Summary of Tgb 2.0: a Benchmark For Learning on Temporal Knowledge Graphs and Heterogeneous Graphs, by Julia Gastinger et al.
TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs
by Julia Gastinger, Shenyang Huang, Mikhail Galkin, Erfan Loghmani, Ali Parviz, Farimah Poursafaei, Jacob Danovitch, Emanuele Rossi, Ioannis Koutis, Heiner Stuckenschmidt, Reihaneh Rabbany, Guillaume Rabusseau
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel benchmarking framework, Temporal Graph Benchmark 2.0 (TGB 2.0), is introduced for evaluating methods predicting future links on Temporal Knowledge Graphs and Temporal Heterogeneous Graphs. The framework includes eight datasets spanning five domains with up to 53 million edges, significantly larger than existing datasets in terms of node, edge, or timestamp counts. TGB 2.0 provides a reproducible evaluation pipeline for multi-relational temporal graphs, facilitating comprehensive evaluations. Experimental results show that leveraging edge-type information is crucial for high performance, simple heuristic baselines are competitive with more complex methods, and most methods fail to run on the largest datasets, highlighting the need for research on scalable methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to test how well computers can understand changing relationships between things is introduced. This helps scientists study big groups of connected data that keep growing over time. The new tool has many different sets of data from five areas and contains a huge number of connections. It also lets scientists see what works best for understanding this kind of data. |
