stability analysis in dynamic social networks
In this paper, we address the problem that how could the decentralized local interactions of autonomous agents generate social norms. Different from the existing work in this area, we focus on dynamic social networks that agents can freely change their connections based on their individual interests. We propose a new social norm rule called Highest Weighted Neighborhood (HWN) that agents can dynamically choose their neighbors to maximize their own utility through all previous interactions between the agents and these neighbors. Comparing with the traditional models that networks usually are static or agents choose their neighbors randomly, our model is able to handle dynamic interactions between rational selfish agents. We prove that in the 2-action pure coordination games, our system will stabilize in a clustering state and at that time all relationships in the network are rewarded the optimal payoff. Our preliminary experiments verify the theory.
social network pattern analysis dynamic network