Tgb 2.0: A benchmark for learning on temporal knowledge graphs and heterogeneous graphs

J Gastinger, S Huang, M Galkin, E Loghmani… - arxiv preprint arxiv …, 2024 - arxiv.org
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 …

From Link Prediction to Forecasting: Information Loss in Batch-based Temporal Graph Learning

M Lampert, C Blöcker, I Scholtes - arxiv preprint arxiv:2406.04897, 2024 - arxiv.org
Dynamic link prediction is an important problem considered by many recent works
proposing various approaches for learning temporal edge patterns. To assess their efficacy …

[PDF][PDF] Enhancing Cross-domain Link Prediction via Evolution Process Modeling

X Huang, W Chow, Y Zhu, Y Wang, Z Chai… - THE WEB …, 2025 - yangy.org
Dynamic graphs are widespread in the real world [5, 47], their nodes representing entities
and dynamic edges denoting complex interactions between them [20]. For example, in …