Attributed Labeled BTER-Based Generative Model for Benchmarking of Graph Neural Networks

Abstract Graph Neural Networks (GNNs) have become increasingly popular for tasks such as link prediction, node classification, and graph generation. However, a number of models show weak performance on graphs with low assortativity measure. At the same time, other graph characteristics may also influence GNN quality. Therefore, it is extremely important for benchmark datasets to cover a wide range of different graph properties, which can not be provided by real-world sources. In this paper, we present a generative model for attributed graphs based on Block Two-Level Erd\H{o}s-R\'enyi model. Our model allows one to vary larger number of graph structural characteristics (namely, clustering coefficient, average degree, average shortest paths length, label and attribute assortativity) in a wider range. Our attribute generative method can be applied to any other non-attributed graph generative model with community structure and allows to control attribute assortativity corresponding to structure of graph.