GrapHisto: A Robust Representation of Graph-Structured Data for Graph Convolutional Networks

Machine learning from graphs is an established branch of AI research motivated by the relevance of applications that involve graph-structured data. The most popular instance is the graph neural network (GNN). On the other hand, due to the promising results of deep learning models in the most diverse fields of application, several efforts have been made to replicate these successes when dealing with graphical data. A prominent specimen of the kind is the graph convolutional network (GCN). Along these lines, the paper propose a novel approach for processing graphs that exploits the capabilities of convolutional neural networks (CNNs) to learn from images. This is achieved by means of a new representation of graphs, called GrapHisto, that portrays graphs in the form of characteristic “pictures”. The GrapHisto is in the form of graph-specific, unique tensors encapsulating the graph topology and its features (i.e., the labels associated with vertexes and edges). This representation is fed to a CNN, and the resulting machine is termed GrapHisto-CNN. The paper provides some theoretical investigations of the properties of the approach, and proposes solutions to some practical issues. An experimental evaluation of the GrapHisto-CNN is reported, revolving around two setups: classification of synthetically-generated graphs, and molecule classification form the dataset QM9. The results show that the approach is effective and robust, and that it compares favorably with GNNs and GCNs.