Graph-structured data arise ubiquitously in many application domains. A fundamental problem is to quantify their similarities. Graph kernels are often used for this purpose, which decompose graphs into substructures and compare these substructures. However, most of the existing graph do not have property scale-adaptivity, i.e., they cannot at multiple levels granularities. Many real-world such ...

Graph neural network (GNN) has achieved great success on graph representation learning. Challenged by large-scale private data collected from user side, GNN may not be able to reflect the excellent performance, without rich features and complete adjacent relationships. Addressing problem, vertical federated learning (VFL) is proposed implement local protection through training a global model co...