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This paper analyzes the effect of structural and temporal heterogeneities in decentralized federated learning over temporal networks, showing that ignoring these heterogeneities leads to unrealistically rapid convergence and that real-world networks slow down diffusion.
This paper proposes a modular temporal enhancement framework for signed graph neural networks that integrates historical context via a Historical Context Integration Module (HCIM) with LSTM and multi-head temporal attention, achieving consistent improvements on real-world temporal signed networks for dynamic link prediction.