# Spiking Graph Convolutional Networks

arXiv: https://arxiv.org/abs/2205.02767 · HTML: https://mlsanity.com/paper/2205.02767/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 67 references that are in the map.
- First posted on arXiv 5 May 2022.

## Builds on (most cited first, 27 of 27)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Graph Neural Networks: A Review of Methods and Applications](https://mlsanity.com/paper/1812.08434/) (2018)
- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Revisiting Semi-Supervised Learning with Graph Embeddings](https://mlsanity.com/paper/1603.08861/) (2016)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling](https://mlsanity.com/paper/1801.10247/) (2018)
- [Geometric deep learning on graphs and manifolds using mixture model CNNs](https://mlsanity.com/paper/1611.08402/) (2016)
- [Towards Deeper Graph Neural Networks](https://mlsanity.com/paper/2007.09296/) (2020)
- [Deep Learning in Spiking Neural Networks](https://mlsanity.com/paper/1804.08150/) (2018)
- [Training Deep Spiking Neural Networks using Backpropagation](https://mlsanity.com/paper/1608.08782/) (2016)
- [Predictive Entropy Search for Efficient Global Optimization of Black-box Functions](https://mlsanity.com/paper/1406.2541/) (2014)
- [Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection](https://mlsanity.com/paper/1903.06530/) (2019)
- [Carbontracker: Tracking and Predicting the Carbon Footprint of Training Deep Learning Models](https://mlsanity.com/paper/2007.03051/) (2020)
- [SplineCNN: Fast Geometric Deep Learning with Continuous B-Spline Kernels](https://mlsanity.com/paper/1711.08920/) (2017)
- [AM-GCN: Adaptive Multi-channel Graph Convolutional Networks](https://mlsanity.com/paper/2007.02265/) (2020)
- [An Analysis of Deep Neural Network Models for Practical Applications](https://mlsanity.com/paper/1605.07678/) (2016)
- [Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks](https://mlsanity.com/paper/1704.06803/) (2017)
- [Convolutional Networks for Fast, Energy-Efficient Neuromorphic Computing](https://mlsanity.com/paper/1603.08270/) (2016)
- [Hybrid Macro/Micro Level Backpropagation for Training Deep Spiking Neural Networks](https://mlsanity.com/paper/1805.07866/) (2018)
- [Matrix Completion on Graphs](https://mlsanity.com/paper/1408.1717/) (2014)
- [Spiking Deep Residual Network](https://mlsanity.com/paper/1805.01352/) (2018)
- [SpikeGrad: An ANN-equivalent Computation Model for Implementing Backpropagation with Spikes](https://mlsanity.com/paper/1906.00851/) (2019)
- [Recent Advances and New Frontiers in Spiking Neural Networks](https://mlsanity.com/paper/2204.07050/) (2022)
- [Rectified Linear Postsynaptic Potential Function for Backpropagation in Deep Spiking Neural Networks](https://mlsanity.com/paper/2003.11837/) (2020)

