The DOI matches its Crossref record; 47 of the 49 references it cites were found in a registry or an index.
AvgNet: Adaptive Visibility Graph Neural Network and Its Application in Modulation Classification
Abstract
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), which simultaneously captures relevant aspects of both local and global dynamics in an easy way, so that researchers can use graph algorithms to mine the knowledge in time series and gain special latent graph representation features. 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.
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Cites
Adam: A Method for Stochastic Optimization
Kingma, D. P., & Ba, J. (2014) · arXiv · doi:10.48550/arxiv.1412.6980 · Cited by 98,946 · cited 2×
“In the simulations, we use cross-entropy loss function and Adam [44] optimizer.”
Collective dynamics of ‘small-world’ networks
Watts, D. J., & Strogatz, S. H. (1998) · Nature · doi:10.1038/30918 · Cited by 43,060 · cited 1×
“The former is used to describe the topological structure of many complex systems, where nodes represent subsystems and links capture the relationship between them, leading to the emergence of complex networks [1], [2]; while the latter is used to capture the temporal dynamics of these systems.”
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
Gephi: An Open Source Software for Exploring and Manipulating Networks
Bastian, M., Heymann, S., & Jacomy, M. (2009) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v3i1.13937 · Cited by 11,675 · cited 2×
“These graphs are drawn by Gephi [44], and the layout is OpenOrd [44].”
(2017) · Cited by 11,625
A Comprehensive Survey on Graph Neural Networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2978386 · Cited by 11,000 · cited 1×
“It is worth noting that, due to the great success of GNN [34], [35], [36], [37] and effective graph mapping methods, it would be also an exciting direction to first map time series into graphs and then utilize powerful GNN to classify them.”
BackgroundThe spread of true and false news online
Vosoughi, S., Roy, D., & Aral, S. (2018) · Science · doi:10.1126/science.aap9559 · Cited by 8,847 · cited 1×
“Fortunately, by introducing graph data mining technologies, we can detect and filter the false information and purify the Internet environment to certain extent [8], [9].”
Background(2011) · Cited by 6,993
Fast Graph Representation Learning with PyTorch GeometricPreprint
Fey, M., & Lenssen, J. E. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1903.02428 · Cited by 3,403 · cited 2×
“What’s more, we use PyTorch Geometric [43], a geometric deep learning extension library for PyTorch, to build the neural network layers of DiffPool in our AVGNet model.”
Representation Learning on Graphs: Methods and ApplicationsPreprint
Hamilton, W. L., Ying, R., & Leskovec, J. (2017) · IEEE Data Engineering Bulletin · doi:10.48550/arxiv.1709.05584 · Cited by 2,203 · cited 1×
“It is worth noting that, due to the great success of GNN [34], [35], [36], [37] and effective graph mapping methods, it would be also an exciting direction to first map time series into graphs and then utilize powerful GNN to classify them.”
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Research works printed but not linked: 2
- 10.1109/lwc.2020.2999453 · DOI
- OShea, 2016, Radio machine learning dataset generation with GNU radio
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Recent Advances in Automatic Modulation Classification Technology: Methods, Results, and Prospects
Zheng, Q., Tian, X., Yu, L., Elhanashi, A., & Saponara, S. (2025) · International Journal of Intelligent Systems · doi:10.1155/int/4067323 · Cited by 89
“…deep neural networks (e.g., convolutional neural networks (CNNs) [40–44], recurrent neural networks (RNNs) [45–49], graph neural networks (GNNs) [50–52], transformers [53–55], and hybrid neural networks [56–59]) to perform the end-to-end feature 2 International Journal of Intelligent Systems…”
Guo, L., Wang, Y., Liu, Y., Lin, Y., Zhao, H., & Gui, G. (2024) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3373497 · Cited by 73
“…of DL in AMC, particularly through models, such as convolutional neural network (CNN) [7], recurrent neural networks (RNNs) [14], and graph neural network (GNN) [15], has facilitated the automated extraction of salient features from extensive data sets, substantially boosting performance.”
BackgroundAutomatic Modulation Classification Based on CNN-Transformer Graph Neural Network
Wang, D., Lin, M., Zhang, X., Huang, Y., & Zhu, Y. (2023) · Sensors · doi:10.3390/s23167281 · Cited by 74
“These models include AvgNet [70], MCLDNN [39], Resnet1d, VGG [28], CNN2d [27], GRU [29], LSTM [8], GAF [59], and ConsCNN [62].”
MethodInfluentialDeepSIG: A Hybrid Heterogeneous Deep Learning Framework for Radio Signal Classification
Qiu, K., Zheng, S., Zhang, L., Lou, C., & Yang, X. (2023) · IEEE Transactions on Wireless Communications · doi:10.1109/twc.2023.3281896 · Cited by 67
“II-B and AVG [50] mentioned in Sec.”
BackgroundSelf-citationLearn to Defend: Adversarial Multi-Distillation for Automatic Modulation Recognition Models
Chen, Z., Wang, Z., Xu, D., Zhu, J., Shen, W., Zheng, S., Xuan, Q., & Yang, X. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3361172 · Cited by 45
“Recently, with the development of deep learning (DL) technology, DL-based models have made significant progress in AMR task [6], [7], [8], [9], [10], [11].”
BackgroundMethodSelf-citation
Metadata
- Authors
- Xuan, Q., Zhou, J., Qiu, K., Chen, Z., Xu, D., Zheng, S., & Yang, X.
- Affiliations
- Zhejiang University of Technology (CN)
- Science and Technology on Communication Information Security Control Laboratory, Jiaxing, China
- Venue
- IEEE Transactions on Network Science and Engineering
- Published
- 2022-05-01
- Volume
- 9
- Issue
- 3
- Pages
- 1516-1526
- ISSN
- 2327-4697 (ISSN-L)
- 2334-329X
- OpenAlex
- W4225992487
- Cited by
- 106
- References
- 49
- Citations by year
- 2026: 25 · 2025: 31 · 2024: 32 · 2023: 16 · 2022: 2
- FWCI
- 6.51
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Network Science and Engineering (publishedVersion)
- Funders
- National Natural Science Foundation of China (U21B2001, 61973273)
- Key R&D Programs of Zhejiang (2022C01018)
- Natural Science Foundation of Zhejiang Province (LR19F030001)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Wireless Signal Modulation Classification
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Graph Neural Networks
- Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
- Keywords
- visibility graph, graph neural networks, wireless communication, time series classification
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