Few-shot node classification on attributed networks based on deep metric learning for Cyber-Physical-Social Services

In Cyber-Physical-Social Systems (CPSS), the interactions among various entities form complex graphs. Many tasks can be formulated as instances of node classification. Node classification on graphs has attracted increasing research interest. However, the performance of existing graph neural networks for few-shot node classification has not achieved satisfactory results due to the limitation of the number of labeled instances. Therefore, we propose a graph-weighted prototype scaling network (GWPSN) based on deep metric learning for addressing the graph few-shot node classification problem. Specifically, we first extract node representations for the attributed graph via the simplifying graph convolutional network. At the same time, learning the importance of each node in the attributed graph is used to aggregate class prototypes. Finally, the class of the test node can be predicted by comparing the scaled metric distance between the test node and the class prototype. Experiments indicate that GWPSN can achieve superior performance on three real-world datasets and thus can provide enhanced few-shot classification services for CPSS.