  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. Code is publicly available at
https://github.com/ihollywhy/SPAGAN.
