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Jacob Danovitch

2 accepted papers

2024

TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs

NeurIPS 2024poster

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. Ho…

Cited by 8SourcePDFScholar
2023

Temporal Graph Benchmark for Machine Learning on Temporal Graphs

NeurIPS 2023poster

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-leve…

Cited by 120SourcePDFScholar