Ranking Entities on the Web using Social Network Mining and Ranking Learning

Social networks have garnered much attention recently. Several studies have been undertaken to extract social networks among people, companies, and so on automatically from the web. For use in social sciences, social networks enable analyses of the performance and valuation of companies. This paper describes an attempt to learn ranking of entities from a social network that has been mined from the web. In our approach, we first extract different kinds of relational data from the web. We construct social networks using several relevance measures in addition to text analysis. Subsequently, the relations are integrated to maximize the ranking predictability. We also integrate several relations into a combined-relational network and use the latest ranking learning algorithm to obtain the ranking model. Additionally, we propose the use of centrality scores of companies on the network as features for ranking. We conducted two experiments on a social network among companies to learn the ranking of market capitalization, and on a social network among researchers for ranking of researchers’ productivity. This study specifically examines a new approach to using web information for advanced analysis by integrating multiple relations among named entities.