https://doi.org/10.1140/epjp/s13360-024-05802-y
Regular Article
Evolutionary complex network for uncovering rich structure of series
1
School of Mathematics and Computational Science/Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education, Xiangtan University, 411105, Xiangtan, China
2
Hunan Provincial Key Laboratory of Dong Medicine, Hunan University of Medicine, 418000, Huaihua, China
a
fwang4@xtu.edu.cn
b
liuqing@stu.hunau.edu.cn
Received:
28
August
2024
Accepted:
5
November
2024
Published online:
21
December
2024
Important structures hidden in series often reflect various real-world information. Analyzing and recognizing series is, therefore, of great practical significance. Complex networks have shown outstanding performance in mining the topological features of data, which provides rich information from high-dimensional perspective. In this work, we develop a new evolutionary complex network mapped method from series, termed weighted k-series maximum differential graph (ks-maxDG). This method facilitates the mapping of series into complex networks from multiple perspectives, providing a more comprehensive exploration of their topological properties. These dynamic network properties offer deeper insights into the evolving structure of the original series. We validate its accuracy in uncovering the topological features theoretically and empirically, showing excellent performance in chaos and noise identification as well as series classification.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2024
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
