Efficient Dynamic Algorithms for Graph Neural Networks with Non-Linear Propagation
Graph Neural Networks (GNNs) are widely used for representation learning on graphs, but most methods assume static topologies, making them inefficient on evolving networks where edges change over time. Existing dynamic approaches either model graph evolution through temporal GNN architectures without focusing on effici...
Kiarash Banihashem, Mohammadtaghi Hajiaghayi, Mahdi JafariRaviz et al.
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