Graph Neural Networks, Transformers, Machine Learning, Artificial Intelligence, Graph Processing.
power than SOTA GNNs while also reducing computation burdens. The paper Graph Kernel Attention Transformers starts by posing the question: “Is it possible to design GNNs with densified individual layers modelling explicitly longer range node-to-node relationships in a graph, that enable more