Personalized Influential Topic Search via Social Network Summarization

 Personalized Influential Topic Search via Social Network Summarization

 Social networks are a vital mechanism to disseminate information to friends and colleagues. In this work, we investigate an important problem - the personalized influential topic search, or PIT-Search in a social network: Given a keyword query q issued by a user u in a social network, a PIT-Search is to find the top-k q-related topics that are most influential for the query user u. The influence of a topic to a query user depends on the social connection between the query user and the social users containing the topic in the social network. To measure the topics’ influence at the similar granularity scale, we need to extract the social summarization of the social network regarding topics. To make effective topic-aware social summarization, we propose two random-walk based approaches: random clustering and an L-length random walk.