Dynamic graphs are used to represent relationships between entities that evolve over time. Meaningful patterns in such structured data must capture strong interactions and their evolution over time. In social networks, such patterns can be seen as dynamic community structures, i.e., sets of individuals who strongly and repeatedly interact. In this paper, we propose a constraint-based mining approach to uncover evolving patterns. We propose to mine dense and isolated subgraphs defined by two user-parameterized constraints. The temporal evolution of such patterns is captured by associating a temporal event type to each identified subgraph. We consider five basic temporal events: The formation, dissolution, growth, diminution and stability of subgraphs from one time stamp to the next. We propose an algorithm that finds such subgraphs in a time series of graphs processed incrementally. The extraction is feasible due to efficient patterns and data pruning strategies. We demonstrate the applicability of our method on several real-world dynamic graphs and extract meaningful evolving communities.