TY - UNPB
T1 - Dispersion entropy: A Measure of Irregularity for Graph Signals
AU - Fabila-Carrasco, John Stewart
AU - Tan, Chao
AU - Escudero, Javier
PY - 2023/3/31
Y1 - 2023/3/31
N2 - We introduce a novel method, called Dispersion Entropy for Graph Signals, DEG, as a powerful tool for analysing the irregularity of signals defined on graphs. We demonstrate the effectiveness of DEG in detecting changes in the dynamics of signals defined on synthetic and real-world graphs, by defining mixed processing on random geometric graphs or those exhibiting with small-world properties. Remarkably, DEG generalises the classical dispersion entropy for univariate time series, enabling its application in diverse domains such as image processing, time series analysis, and network analysis, as well as in establishing theoretical relationships (i.e., graph centrality measures, spectrum). Our results indicate that DEG effectively captures the irregularity of graph signals across various network configurations, successfully differentiating between distinct levels of randomness and connectivity. Consequently, DEG provides a comprehensive framework for entropy analysis of various data types, enabling new applications of dispersion entropy not previously feasible, and revealing relationships between graph signals and its graph topology.
AB - We introduce a novel method, called Dispersion Entropy for Graph Signals, DEG, as a powerful tool for analysing the irregularity of signals defined on graphs. We demonstrate the effectiveness of DEG in detecting changes in the dynamics of signals defined on synthetic and real-world graphs, by defining mixed processing on random geometric graphs or those exhibiting with small-world properties. Remarkably, DEG generalises the classical dispersion entropy for univariate time series, enabling its application in diverse domains such as image processing, time series analysis, and network analysis, as well as in establishing theoretical relationships (i.e., graph centrality measures, spectrum). Our results indicate that DEG effectively captures the irregularity of graph signals across various network configurations, successfully differentiating between distinct levels of randomness and connectivity. Consequently, DEG provides a comprehensive framework for entropy analysis of various data types, enabling new applications of dispersion entropy not previously feasible, and revealing relationships between graph signals and its graph topology.
KW - Mathematics - Combinatorics
KW - 05C82
KW - 05C85
KW - 37E25
KW - 05C50
KW - 68R01
KW - 68R10
M3 - Preprint
BT - Dispersion entropy: A Measure of Irregularity for Graph Signals
PB - ArXiv
ER -