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Community Web sites on specific topics are very popular on the Web. Some active Web communities are so huge and diverse that it becomes a challenging issue to efficiently mine meaningful knowledge from the Web communities. In this paper, we develop schemes to discover and browse power users by their activities in online communities. The novelties of this work are two-fold. 1) We define new features to describe user's social activities: statistical features to summarize userspsila activities and relationship-based features to describe interactions between individual users. And, through extensive user study and experiments to compare the performances of the ranking models based on various features, it is shown that the cross reference (CR) feature plays an unique and effective role in discovering power users in post-dominant online communities. 2) Thereafter, we develop a novel interface for effective exploration of power users based on the CR rank. Two schemes are proposed to incrementally navigate a large number of candidate power users with higher CR values: threshold-based navigation and traversal-based one. Experimental results shows that the proposed CR rank can be used for effective browsing of power users: about 70% precision is maintained while retrieving all the power users, which means that we can discover all the power users with relatively small number of false alarms.