  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, these approaches are limited to traditional Von
Neumann-based computing systems, which still face hardware inefficiencies. In
this study, we present a fully neuromorphic implementation of spiking graph
neural networks designed for Loihi 2. We optimize network parameters using Lava
Bayesian Optimization, a novel hyperparameter optimization system compatible
with neuromorphic computing architectures. We showcase the performance benefits
of combining neuromorphic Bayesian optimization with our approach for citation
graph classification using fixed-precision spiking neurons. Our results
demonstrate the capability of integer-precision, Loihi 2 compatible spiking
neural networks in performing citation graph classification with comparable
accuracy to existing floating point implementations.
