# Auto Graph Encoder-Decoder for Neural Network Pruning

arXiv: https://arxiv.org/abs/2011.12641 · HTML: https://mlsanity.com/paper/2011.12641/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2020.

## Built on by (most cited first, 26 of 26)

- [Structured Pruning for Deep Convolutional Neural Networks: A survey](https://mlsanity.com/paper/2303.00566/) (2023)
- [Revisiting Random Channel Pruning for Neural Network Compression](https://mlsanity.com/paper/2205.05676/) (2022)
- [Federated Foundation Models: Privacy-Preserving and Collaborative Learning for Large Models](https://mlsanity.com/paper/2305.11414/) (2023)
- [Topology-Aware Network Pruning using Multi-stage Graph Embedding and Reinforcement Learning](https://mlsanity.com/paper/2102.03214/) (2021)
- [Reinforcement learning on graphs: A survey](https://mlsanity.com/paper/2204.06127/) (2022)
- [Towards Sparsification of Graph Neural Networks](https://mlsanity.com/paper/2209.04766/) (2022)
- [SPATL: Salient Parameter Aggregation and Transfer Learning for Heterogeneous Clients in Federated Learning](https://mlsanity.com/paper/2111.14345/) (2021)
- [Enhancing Heterogeneous Federated Learning with Knowledge Extraction and Multi-Model Fusion](https://mlsanity.com/paper/2208.07978/) (2022)
- [Bridging the Gap Between Foundation Models and Heterogeneous Federated Learning](https://mlsanity.com/paper/2310.00247/) (2023)
- [Lossy and Lossless (L2) Post-training Model Size Compression](https://mlsanity.com/paper/2308.04269/) (2023)
- [Heterogeneous Graph Neural Networks for Software Effort Estimation](https://mlsanity.com/paper/2206.11023/) (2022)
- [Resource Constrained Model Compression via Minimax Optimization for Spiking Neural Networks](https://mlsanity.com/paper/2308.04672/) (2023)
- [Bayesian Optimization with Clustering and Rollback for CNN Auto Pruning](https://mlsanity.com/paper/2109.10591/) (2021)
- [Maximum Redundancy Pruning: A Principle-Driven Layerwise Sparsity Allocation for LLMs](https://mlsanity.com/paper/2503.18377/) (2025)
- [AutoDFP: Automatic Data-Free Pruning via Channel Similarity Reconstruction](https://mlsanity.com/paper/2403.08204/) (2024)
- [Rethinking Pruning for Backdoor Mitigation: An Optimization Perspective](https://mlsanity.com/paper/2405.17746/) (2024)
- [AutoSculpt: A Pattern-based Model Auto-pruning Framework Using Reinforcement Learning and Graph Learning](https://mlsanity.com/paper/2412.18091/) (2024)
- [Structure-Aware Automatic Channel Pruning by Searching with Graph Embedding](https://mlsanity.com/paper/2506.11469/) (2025)
- [DESIGN: Encrypted GNN Inference via Server-Side Input Graph Pruning](https://mlsanity.com/paper/2507.05649/) (2025)
- [Addressing Data Heterogeneity in Decentralized Learning via Topological Pre-processing](https://mlsanity.com/paper/2212.08743/) (2022)
- [Bespoke: A Block-Level Neural Network Optimization Framework for Low-Cost Deployment](https://mlsanity.com/paper/2303.01913/) (2023)
- [Layer Pruning with Consensus: A Triple-Win Solution](https://mlsanity.com/paper/2411.14345/) (2024)
- [FAIR-Pruner: A Flexible Framework for Automatic Layer-Wise Pruning via Tolerance of Difference](https://mlsanity.com/paper/2508.02291/) (2025)
- [Automatic Pruning Discovery for Large Language Models](https://mlsanity.com/paper/2511.15390/) (2025)
- [FastForward Pruning: Efficient LLM Pruning via Single-Step Reinforcement Learning](https://mlsanity.com/paper/2511.18977/) (2025)
- [FairGFL: Privacy-Preserving Fairness-Aware Federated Learning with Overlapping Subgraphs](https://mlsanity.com/paper/2512.23235/) (2025)

## Abstract (arXiv)

Model compression aims to deploy deep neural networks (DNN) on mobile devices with limited computing and storage resources. However, most of the existing model compression methods rely on manually defined rules, which require domain expertise. DNNs are essentially computational graphs, which contain rich structural information. In this paper, we aim to find a suitable compression policy from DNNs' structural information. We propose an automatic graph encoder-decoder model compression (AGMC) method combined with graph neural networks (GNN) and reinforcement learning (RL). We model the target DNN as a graph and use GNN to learn the DNN's embeddings automatically. We compared our method with rule-based DNN embedding model compression methods to show the effectiveness of our method. Results show that our learning-based DNN embedding achieves better performance and a higher compression ratio with fewer search steps. We evaluated our method on over-parameterized and mobile-friendly DNNs and compared our method with handcrafted and learning-based model compression approaches. On over parameterized DNNs, such as ResNet-56, our method outperformed handcrafted and learning-based methods with 4.36% and 2.56% higher accuracy, respectively. Furthermore, on MobileNet-v2, we achieved a higher compression ratio than state-of-the-art methods with just 0.93% accuracy loss.