Accurate learning of graph representation with the consideration of fuzzy overlapping community

Abstract

Graph classification task plays a crucial role in many practical applications, for which the model is required to be capable of learning an accurate representation with discriminative characteristics, but existing methods typically fail to exploit the most authentic substructures for better representation. Aiming at this limitation, a fuzzy overlapping community guided subgraph neural network is proposed in this paper to make the best use of graph latent topologies for fine-grained representation learning and discriminative feature extraction, namely FS-GRAL. Taking multi-functional elements and their preferences fully into consideration, FS-GRAL allows the nodes to appear in more than one subgraph with different contributions for message passing, so that the model is powerful enough in capturing those natural aggregations inherent in complex networks to achieve accurate representation learning. Meanwhile, on the basis of learned accurate representations, a two-level pooling strategy dominated by intra-subgraph node discarding is presented for feature extraction, which enables graph latent topology can be integrated into extracted discriminative features, and thus the coarsened graph is more representative for graph classification. Extensive experiments on real-world benchmarks demonstrate that our fuzzy overlapping community guided subgraph neural network is highly competitive in learning the accurate graph representation and its intra-subgraph dominated two-level pooling strategy promotes a more promising classification performance.