Adaptive community detection besed on correlation spectral mapping

One of the most significant problems of detecting communities in networks is the lack of prior knowledge about the number of communities. Here we proposed a correlation spectral mapping based adaptive community detection (CSMACD) algorithm, which employs the concept of the well-known modularity value and the Eigenspectrum of the correlation matrix to automatically detect the network´s community structure. Extensive tests on both artificial and real-world networks demonstrate the competitive performance of CSMACD compared to other state-of-the-art community detection algorithms, especially when one has to discover the structure of fuzzy and multi-scale networks.