We study human mobility networks through timeseries of contacts between individuals. Our proposed Random Walkers Induced temporal Graph (RWIG) model generates temporal graph sequences based on independent random walkers that traverse an underlying graph in discrete time steps. Co-location of walkers at a given node and time defines an individual-level contact. RWIG is shown to be a realistic model for temporal human contact graphs, which may place RWIG on a same footing as the Erdos-Renyi (ER) and Barabasi-Albert (BA) models for fixed graphs. Moreover, RWIG is analytically feasible: we derive closed form solutions for the probability distribution of contact graphs.

We examine the Random Walkers Induced temporal Graph (RWIG) model, which generates temporal graphs based on the co-location principle of M independent walkers that traverse the underlying Markov graph with different transition probabilities. Given the assumption that each random walker is in the steady state, we determine the steady-state vector s̃and the Markov transition matrix P _i of each walker w _i that can reproduce the observed temporal network G ₀, . . ., G _{K –1} with the lowest mean squared error. We also examine the performance of RWIG for periodic temporal graph sequences.