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.

In this study, we construct a 193 nm ultraviolet laser dissociation high-resolution mass spectrometry (HRMS) platform to produce Pt+ cations with high efficiency, which is in situ applied for monitoring the "Pt+ + alkanes" reactions (where alkanes include methane, ethane, and propane). The conversion intermediates and products could be accurately determined by an orbitrap detector with high resolution (up to 150 000). Importantly, methane conversion by Pt+ cations yields [Pt + ethane]+ and [Pt + ethylene]+ as the sole products formed via the cross-coupling reaction of the Pt-CH2 intermediate with gaseous methane. However, the Pt+ cations promote only the nonoxidative dehydrogenation of ethane and propane to give the corresponding [Pt + alkenes]+ and [Pt + alkynes]+. The details of the reaction mechanism are corroborated by density functional theory (DFT) calculations. These results highlight the power of HRMS with the laser dissociation of metal clusters in the generation and reaction characterization of metal ions.