A Deep Hybrid Pooling Architecture for Graph Classification with Hierarchical Attention

Sambaran Bandyopadhyay, Manasvi Aggarwal, M. Narasimha Murty

Abstract: Graph classification has been a classical problem of interest in machine learning and data mining because of its role in biological and social network analysis. Due to the recent success of graph neural networks for node classification and representation, researchers started extending them for the entire graph classification purpose. The main challenge is to represent the whole graph by a single vector which can be used to classify the graph in an end-to-end fashion. Global pooling, where node representations are directly aggregated to form the graph representation and more recently hierarchical pooling, where the whole graph is converted to a smaller graph through a set of hierarchies, are proposed in the literature. Though hierarchical pooling shows promising results for graph classification, it looses a significant amount of information in the hierarchical architecture. To address this, we propose a novel hybrid graph pooling architecture, which finds the importance of different hierarchies of pooling and aggregates them accordingly. We use a series of graph isomorphism networks, along with a bi-directional LSTM with self attention to implement the proposed hybrid pooling. Experiments show the merit of the proposed architecture with respect to a diverse set of state-of-the-art algorithms on multiple datasets.