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Mining different types of communities from web data have attracted a lot of research efforts in recent years. However, none of the existing community mining techniques has taken into account both the dynamic as well as heterogeneous nature of web data. In this paper, we propose to characterize and predict community members from the evolution of heterogeneous web data. We first propose a general framework for analyzing the evolution of heterogeneous networks. Then, the academic network, which isdoi:10.1145/1458082.1458125 dblp:conf/cikm/ZhaoBZY08 fatcat:6v6ki5vtmzblnfish4ywfctanu