TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866929548840402944 |
|---|---|
| author | Gastinger, Julia Huang, Shenyang Galkin, Mikhail Loghmani, Erfan Parviz, Ali Poursafaei, Farimah Danovitch, Jacob Rossi, Emanuele Koutis, Ioannis Stuckenschmidt, Heiner Rabbany, Reihaneh Rabusseau, Guillaume |
| author_facet | Gastinger, Julia Huang, Shenyang Galkin, Mikhail Loghmani, Erfan Parviz, Ali Poursafaei, Farimah Danovitch, Jacob Rossi, Emanuele Koutis, Ioannis Stuckenschmidt, Heiner Rabbany, Reihaneh Rabusseau, Guillaume |
| contents | Multi-relational temporal graphs are powerful tools for modeling real-world data, capturing the evolving and interconnected nature of entities over time. Recently, many novel models are proposed for ML on such graphs intensifying the need for robust evaluation and standardized benchmark datasets. However, the availability of such resources remains scarce and evaluation faces added complexity due to reproducibility issues in experimental protocols. To address these challenges, we introduce Temporal Graph Benchmark 2.0 (TGB 2.0), a novel benchmarking framework tailored for evaluating methods for predicting future links on Temporal Knowledge Graphs and Temporal Heterogeneous Graphs with a focus on large-scale datasets, extending the Temporal Graph Benchmark. TGB 2.0 facilitates comprehensive evaluations by presenting eight novel datasets spanning five domains with up to 53 million edges. TGB 2.0 datasets are significantly larger than existing datasets in terms of number of nodes, edges, or timestamps. In addition, TGB 2.0 provides a reproducible and realistic evaluation pipeline for multi-relational temporal graphs. Through extensive experimentation, we observe that 1) leveraging edge-type information is crucial to obtain high performance, 2) simple heuristic baselines are often competitive with more complex methods, 3) most methods fail to run on our largest datasets, highlighting the need for research on more scalable methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_09639 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs Gastinger, Julia Huang, Shenyang Galkin, Mikhail Loghmani, Erfan Parviz, Ali Poursafaei, Farimah Danovitch, Jacob Rossi, Emanuele Koutis, Ioannis Stuckenschmidt, Heiner Rabbany, Reihaneh Rabusseau, Guillaume Machine Learning Social and Information Networks Multi-relational temporal graphs are powerful tools for modeling real-world data, capturing the evolving and interconnected nature of entities over time. Recently, many novel models are proposed for ML on such graphs intensifying the need for robust evaluation and standardized benchmark datasets. However, the availability of such resources remains scarce and evaluation faces added complexity due to reproducibility issues in experimental protocols. To address these challenges, we introduce Temporal Graph Benchmark 2.0 (TGB 2.0), a novel benchmarking framework tailored for evaluating methods for predicting future links on Temporal Knowledge Graphs and Temporal Heterogeneous Graphs with a focus on large-scale datasets, extending the Temporal Graph Benchmark. TGB 2.0 facilitates comprehensive evaluations by presenting eight novel datasets spanning five domains with up to 53 million edges. TGB 2.0 datasets are significantly larger than existing datasets in terms of number of nodes, edges, or timestamps. In addition, TGB 2.0 provides a reproducible and realistic evaluation pipeline for multi-relational temporal graphs. Through extensive experimentation, we observe that 1) leveraging edge-type information is crucial to obtain high performance, 2) simple heuristic baselines are often competitive with more complex methods, 3) most methods fail to run on our largest datasets, highlighting the need for research on more scalable methods. |
| title | TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs |
| topic | Machine Learning Social and Information Networks |
| url | https://arxiv.org/abs/2406.09639 |