Exploiting rank-learning models to predict the diffusion of preferences on social networks

This work tries to bring a marriage between two areas of computer science, social network analysis and machine learning, by exploiting ranking-based learning models for preference prediction on social networks. In the field of social network analysis, the diffusion of information on social networks has been studied for decades. This paper proposes the study of diffusion of preference on social networks. In general, there are two types of approaches proposed to predict the diffusion of information on a network, model-driven and data-driven approaches. The former assumes an underlying mechanism for diffusion while the latter tries to learn a more flexible model with the given data. This paper first proposes a simple modification on the existing model-driven binary diffusion approaches for preference list diffusion, and then addresses some concerns by proposing a rank-learning based data-driven approach. To evaluate the approaches, we propose two scenarios which data can be obtained from publicly available sources, namely predicting the preference propagation about the citation behavior and the microblogging behavior. The experiments show that the proposed ranking-based data-driven method outperforms all the other competitors significantly in both evaluation scenarios.