# AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models

arXiv: https://arxiv.org/abs/1908.05081 · HTML: https://mlsanity.com/paper/1908.05081/

## Key facts

- Cited by 37 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2019.

## Built on by (most cited first, 30 of 37)

- [Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPT](https://mlsanity.com/paper/2304.11116/) (2023)
- [Graph-based Semi-supervised Learning: A Comprehensive Review](https://mlsanity.com/paper/2102.13303/) (2021)
- [Ordered GNN: Ordering Message Passing to Deal with Heterophily and Over-smoothing](https://mlsanity.com/paper/2302.01524/) (2023)
- [ReconBoost: Boosting Can Achieve Modality Reconcilement](https://mlsanity.com/paper/2405.09321/) (2024)
- [A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking](https://mlsanity.com/paper/2210.07494/) (2022)
- [Boost then Convolve: Gradient Boosting Meets Graph Neural Networks](https://mlsanity.com/paper/2101.08543/) (2021)
- [Mixed Graph Contrastive Network for Semi-Supervised Node Classification](https://mlsanity.com/paper/2206.02796/) (2022)
- [SA-GDA: Spectral Augmentation for Graph Domain Adaptation](https://mlsanity.com/paper/2408.09189/) (2024)
- [Boosting Graph Neural Networks via Adaptive Knowledge Distillation](https://mlsanity.com/paper/2210.05920/) (2022)
- [The Snowflake Hypothesis: Training Deep GNN with One Node One Receptive field](https://mlsanity.com/paper/2308.10051/) (2023)
- [Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification](https://mlsanity.com/paper/2105.11625/) (2021)
- [Does your graph need a confidence boost? Convergent boosted smoothing on graphs with tabular node features](https://mlsanity.com/paper/2110.13413/) (2021)
- [Topological Regularization for Graph Neural Networks Augmentation](https://mlsanity.com/paper/2104.02478/) (2021)
- [AGNN: Alternating Graph-Regularized Neural Networks to Alleviate Over-Smoothing](https://mlsanity.com/paper/2304.07014/) (2023)
- [Decouple Graph Neural Networks: Train Multiple Simple GNNs Simultaneously Instead of One](https://mlsanity.com/paper/2304.10126/) (2023)
- [Mixture of Weak & Strong Experts on Graphs](https://mlsanity.com/paper/2311.05185/) (2023)
- [Graph Data Selection for Domain Adaptation: A Model-Free Approach](https://mlsanity.com/paper/2505.17293/) (2025)
- [GNN-Ensemble: Towards Random Decision Graph Neural Networks](https://mlsanity.com/paper/2303.11376/) (2023)
- [KnowGraph: Knowledge-Enabled Anomaly Detection via Logical Reasoning on Graph Data](https://mlsanity.com/paper/2410.08390/) (2024)
- [DisRFM: Polar Riemannian Flow Matching for Structure-Preserving Graph Domain Adaptation](https://mlsanity.com/paper/2602.00656/) (2026)
- [Multi-scale Graph Convolutional Networks with Self-Attention](https://mlsanity.com/paper/2112.03262/) (2021)
- [Beyond Graph Convolutional Network: An Interpretable Regularizer-centered Optimization Framework](https://mlsanity.com/paper/2301.04318/) (2023)
- [COMBHelper: A Neural Approach to Reduce Search Space for Graph Combinatorial Problems](https://mlsanity.com/paper/2312.09086/) (2023)
- [ADMP-GNN: Adaptive Depth Message Passing GNN](https://mlsanity.com/paper/2509.01170/) (2025)
- [GANN: Graph Alignment Neural Network for Semi-Supervised Learning](https://mlsanity.com/paper/2303.07778/) (2023)
- [MuseGNN: Forming Scalable, Convergent GNN Layers that Minimize a Sampling-Based Energy](https://mlsanity.com/paper/2310.12457/) (2023)
- [A novel hybrid time-varying graph neural network for traffic flow forecasting](https://mlsanity.com/paper/2401.10155/) (2024)
- [Graph Fuzzy System: Concepts, Models and Algorithms](https://mlsanity.com/paper/2210.16730/) (2022)
- [A Model-Agnostic Graph Neural Network for Integrating Local and Global Information](https://mlsanity.com/paper/2309.13459/) (2023)
- [Prioritized Propagation in Graph Neural Networks](https://mlsanity.com/paper/2311.02832/) (2023)

## Abstract (arXiv)

The design of deep graph models still remains to be investigated and the crucial part is how to explore and exploit the knowledge from different hops of neighbors in an efficient way. In this paper, we propose a novel RNN-like deep graph neural network architecture by incorporating AdaBoost into the computation of network; and the proposed graph convolutional network called AdaGCN~(Adaboosting Graph Convolutional Network) has the ability to efficiently extract knowledge from high-order neighbors of current nodes and then integrates knowledge from different hops of neighbors into the network in an Adaboost way. Different from other graph neural networks that directly stack many graph convolution layers, AdaGCN shares the same base neural network architecture among all ``layers'' and is recursively optimized, which is similar to an RNN. Besides, We also theoretically established the connection between AdaGCN and existing graph convolutional methods, presenting the benefits of our proposal. Finally, extensive experiments demonstrate the consistent state-of-the-art prediction performance on graphs across different label rates and the computational advantage of our approach AdaGCN~footnoteCode is available at urlhttps://github.com/datake/AdaGCN.