Homophily-Driven Evolution Increases the Diffusion Accuracy in Social Networks

In real world, social networks are often homophily-driven evolving, which represents the situation that users cut old connections and try to connect with others who share same attributes with them. However, existing works about information diffusion mainly focus on the static social network, while the influences of homophily-driven evolution has been neglected. Motivated by this, we investigate the diffusion accuracy problem in homophily-driven evolving social networks. Specifically, we consider a spreading-based diffusion mechanism, where a user simply spreads the information she/he is interested in to all her/his friends. This spreading-based diffusion mechanism is blind-guided and results in low diffusion performance in social networks without homophily-driven evolution. Our theoretical analyses present that the diffusion accuracy can be greatly improved during the evolution process. Moreover, we disclose that when the evolution process converges to a stable state, the diffusion process could achieve even higher performance, where all the information receivers are interested in it. In other word, the diffusion accuracy can simultaneously achieve high precision and recall. At last, the theoretical results are verified by simulations based on the synthetic network and experimental results based on real world network.