Efficient Spiking Variational Graph Auto-Encoders for Unsupervised Graph Representation Learning Tasks

Variational graph auto-encoders (VGAEs) are popular artificial neural network (ANN) based models for unsupervised graph representation learning tasks, including link prediction and graph generation, which are critical in many real-world applications. Despite the promising results of VGAEs on these tasks, existing VGAEs typically suffer from extremely high energy cost. Recently, the spiking neural networks (SNNs) have emerged as energy-efficient alternatives for applications on graph-structured data, while they are typically trained under supervised settings using label information. To leverage the energy efficiency of SNNs for unsupervised graph learning tasks, in this paper, we propose the SNN-based Spiking Variational Graph Auto-Encoder (S-VGAE) to efficiently learn spiking node representations using graph structural information. We conduct extensive experiments on two typical unsupervised graph learning tasks using benchmark datasets. The results demonstrate that our method can significantly save energy consumption with little or no loss on performances compared to both ANN- and SNN-based baselines.