Toward the analysis of graph neural networks

Graph Neural Networks (GNNs) have recently emerged as an effective framework for representing and analyzing graph-structured data. GNNs have been applied to many real-world problems such as knowledge graph analysis, social networks recommendation, and even COVID-19 detection and vaccine development. However, unlike other deep neural networks such as Feedforward Neural Networks (FFNNs), few verification and property inference techniques exist for GNNs. This is potentially due to dynamic behaviors of GNNs, which can take arbitrary graphs as input, whereas FFNNs which only take fixed size numerical vectors as inputs.