A Novel Higher-order Weisfeiler-Lehman Graph Convolution

Graph-structured data has recently received increasing attention in machine learning, with applications ranging from the prediction of chemical properties, e.g., whether a molecule is toxic [1], to the analysis of social network structures [2] and source code [3]. This paper focuses on the prediction of (global) graph properties, i.e., graph classification and regression. In order to predict a certain property of interest, for example to discriminate between graphs in a classification task, a learner must be able to detect, either explicitly or implicitly, characteristic features of a graph that are indicative of the sought property. To this end, suitable approaches have been developed in the fields of kernel-based machine learning and (deep) neural networks.