Comparing fuzzy algorithms on overlapping communities in networks

Authors:
Jian Liu
Affiliations:
LMAM and School of Mathematical Sciences, Peking University, Beijing, P.R. China
Venue:
ICICA'10 Proceedings of the First international conference on Information computing and applications
Year:
2010

Citing 3
Cited 0

The Structure and Dynamics of Networks: (Princeton Studies in Complexity)

The Structure and Dynamics of Networks: (Princeton Studies in Complexity)
Diffusion Maps and Coarse-Graining: A Unified Framework for Dimensionality Reduction, Graph Partitioning, and Data Set Parameterization

IEEE Transactions on Pattern Analysis and Machine Intelligence
Detecting the fuzzy clusters of complex networks

Pattern Recognition

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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.