Reimagining Graph Classification from a Prototype View with Optimal Transport: Algorithm and Theorem

Recently, Graph Neural Networks (GNNs) have achieved inspiring performances in graph classification tasks. However, the message passing mechanism in GNNs implicitly utilizes the topological information of the graph, which may lead to a potential loss of structural information. Furthermore, the graph classification decision process based on GNNs resembles a black box and lacks sufficient transparency. The non-linear classifier following the GNNs also defaults to the assumption that each class is represented by a single vector, thereby limiting the diversity of intra-class representations.