Sumedh B G

Kernelized Edge Attention: Addressing Semantic Attention Blurring in Temporal Graph Neural Networks

Temporal Graph Neural Networks (TGNNs) aim to capture the evolving structure and timing of interactions in dynamic graphs. Although many models incorporate time through encodings or architectural design, they often compute attention over entangled node and edge representations, failing to reflect th