Adaptive Visibility Graph Neural Network and its Application in Modulation Classification
This addresses a domain-specific problem in wireless communication by enabling more flexible time series analysis, though it is incremental as it builds on existing mapping and GNN techniques.
The paper tackled the inflexibility of fixed-rule mapping from time series to graphs by proposing an Adaptive Visibility Graph (AVG) algorithm and an end-to-end classification framework AVGNet, which achieved state-of-the-art performance in radio signal modulation classification.
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.