# Graph Signal Processing and Deep Learning: Convolution, Pooling, and Topology

arXiv: https://arxiv.org/abs/2008.01247 · HTML: https://mlsanity.com/paper/2008.01247/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Aug 2020.

## Built on by (most cited first, 11 of 11)

- [Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention](https://mlsanity.com/paper/2105.13495/) (2021)
- [Graph Filters for Signal Processing and Machine Learning on Graphs](https://mlsanity.com/paper/2211.08854/) (2022)
- [Signed Graph Neural Networks: A Frequency Perspective](https://mlsanity.com/paper/2208.07323/) (2022)
- [A Computational Framework for Modeling Complex Sensor Network Data Using Graph Signal Processing and Graph Neural Networks in Structural Health Monitoring](https://mlsanity.com/paper/2105.05316/) (2021)
- [Spatial Graph Convolutional Neural Network via Structured Subdomain Adaptation and Domain Adversarial Learning for Bearing Fault Diagnosis](https://mlsanity.com/paper/2112.06033/) (2021)
- [Developing Imperceptible Adversarial Patches to Camouflage Military Assets From Computer Vision Enabled Technologies](https://mlsanity.com/paper/2202.08892/) (2022)
- [Barron Space for Graph Convolution Neural Networks](https://mlsanity.com/paper/2311.02838/) (2023)
- [Explainable Machine Learning: The Importance of a System-Centric Perspective](https://mlsanity.com/paper/2302.02347/) (2023)
- [Feature Network Methods in Machine Learning and Applications](https://mlsanity.com/paper/2401.04874/) (2024)
- [SlepNet: Spectral Subgraph Representation Learning for Neural Dynamics](https://mlsanity.com/paper/2506.16602/) (2025)
- [DisenReason: Behavior Disentanglement and Latent Reasoning for Shared-Account Sequential Recommendation](https://mlsanity.com/paper/2603.03782/) (2026)

## Abstract (arXiv)

Deep learning, particularly convolutional neural networks (CNNs), have yielded rapid, significant improvements in computer vision and related domains. But conventional deep learning architectures perform poorly when data have an underlying graph structure, as in social, biological, and many other domains. This paper explores 1)how graph signal processing (GSP) can be used to extend CNN components to graphs in order to improve model performance; and 2)how to design the graph CNN architecture based on the topology or structure of the data graph.