  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.
