Transductive Spiking Graph Neural Networks for Loihi

arXiv:2404.17048v1 Announce Type: cross
Abstract: Graph neural networks have emerged as a specialized branch of deep learning, designed to address problems where pairwise relations between objects are crucial. Recent advancements utilize graph convolutional neural networks to extract features within graph structures. Despite promising results, these methods face challenges in real-world applications due to sparse features, resulting in inefficient resource utilization. Recent studies draw inspiration from the mammalian brain and employ spiking neural networks to model and learn graph structures. However, …

abstract applications arxiv challenges convolutional convolutional neural networks cs.et cs.lg cs.ne deep learning extract face features graph graph neural networks networks neural networks objects relations results type world