# Adaptive Visibility Graph Neural Network and its Application in Modulation Classification

arXiv: https://arxiv.org/abs/2106.08564 · HTML: https://mlsanity.com/paper/2106.08564/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 47 references that are in the map.
- First posted on arXiv 16 Jun 2021.

## Builds on (most cited first, 19 of 19)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Comprehensive Survey on Graph Neural Networks](https://mlsanity.com/paper/1901.00596/) (2019)
- [Fast Graph Representation Learning with PyTorch Geometric](https://mlsanity.com/paper/1903.02428/) (2019)
- [Hierarchical Graph Representation Learning with Differentiable Pooling](https://mlsanity.com/paper/1806.08804/) (2018)
- [Representation Learning on Graphs: Methods and Applications](https://mlsanity.com/paper/1709.05584/) (2017)
- [Self-Attention Graph Pooling](https://mlsanity.com/paper/1904.08082/) (2019)
- [Deep Learning on Graphs: A Survey](https://mlsanity.com/paper/1812.04202/) (2018)
- [graph2vec: Learning Distributed Representations of Graphs](https://mlsanity.com/paper/1707.05005/) (2017)
- [Over the Air Deep Learning Based Radio Signal Classification](https://mlsanity.com/paper/1712.04578/) (2017)
- [Convolutional Radio Modulation Recognition Networks](https://mlsanity.com/paper/1602.04105/) (2016)
- [Network Representation Learning: A Survey](https://mlsanity.com/paper/1801.05852/) (2017)
- [FANG: Leveraging Social Context for Fake News Detection Using Graph Representation](https://mlsanity.com/paper/2008.07939/) (2020)
- [Distributed Deep Learning Models for Wireless Signal Classification with Low-Cost Spectrum Sensors](https://mlsanity.com/paper/1707.08908/) (2017)
- [subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs](https://mlsanity.com/paper/1606.08928/) (2016)
- [Subgraph Networks with Application to Structural Feature Space Expansion](https://mlsanity.com/paper/1903.09022/) (2019)
- [SigNet: A Novel Deep Learning Framework for Radio Signal Classification](https://mlsanity.com/paper/2011.03525/) (2020)
- [Deep Learning for Large-Scale Real-World ACARS and ADS-B Radio Signal Classification](https://mlsanity.com/paper/1904.09425/) (2019)
- [CLPVG: Circular limited penetrable visibility graph as a new network model for time series](https://mlsanity.com/paper/2104.13772/) (2021)
- [Generalized Shortest Path Kernel on Graphs](https://mlsanity.com/paper/1510.06492/) (2015)

## Built on by (most cited first, 9 of 9)

- [A Systematic Literature Review of Spatio-Temporal Graph Neural Network Models for Time Series Forecasting and Classification](https://mlsanity.com/paper/2410.22377/) (2024)
- [RadioMamba: Breaking the Accuracy-Efficiency Trade-off in Radio Map Construction via a Hybrid Mamba-UNet](https://mlsanity.com/paper/2508.09140/) (2025)
- [Survey of Graph Neural Network for Internet of Things and NextG Networks](https://mlsanity.com/paper/2405.17309/) (2024)
- [Supervised Contrastive Learning with Structure Inference for Graph Classification](https://mlsanity.com/paper/2203.07691/) (2022)
- [GNN-Geo: A Graph Neural Network-based Fine-grained IP geolocation Framework](https://mlsanity.com/paper/2112.10767/) (2021)
- [Mixing Signals: Data Augmentation Approach for Deep Learning Based Modulation Recognition](https://mlsanity.com/paper/2204.03737/) (2022)
- [Augmenting Radio Signals with Wavelet Transform for Deep Learning-Based Modulation Recognition](https://mlsanity.com/paper/2311.03761/) (2023)
- [Graph Neural Networks Based Anomalous RSSI Detection](https://mlsanity.com/paper/2505.15847/) (2025)
- [A Network Science Approach to Granular Time Series Segmentation](https://mlsanity.com/paper/2505.17640/) (2025)

## Abstract (arXiv)

Our digital world is full of time series and graphs which capture the various aspects of many complex systems. Traditionally, there are respective methods in processing these two different types of data, e.g., Recurrent Neural Network (RNN) and Graph Neural Network (GNN), while in recent years, time series could be mapped to graphs by using the techniques such as Visibility Graph (VG), so that researchers can use graph algorithms to mine the knowledge in time series. Such mapping methods establish a bridge between time series and graphs, and have high potential to facilitate the analysis of various real-world time series. However, the VG method and its variants are just based on fixed rules and thus lack of flexibility, largely limiting their application in reality. In this paper, we propose an Adaptive Visibility Graph (AVG) algorithm that can adaptively map time series into graphs, based on which we further establish an end-to-end classification framework AVGNet, by utilizing GNN model DiffPool as the classifier. We then adopt AVGNet for radio signal modulation classification which is an important task in the field of wireless communication. The simulations validate that AVGNet outperforms a series of advanced deep learning methods, achieving the state-of-the-art performance in this task.