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The variation in dynamic networks may contain information drifting over time, which might imply significant events or behaviors. Taking full use of the variation of a dynamic network, especially the variation of cliques within a dynamic network, has the potential to enable us to partly predict what is going to happen, to bring us early warnings and to provide us a faster response to emergency. As an attempt to utilize this time-related information in social network analysis, a dynamic core detection algorithm is proposed in this paper. To clarify this method, a group of core features are first given, which are applied to abstract dynamic patterns from original data. A relationship network is then created based on these core features, and the core of social network is then generated from this relationship network by relation strength thresholding. This approach is further illustrated in a real-world case study, which is a worldwide terrorist network based on open source data. The experimental results show that this method is able to detect meaningful dynamic core within a social network.