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We propose DOCA (Detecting Overlapping Community Algorithm), a connection-based algorithm for discovering high quality overlapping community structures in social networks. Our proposed method is fast, very limited parameter dependent and only requires local knowledge about the network topology. Furthermore, the community structures discovered by DOCA are deterministic, i.e., no fuzzy community assignments are produced. DOCA's performance is certified by extensive experiments on real-world traces including Enron communication network, ArXiv citation and Astro physics collaboration networks as well as Face book and Foursquare social networks. The demonstrative benchmark with other detection methods highlights the efficiency of DOCA when discovering community structures of large-scale networks. By using DOCA to analyze the community structures of real datasets, we find that overlapping communities occur naturally and quite frequently, especially for top largest communities. In addition, overlapped nodes tend to be active users who participate in multiple communities at the same time. This happens not only on social networks but also on collaboration, citation and communication networks.