## Built on by (most cited first, 27 of 27)

- [Spikformer: When Spiking Neural Network Meets Transformer](https://mlsanity.com/paper/2209.15425/) (2022)
- [Finding the Missing-half: Graph Complementary Learning for Homophily-prone and Heterophily-prone Graphs](https://mlsanity.com/paper/2306.07608/) (2023)
- [Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks](https://mlsanity.com/paper/2208.10364/) (2022)
- [SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization](https://mlsanity.com/paper/2207.09179/) (2022)
- [Spiking Graph Neural Network on Riemannian Manifolds](https://mlsanity.com/paper/2410.17941/) (2024)
- [SIGMA: An Efficient Heterophilous Graph Neural Network with Fast Global Aggregation](https://mlsanity.com/paper/2305.09958/) (2023)
- [SiGNN: A Spike-induced Graph Neural Network for Dynamic Graph Representation Learning](https://mlsanity.com/paper/2404.07941/) (2024)
- [A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks](https://mlsanity.com/paper/2305.19306/) (2023)
- [SpikingNeRF: Making Bio-inspired Neural Networks See through the Real World](https://mlsanity.com/paper/2309.10987/) (2023)
- [MK-SGN: A Spiking Graph Convolutional Network with Multimodal Fusion and Knowledge Distillation for Skeleton-based Action Recognition](https://mlsanity.com/paper/2404.10210/) (2024)
- [Temporal Spiking Neural Networks with Synaptic Delay for Graph Reasoning](https://mlsanity.com/paper/2405.16851/) (2024)
- [Degree-Conscious Spiking Graph for Cross-Domain Adaptation](https://mlsanity.com/paper/2410.06883/) (2024)
- [Spiking GATs: Learning Graph Attentions via Spiking Neural Network](https://mlsanity.com/paper/2209.13539/) (2022)
- [Learning Graph Neural Networks using Exact Compression](https://mlsanity.com/paper/2304.14793/) (2023)
- [Attention-free Spikformer: Mixing Spike Sequences with Simple Linear Transforms](https://mlsanity.com/paper/2308.02557/) (2023)
- [Signal-SGN: A Spiking Graph Convolutional Network for Skeletal Action Recognition via Learning Temporal-Frequency Dynamics](https://mlsanity.com/paper/2408.01701/) (2024)
- [Edge Intelligence with Spiking Neural Networks](https://mlsanity.com/paper/2507.14069/) (2025)
- [Spiking Variational Graph Auto-Encoders for Efficient Graph Representation Learning](https://mlsanity.com/paper/2211.01952/) (2022)
- [Artificial-Spiking Hierarchical Networks for Vision-Language Representation Learning](https://mlsanity.com/paper/2308.09455/) (2023)
- [Event-based Shape from Polarization with Spiking Neural Networks](https://mlsanity.com/paper/2312.16071/) (2023)
- [Signal-SGN++: Topology-Enhanced Time-Frequency Spiking Graph Network for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2512.22214/) (2025)
- [When Prompting Meets Spiking: Graph Sparse Prompting via Spiking Graph Prompt Learning](https://mlsanity.com/paper/2601.02662/) (2026)
- [ChronoSpike: An Adaptive Spiking Graph Neural Network for Dynamic Graphs](https://mlsanity.com/paper/2602.01124/) (2026)
- [Scalable and Efficient Joint Spiking Embedding Predictive Architecture for Large-Scale Dynamic Graphs](https://mlsanity.com/paper/2607.18412/) (2026)
- [Continuous Spiking Graph Neural Networks](https://mlsanity.com/paper/2404.01897/) (2024)
- [Unveiling the Potential of Spiking Dynamics in Graph Representation Learning through Spatial-Temporal Normalization and Coding Strategies](https://mlsanity.com/paper/2407.20508/) (2024)
- [Improving vision-language alignment with graph spiking hybrid Networks](https://mlsanity.com/paper/2501.19069/) (2025)

## Abstract (arXiv)

Graph Convolutional Networks (GCNs) achieve an impressive performance due to the remarkable representation ability in learning the graph information. However, GCNs, when implemented on a deep network, require expensive computation power, making them difficult to be deployed on battery-powered devices. In contrast, Spiking Neural Networks (SNNs), which perform a bio-fidelity inference process, offer an energy-efficient neural architecture. In this work, we propose SpikingGCN, an end-to-end framework that aims to integrate the embedding of GCNs with the biofidelity characteristics of SNNs. The original graph data are encoded into spike trains based on the incorporation of graph convolution. We further model biological information processing by utilizing a fully connected layer combined with neuron nodes. In a wide range of scenarios (e.g. citation networks, image graph classification, and recommender systems), our experimental results show that the proposed method could gain competitive performance against state-of-the-art approaches. Furthermore, we show that SpikingGCN on a neuromorphic chip can bring a clear advantage of energy efficiency into graph data analysis, which demonstrates its great potential to construct environment-friendly machine learning models.