Dynamic Graph World Modelling: Learning Future Dynamics from Temporal Context
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 rea...
Bo Li, Yue Yang, Yu-Jin Huang et al.
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