Pathfinder Discovery Networks for Neural Message Passing

Understand

In this work we propose Pathfinder Discovery Networks (PDNs), a method for jointly learning a message passing graph over a multiplex network with a downstream semi-supervised model.

• PDNs inductively learn an aggregated weight for each edge, optimized to produce the best outcome for the downstream learning task.
• PDNs are a generalization of attention mechanisms on graphs which allow flexible construction of similarity functions between nodes, edge convolutions, and cheap multiscale mixing layers.
• We show that PDNs overcome weaknesses of existing methods for graph attention (e.g.