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We argue that when comparing two graphs, the distribution of node structuralfeatures is more informative than global graph statistics which are often usedin practice, especially to evaluate graph generative models. Thus, we presentGraphDCA - a framework for evaluating similarity between graphs based on thealignment of their respective node representation sets. The sets are comparedusing a recently proposed method for comparing representation spaces, calledDelaunay Component Analysis (DCA), which we extend to graph data. To evaluateour framework, we generate a benchmark dataset of graphs exhibiting differentstructural patterns and show, using three node structure feature extractors,that GraphDCA recognizes graphs with both similar and dissimilar localstructure. We then apply our framework to evaluate three publicly availablereal-world graph datasets and demonstrate, using gradual edge perturbations,that GraphDCA satisfyingly captures gradually decreasing similarity, unlikeglobal statistics. Finally, we use GraphDCA to evaluate two state-of-the-artgraph generative models, NetGAN and CELL, and conclude that furtherimprovements are needed for these models to adequately reproduce localstructural features.

GraphDCA -- a Framework for Node Distribution Comparison in Real and Synthetic Graphs