The Eternal-Return Model of Human Mobility and Its Impact on Information Flow

The Eternal-Return Model of Human Mobility and Its Impact on Information Flow

Martine Collard (University of the French West Indies, France), Philippe Collard (University of Nice – Sophia Antipolis, France) and Erick Stattner (University of the French West Indies, France)
DOI: 10.4018/978-1-5225-2814-2.ch015


Human motions determine spatial social contacts that influence the way information spreads in a population. With the Eternal-Return model, we simulate an artificial world populated by heterogeneous individuals who differ in their mobility. This mobility model is synthetic but it represents regular patterns and it integrates the principles of periodicity, circular trajectory and variable amplitude of real patterns. We use a multi-agent framework for simulation and we endow agents with simple rules on how to move around the space and how to establish proximity-contacts. We distinguish different kinds of mobile agents, from sedentary ones to travelers. To summarize the dynamics induced by mobility over time, we define the mobility-based “social proximity network” as the network of all distinct contacts between agents. Properties such as the emergence of a giant component are given insight in the process of information spreading. We have conducted simulations to understand which density threshold allows percolation on the network when the mobility is constant and when it is varying.
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Spatial diffusion of an information whatever is the kind of information, knowledge, rumor, diseases or numeric viruses for instance, can be modeled with common principles. Related works (Eubank, Anil Kumar, & Marathe, 2008; De & Das, 2008; Borner, Sanyal, & Vespignani, 2007) have tend to show that these dissemination phenomena present very analogous characteristics indeed. This field has been well studied for years with a particular emphasis on epidemics since real datasets and simulation tools are available in this domain. But researches are generally restricted to studies on diffusion phenomena according to static network properties or virtual dynamics models. Few attention has been paid until very recently on social agents mobility and its impact on network dynamics and on information spread while mobility is obviously an important dimension in social practices. New societal challenges like urban planning or traffic management need to get a better knowledge of user motion patterns and user behavior in their environment.

Human mobility may induce deep modifications in social links among persons and thus variations on the information spreading. The phenomenon depends on the kind of mobility and on the kind of social relationship underlying the network. In most works, the mobility considered is spatial and the social behavior is realized by the ability for an individual to have a direct contact via spatial proximity. Proximity has been studied for years and it has been shown that it plays an important role on social communications. Individuals who share a close space should be more likely to develop friendship or other social even on-line relationships (Hall, 1963; Eveland & Bikson, 1986; Huang, Shen, Williams, & Contractor, 2009) and to create dynamics contact graphs (Toroczkai & Guclu, 2007).

But the lack of general tools to track individual locations has been an obstacle to extract any knowledge on human mobility from real situations. Synthetic mobility models like the random walk (RW) model and derivatives like the random waypoint (RWP) model were mostly studied for designing mobile ad hoc networks (Manets) and communication protocols (Camp, Boleng, & Davies, 2002; Chaintreau et al., 2005; Amor, Bui, & Lavallée, 2010). The RW model was intended to represent the movement of living beings considering that an individual moves from its current location to a new location by randomly choosing his direction and his speed until a given time or until its next location. The random way point (RWP) model is a variation of the RW model and was widely used. In this model, each individual chooses randomly a destination and a speed in the available space. But studies on RWP showed non realistic features (Bai, Sadagopan, & Helmy, 2003; Yoon, Noble, Liu, & Kim, 2006) and other variations like the Levy-walk models (A. M. Edwards, 2007) were proposed. Camp et al. (Camp et al., 2002) have provided a good survey of these models and their impact on adhoc network protocol performances.

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