Predictive Dynamic Graph World Modelling: From Temporal Context to Future Relations

Dynamic graph learning models how entities and their relations evolve from historical observations to predict future graph states. Existing methods do not fully address dynamic graph world modelling, which requires learning transitions from temporal context to future latent graph states. This is difficult for three reasons: nodes and relations rely on different historical evidence, structural evolution is path-dependent, and future snapshots provide sparse and realization-specific targets. We propose **Dynamic Graph World Modelling (DYG-WM)**, the first framework to jointly predict future node and relation representations from temporal context by answering three questions: *what has evolved, how it evolved, and what will evolve next*. To capture (a) *what has evolved*, a historical node-relation context encoder summarizes node-neighborhood trajectories and pair-specific histories. To characterize (b) *how it evolved*, a node-relation path-signature encoder captures ordered structural changes. To predict (c) *what will evolve next*, a dual-level joint-embedding objective predicts target-snapshot node and relation representations without reconstructing the raw graph. Experiments on 12 link-prediction and two node-classification datasets demonstrate the effectiveness of **DYG-WM** across both tasks.