HDGN: Heat diffusion graph network for few-shot learning

Abstract

A heat diffusion graph network (HDGN) is proposed in this paper, which retains more similar graph signals in the spectral domain, for few-shot learning. Convolution on the graph is essentially the filtering of the graph signal. Most existing graph-network-based few-shot learning methods process graph signals with high-pass filters to get the difference in information. However, the low-frequency similar information is usually more valuable in the few-shot tasks. A joint low-pass filter is constructed to filter low-frequency graph signals, that is, heat kernel convolution aggregates similar information from neighboring nodes. The obtained low-frequency similarity information is utilized to update the representations of nodes on the graph. In addition, a more robust mixed metric is designed to dynamically update the edge feature of the graph. Predicting Unknown Nodes on Graphs by Alternating Updates of Node Representation and Edge Matrix. The experimental results also demonstrate that HDGN achieves better performance for the few-shot classification task.