Modeling human dynamics of face-to-face interaction networks

Abstract

Face-to-face interaction networks describe social interactions in human gatherings, and are the substrate for processes such as epidemic spreading and gossip propagation. The bursty nature of human behavior characterizes many aspects of empirical data, such as the distribution of conversation lengths, of conversations per person, or of inter-conversation times. Despite several recent attempts, a general theoretical understanding of the global picture emerging from data is still lacking. Here we present a simple model that reproduces quantitatively most of the relevant features of empirical face-to-face interaction networks. The model describes agents which perform a random walk in a two dimensional space and are characterized by an attractiveness whose effect is to slow down the motion of people around them. The proposed framework sheds light on the dynamics of human interactions and can improve the modeling of dynamical processes taking place on the ensuing dynamical social networks.

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Cited by in corpus (8)

• Statistical physics of vaccination
• Contact patterns among high school students
• Predicting epidemic risk from past temporal contact data
• How memory generates heterogeneous dynamics in temporal networks
• Mutual Feedback Between Epidemic Spreading and Information Diffusion
• Asymmetrically interacting spreading dynamics on complex layered networks
• Non-Markovian Character in Human Mobility: Online and Offline
• Flocking and spreading dynamics in populations of self-propelled agents