Ranking Learning 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 its elements' 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 they embedded. In this study, we propose an algorithm to generate and integrate network-based features systematically from a given social network that mined from the Web to learn a model for explaining target rankings. Experimental results for learning to rank researchers' productivity based on social networks confirm the effectiveness of our models. This paper specifically examines the application of a social network that provides an example of advanced utilization of social networks mined from the Web.