Enhancing Graph Representation Learning with Attention-Driven Spiking
  Neural Networks
Huifeng Yin Mingkun Xu Jing Pei Lei Deng
Graph representation learning has become a crucial task in machine learning
and data mining due to its potential for modeling complex structures such as
social networks, chemical compounds, and biological systems. Spiking neural
networks (SNNs) have recently emerged as a promising alternative to traditional
neural networks for graph learning tasks, benefiting from their ability to
efficiently encode and process temporal and spatial information. In this paper,
we propose a novel approach that integrates attention mechanisms with SNNs to
improve graph representation learning. Specifically, we introduce an attention
mechanism for SNN that can selectively focus on important nodes and
corresponding features in a graph during the learning process. We evaluate our
proposed method on several benchmark datasets and show that it achieves
comparable performance compared to existing graph learning techniques.