Abstract
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. SPAGAN therefore allows for a more informative and intact exploration of the graph structure and further a more effective aggregation of information from distant neighbors into the center
node, as compared to node-based GCN methods.
We test SPAGAN on the downstream classification
task on several standard datasets, and achieve performances superior to the state of the art