  Quantifying the structural and functional differences of temporal networks is
a fundamental and challenging problem in the era of big data. This work
proposes a temporal dissimilarity measure for temporal network comparison based
on the fastest 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.
