AI RESEARCH
Learning from Historical Activations in Graph Neural Networks
arXiv CS.LG
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ArXi:2601.01123v2 Announce Type: replace Graph Neural Networks (GNNs) have nstrated remarkable success in various domains such as social networks, molecular chemistry, and more. A crucial component of GNNs is the pooling procedure, in which the node features calculated by the model are combined to form an informative final descriptor to be used for the downstream task.