A new study in Nature Communications by Associate Professor Marton Karsai and Assistant Professor Gerardo Iniguez from CEU's Department of Network and Data Science with Samuel Unicomb (University of Lyon) and James P. Gleeson (University of Limerick) has been published, illuminating aspects of complex systems where interactions between components vary in time.
Temporal networks provide a representation of real-world complex systems where interactions between components vary in time. In real systems, temporal interactions may be concentrated within short periods of intense activity followed by long intervals of inactivity, an effect known as burstiness. Bursty dynamics appear in diverse physical phenomena including earthquakes and solar flares, biological processes like neuron firing, as well as the dynamics of human social interactions.
Burstiness in temporal interactions has profound implications for the diffusion of information over temporal networks. This is true in the case of epidemic processes or in case of social contagion phenomena commonly assumed to be driven by threshold mechanisms. However, despite the broad set of observations of spreading processes on temporal networks, an analytical framework capturing the effect of burstiness on generic dynamics is lacking.
This paper develops a formalism to study cascades on temporal networks with burstiness modelled by renewal processes to describe the interplay between heterogeneous temporal interactions and models of threshold-driven and epidemic spreading. It finds that increasing bustiness can both accelerate and decelerate spreading for threshold models, but can only decelerate epidemic spreading. The framework uncovers a deep yet subtle connection between generic diffusion mechanisms and underlying temporal network structures that impacts a broad class of networked phenomena, from spin interactions to epidemic contagion and language dynamics.
Read the full article here.