  Visibility Graph (VG) transforms time series into graphs, facilitating signal
processing by advanced graph data mining algorithms. In this paper, based on
the classic Limited Penetrable Visibility Graph (LPVG) method, we propose a
novel nonlinear mapping method named Circular Limited Penetrable Visibility
Graph (CLPVG). The testing on degree distribution and clustering coefficient on
the generated graphs of typical time series validates that our CLPVG is able to
effectively capture the important features of time series and has better
anti-noise ability than traditional LPVG. The experiments on real-world
time-series datasets of radio signal and electroencephalogram (EEG) also
suggest that the structural features provided by CLPVG, rather than LPVG, are
more useful for time-series classification, leading to higher accuracy. And
this classification performance can be further enhanced through structural
feature expansion by adopting Subgraph Networks (SGN). All of these results
validate the effectiveness of our CLPVG model.
