Centrality-Based Opinion Modeling on Temporal Networks

While most of opinion formation models consider static networks, a dynamic opinion formation model is proposed in this work. The so-called Temporal Threshold Page Rank Opinion Formation model (TTPROF) integrates temporal evolution in two ways. First, the opinion of the agents evolves with time. Second, the network structure is also time varying. More precisely, the relations between agents evolve with time. In the TTPROF model, a node is affected by the part of its neighbors opinions weighted by their Page Rank values. A threshold is introduced to limit the neighbors that can share their opinion. In other words, a neighbor influences a node if the difference between their opinions is below the threshold. Finally, a fraction of top ranked nodes in the neighborhood is considered influential nodes irrespective of the threshold value. Experiments have been performed on random temporal networks to analyze how opinions propagate and converge to consensus or multiple clusters. Preliminary results have been presented. In this paper, this work is extended in two directions. First, the impact of various centrality measures on model behavior is investigated. Indeed, in earlier work, the influence of a node is measured using Page Rank. New results using Directed Degree centrality and Closeness Centrality are derived. They allow to compare global against local influence measures as well as distance-based centrality and to better understand the impact of the weighting parameter on the model convergence. Second, the results of an extensive experimental investigation are reported and analyzed in order to characterize the model convergence in various situations.