Abstract
Quantifying the structural and functional differences of temporal networks remains a fundamental and challenging problem in the era of big data. Traditional network comparison methods, originally developed for static networks, often fall short in capturing the intricate interplay between structural configurations and dynamic temporal patterns inherent in complex systems. This work proposes a temporal dissimilarity measure for temporal network comparison based on the first arrival distance distribution and spectral entropy based Jensen-Shannon divergence. Experimental results on both synthetic and empirical temporal networks show that the proposed measure could discriminate diverse temporal networks with different structures by capturing various topological and temporal properties. Moreover, the proposed measure can discern the functional distinctions and is found effective applications in temporal network classification and spreadability discrimination.
Published Version
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