Causality-driven slow-down and speed-up of diffusion in non-Markovian temporal networks

By Ingo Scholtes, Nicolas Wider, René Pfitzner, Antonios Garas, Claudio Juan Tessone, Frank Schweitzer. Nature Communications, 2013

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Recent research has highlighted limitations of studying complex systems with time-varying topologies from the perspective of static, time-aggregated networks. Non-Markovian characteristics resulting from the ordering of interactions in temporal networks were identified as one important mechanism that alters causality and affects dynamical processes. So far, an analytical explanation for this phenomenon and for the significant variations observed across different systems is missing. Here we introduce a methodology that allows to analytically predict causality-driven changes of diffusion speed in non-Markovian temporal networks. Validating our predictions in six data sets we show that compared with the time-aggregated network, non-Markovian characteristics can lead to both a slow-down or speed-up of diffusion, which can even outweigh the decelerating effect of community structures in the static topology. Thus, non-Markovian properties of temporal networks constitute an important additional dimension of complexity in time-varying complex systems. In complex networks, non-Markovianity is an important mechanism affecting causality and the dynamics of a process. Here, Scholtes et al.introduce an analytical approach to study non-Markovian temporal networks, allowing to predict causality-driven changes of diffusion speed.

Subfields: Experimental and Cognitive Psychology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/18dd96a8fc8d912a28086292bddbb0dc8fb0f4f1

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Citations in the corpus

The fundamental advantages of temporal networks (2016, background): “A substantial body of research has found that such temporality [8–12] has profound effects on most dynamical processes taking place on networks [8–10, 13], and raising barriers to accessibility [9].”
Random walks and diffusion on networks (2016, background): “6) where p# ij is the stationary density at node # ijin the memory network. In many empirical temporal networks, H 2 is considerably smaller than H 1, implying that one cannot neglect memory eects [239, 241] (also see [257, 258] for similar measurements). The rst-order Markov chain M 1 tends to over”
Dynamics of information diffusion and its applications on complex networks (2016, background): “In addition, the correlation between events, which cannot be attributed to the time interval distribution, can both slow-down or speed-up diffusion in systems with the same static topology [156].”