Jumping knowledge networks let each node combine embeddings from every layer, so the effective neighborhood size can vary across the graph. I explain the influence distribution argument and the LSTM attention aggregator. Then I apply a jumping knowledge GAT to cortical segmentation, where it holds up better than a plain GAT as depth increases.