  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.
