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Studying protein complexes is very important in biological processes since it helps reveal the structure-functionality relationships in protein complexes. Most of the available algorithms are based on the assumption that dense subgraphs correspond to complexes, fail to take into account the inherence organization within protein complex and the roles of edges. To investigate the roles of edges in PPI networks, we show that the edges connecting less similar vertices in topology are more significant in maintaining the global connectivity, indicating the weak ties phenomenon in PPI networks. By using the concept of bridgeness, a reliable virtual network is constructed, in which each maximal clique corresponds to a core. By this notion, the detection of the protein complexes is transformed into a classic all-clique problem. A novel core-attachment based method is developed, which detects the cores and attachments, respectively. Finally, a comprehensive comparison between the existing algorithms and our algorithm has been made by comparing the predicted complexes against benchmark complexes. The experimental results on the yeast PPI network show that the proposed method outperforms the state-of-the-art algorithms and analysis of detected modules by the present algorithm suggests that most of these modules have well biological significance in context of complexes, implying that the role of interactions is a critical and promising factor in extracting protein complexes.