Learning from Historical Activations in Graph Neural Networks

In three linesHISTOGRAPH, an attention-based final aggregation layer, leverages intermediate activations from previous GNN layers. The method applies layer-wise attention followed by node-wise attention to model representation evolution. Improved results on graph classification benchmarks with enhanced robustness in deep GNNs.

Summary generated by Claude — human-verified