Temporal Graph Reproduction With RWIG

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 $\tilde{s}$ and the Markov transition matrix $P_{i}$ of each walker $w_{i}$ that can reproduce the observed temporal network $G_{0},{{\ldots }},G_{K\text{--}1}$ with the lowest mean squared error. We also examine the performance of RWIG for periodic temporal graph sequences.