract_inverted_index.superior | 103 |
| abstract_inverted_index.temporal | 26, 108, 123, 137 |
| abstract_inverted_index.welcomes | 159 |
| abstract_inverted_index.Benchmark | 5 |
| abstract_inverted_index.addition, | 89 |
| abstract_inverted_index.automated | 129 |
| abstract_inverted_index.available | 174 |
| abstract_inverted_index.benchmark | 13, 72 |
| abstract_inverted_index.community | 160 |
| abstract_inverted_index.datasets, | 163 |
| abstract_inverted_index.datasets. | 87 |
| abstract_inverted_index.duration, | 37 |
| abstract_inverted_index.feedback. | 161 |
| abstract_inverted_index.including | 52, 140 |
| abstract_inverted_index.networks. | 58 |
| abstract_inverted_index.protocols | 65 |
| abstract_inverted_index.realistic | 68 |
| abstract_inverted_index.research, | 139 |
| abstract_inverted_index.accessible | 136 |
| abstract_inverted_index.collection | 8 |
| abstract_inverted_index.edge-level | 42 |
| abstract_inverted_index.evaluation | 20, 64, 168 |
| abstract_inverted_index.experiment | 143 |
| abstract_inverted_index.maintained | 151 |
| abstract_inverted_index.prediction | 43, 94 |
| abstract_inverted_index.realistic, | 16 |
| abstract_inverted_index.use-cases. | 69 |
| abstract_inverted_index.challenging | 10 |
| abstract_inverted_index.drastically | 85 |
| abstract_inverted_index.evaluation. | 147 |
| abstract_inverted_index.extensively | 71 |
| abstract_inverted_index.incorporate | 38 |
| abstract_inverted_index.performance | 79, 104, 146 |
| abstract_inverted_index.leaderboards | 171 |
| abstract_inverted_index.reproducible | 134 |
| abstract_inverted_index.transaction, | 55 |
| abstract_inverted_index.opportunities | 118 |
| abstract_inverted_index.reproducible, | 17 |
| abstract_inverted_index.transportation | 57 |
| abstract_inverted_index.https://tgb.complexdatalab.com/. | 176 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 10 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/17 |
| sustainable_development_goals[0].score | 0.41999998688697815 |
| sustainable_development_goals[0].display_name | Partnerships for the goals |
| citation_normalized_percentile |