Activity driven modeling of time varying networks

By Nicola Perra, Bruno Gonçalves, Romualdo Pastor-Satorras, Alessandro Vespignani. Scientific Reports, 2012

620 citations recorded by the source corpus.

Network modeling plays a critical role in identifying statistical regularities and structural principles common to many systems. The large majority of recent modeling approaches are connectivity driven. The structural patterns of the network are at the basis of the mechanisms ruling the network formation. Connectivity driven models necessarily provide a time-aggregated representation that may fail to describe the instantaneous and fluctuating dynamics of many networks. We address this challenge by defining the activity potential, a time invariant function characterizing the agents' interactions and constructing an activity driven model capable of encoding the instantaneous time description of the network dynamics. The model provides an explanation of structural features such as the presence of hubs, which simply originate from the heterogeneous activity of agents. Within this framework, highly dynamical networks can be described analytically, allowing a quantitative discussion of the biases induced by the time-aggregated representations in the analysis of dynamical processes.

Subfields: Experimental and Cognitive Psychology, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/bc34497341850e716780afb8a1d81171558e5f77

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Citations in the corpus

Comparison and Modelling of Country-level Microblog User and Activity in Cyber-physical-social Systems Using Weibo and Twitter Data (2019, background): “[37] constructed an activity-driven model to describe the structure features of dynamic network and took Twitter as one of their empirical data sources [37].”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, background): “A number of recent publications explored the effect of time-window changes on the structure of social networks (Krings et al., 2012; Perra et al., 2012).”
Coupled disease–behavior dynamics on complex networks: A review (2015, background): “Time-varying networks (also named temporal networks) provide another framework for the activity-driven changing of connection topology [31,123,32].”
Analytically Solvable Model of Spreading Dynamics with Non-Poissonian Processes (2013, background): “[12], although they took a different approach than ours by using inhomogeneous activities of nodes.”
Statistical physics of vaccination (2016, methodology): “where Na is the total number of individuals with activity a [596].”
Random walks and diffusion on networks (2016, background): “ful approach is to examine RWs on an \activity-driven model" of temporal networks [147]. The simplest type of activity-driven model generates a sequence of uncorrelated time-independent networks [242]. First, we associate each node v i (with i2f1 ;:::; Ng) with a random variable a i, called the”