Ranking learning entities on the web by integrating network-based features

Many efforts are undertaken by people and companies to improve their popularity, growth, and power, the outcomes of which are all expressed as rankings (designated as target rankings). Are these rankings merely the results of those person's or that company's own attributes? In the theory of social network analysis (SNA), the performance and power of actors are usually interpreted as relations and the relational structures in which they are embedded.We propose an algorithm to generate and integrate network-based features systematically from a given social network that is mined from the world-wide web. After learning a model for explaining target rankings researchers' productivity based on social networks confirms the effectiveness of our models. This chapter specifically examines the application of a social network that exemplifies the advanced use of social networks mined from the web.