A physical pathway to understand individual's labeling behavior in signed social networks

Abstract In this paper we propose a reshuffling approach to empirical analyze individual's labeling behavior in signed social networks. In our approach, each individual is assumed to have the ability to re-label his/her neighbors randomly with the parameters p s and p + . Many reshuffled networks, which have the same topological structure and different signs' configuration, are built through applying our approach to the given three signed social networks. The entropy S o u t and the giant component ρ G for each reshuffled networks are calculated and analyzed. We find that there exist two kinds of individual's labeling behavior according to the suppressed effect of S o u t and the exponent α in the relationship of ρ G and q + . Additionally, the suppressed effect of S o u t shows the non-randomness factor in individual's labeling behavior. These results offer new insights to understand human's behavior in online social networks.