Generating Useful Network-based Features for Analyzing Social Networks

Jun Karamon, Yutaka Matsuo, Mitsuru Ishizuka

Abstract: Recently, many Web services such as social networking services, blogs, and collaborative tagging have become widely popular. Many attempts are being made to investigate user interactions by analyzing social networks among users. However, analyzing a social network with attributional data is often not an easy task because numerous ways exist to define features through aggregation of different tables. In this study, we propose an algorithm to identify important network-based features systematically from a given social network to analyze user behavior efficiently and to expand the services. We apply our method for link-based classification and link prediction tasks with two different datasets, i.e., an @cosme (an online viral marketing site) dataset and a Hatena Bookmark (collaborative tagging service) dataset, to demonstrate the usefulness of our algorithm. Our algorithm is general and can provide useful network-based features for social network analyses.