A PyTorch implementation of Graph Classification Using Structural Attention
GAM
A PyTorch implementation of Graph Classification Using Structural Attention (KDD 2018).
Abstract
Graph classification is a problem with practical applications in many different domains. To solve this problem, one usually calculates certain graph statistics (i.e., graph features) that help discriminate between graphs of different classes. When calculating such features, most existing approaches process the entire graph. In a graphlet-based approach, for instance, the entire graph is processed to get the total count of different graphlets or subgraphs. In many real-world applications, however, graphs can be noisy with discriminative patterns confined to certain regions in the graph only. In this work, we study the problem of attention-based graph classification . The use of attention allows us to focus on small but informative parts of the graph,