Inferring social roles and statuses in social networks

Users in online social networks play a variety of social roles and statuses. For example, users in Twitter can be represented as advertiser, content contributor, information receiver, etc; users in Linkedin can be in different professional roles, such as engineer, salesperson and recruiter. Previous research work mainly focuses on using categorical and textual information to predict the attributes of users. However, it cannot be applied to a large number of users in real social networks, since much of such information is missing, outdated and non-standard. In this paper, we investigate the social roles and statuses that people act in online social networks in the perspective of network structures, since the uniqueness of social networks is connecting people. We quantitatively analyze a number of key social principles and theories that correlate with social roles and statuses. We systematically study how the network characteristics reflect the social situations of users in an online society. We discover patterns of homophily, the tendency of users to connect with users with similar social roles and statuses. In addition, we observe that different factors in social theories influence the social role/status of an individual user to various extent, since these social principles represent different aspects of the network. We then introduce an optimization framework based on Factor Conditioning Symmetry, and we propose a probabilistic model to integrate the optimization framework on local structural information as well as network influence to infer the unknown social roles and statuses of online users. We will present experiment results to show the effectiveness of the inference.