SPAGAN: Shortest Path Graph Attention Network

Understand

Graph convolutional networks (GCN) have recently demonstrated their potential in analyzing non-grid structure data that can be represented as graphs.

• The core idea is to encode the local topology of a graph, via convolutions, into the feature of a center node.
• In this paper, we propose a novel GCN model, which we term as Shortest Path Graph Attention Network (SPAGAN).
• Unlike conventional GCN models that carry out node-based attentions within each layer, the proposed SPAGAN conducts path-based attention that explicitly accounts for the influence of a sequence of nodes yielding the minimum cost, or shortest path, between the center node and its higher-order neighbors.