Deep Graph Attention Model
This addresses graph classification for domains with large, noisy graphs, but it appears incremental as it builds on existing attention and RNN methods.
The paper tackles graph classification by proposing a Graph Attention Model (GAM) that uses attention to focus on informative parts of graphs, avoiding noise, and demonstrates its effectiveness through experiments.
Graph classification is a problem with practical applications in many different domains. Most of the existing methods take the entire graph into account when calculating graph features. In a graphlet-based approach, for instance, the entire graph is processed to get the total count of different graphlets or sub-graphs. In the real-world, however, graphs can be both large and noisy with discriminative patterns confined to certain regions in the graph only. In this work, we study the problem of attentional processing for graph classification. The use of attention allows us to focus on small but informative parts of the graph, avoiding noise in the rest of the graph. We present a novel RNN model, called the Graph Attention Model (GAM), that processes only a portion of the graph by adaptively selecting a sequence of "interesting" nodes. The model is equipped with an external memory component which allows it to integrate information gathered from different parts of the graph. We demonstrate the effectiveness of the model through various experiments.