Enhancing Graph Representation Learning with Attention-Driven Spiking Neural Networks

arXiv:2403.17040v1 Announce Type: cross
Abstract: 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 …

abstract arxiv attention become chemical compounds cs.ai cs.lg cs.ne data data mining graph graph representation machine machine learning mining modeling networks neural networks representation representation learning social social networks spiking neural networks systems type