Quantifying individual communication capability in opportunistic mobile social networks

Conventional methods with measuring information propagation in static networks mainly rely on paths or the shortest path connecting nodes, whereas in opportunistic mobile social networks the existence of a path or the shortest path between nodes cannot be assumed due to the dynamic topological partition nature of the networks. This paper extends the concept of walk to dynamic settings and combines it with the Green's function that originates from statistical physics to quantify how much information flows through each node in the networks changing over time. By means of the time-evolving graph model and the calculation of weighted combinatorial dynamic walks on the graph, a concise theoretic result is derived to account for the relative information propagation capability of each mobile node based on the historical contacts. In addition, the iteration-form result can be conveniently computed at any time point and therefore can be used for predicting the future network behavior when the time interval is appropriately chosen in specific scenarios. Extensive experiments are conducted based on four real trace datasets and the results show that, the formula derived in this paper is very effective at quantifying the information that flows through each mobile node.