Abstract
Uncovering the overlapping community structure exhibited by real networks is a crucial step toward an understanding of complex systems that goes beyond the local organization of their constituents. Here three fuzzy c-means methods, based on optimal prediction, diffusion distance and dissimilarity index, respectively, are test on two artificial networks, including the widely known ad hoc networks and a recently introduced LFR benchmarks with heterogeneous distributions of degree and community size. All of them have an excellent performance, with the additional advantage of low computational complexity, which enables one to analyze large systems. Moreover, successful applications to real world networks confirm the capability of the methods.
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Liu, J. (2010). Comparing Fuzzy Algorithms on Overlapping Communities in Networks. In: Zhu, R., Zhang, Y., Liu, B., Liu, C. (eds) Information Computing and Applications. ICICA 2010. Lecture Notes in Computer Science, vol 6377. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16167-4_35
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DOI: https://doi.org/10.1007/978-3-642-16167-4_35
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