Evaluating Graph Neural Network Performance on the Multi-Layer Contextual Stochastic Block Model

Abstract

Many networks contain multiple views of the data, which occur when you have one set of vertices with multiple sets of edges that connect them. These multiple sets of edges represent different sets of relationships between the vertices and can be a potent source of information about the underlying groupings of the nodes. Graph Neural Networks (GNNs) are often used to extract information from multi-view data, however, the mechanisms underlying the effectiveness of GNNs are not well understood. We analyzed the performance of two different frameworks for simple GNNs on multi-layer data generated using the multi-layer contextual stochastic block model, which allowed for fine-grained adjustment of graph properties. We found that the layer-wise probability of a community difference and the graph signal-to-noise ratio in each layer both affect model performance, and present intuition behind why this is the case.