Dynamic Reactive Spiking Graph Neural Network
DOI:
https://doi.org/10.1609/aaai.v38i15.29640Keywords:
ML: Graph-based Machine Learning, ML: Semi-Supervised LearningAbstract
Spiking Graph Neural Networks are emerging tools for analyzing graph data along with low energy consumption and certain biological fidelity. Existing methods directly integrate same-reactive spiking neurons into graph neural networks for processing propagated graphs. However, such same-reactive neurons are not biological-functionality enough compared to the brain's dynamic-reactive ones, limiting the model's expression. Meanwhile, insufficient long-range neighbor information can be excavated with the few-step propagated graph, restricting discrimination of graph spiking embeddings. Inspired by the dynamic cognition in the brain, we propose a Dynamic Reactive Spiking Graph Neural Network that can enhance model's expressive ability in higher biological fidelity. Specifically, we design dynamic reactive spiking neurons to process spiking graph inputs, which have unique optimizable thresholds to spontaneously explore dynamic reactive states between neurons. Moreover, discriminative graph positional spikes are learned and integrated adaptively into spiking outputs through our neurons, thereby exploring long-range neighbors more thoroughly. Finally, with the dynamic reactive mechanism and learnable positional integration, we can obtain a powerful and highly bio-fidelity model with low energy consumption. Experiments on various domain-related datasets can demonstrate the effectiveness of our model. Our code is available at https://github.com/hzhao98/DRSGNN.Downloads
Published
2024-03-24
How to Cite
Zhao, H., Yang, X., Deng, C., & Yan, J. (2024). Dynamic Reactive Spiking Graph Neural Network. Proceedings of the AAAI Conference on Artificial Intelligence, 38(15), 16970-16978. https://doi.org/10.1609/aaai.v38i15.29640
Issue
Section
AAAI Technical Track on Machine Learning VI