Community Detection of Complex Networks Based on the Spectrum Optimization Algorithm

DOI

10.2991/sekeie-14.2014.44How to use a DOI?

Keywords

Community detection; complex networks; spectrum optimization algorithm; edge cutting model

Abstract

This paper presents a spectrum optimization algorithm for community detection of the complex networks. An edge cutting model is proposed for selecting edges to be removed from candidates by minimizing algebraic connectivity function. In this model a greedy heuristic method is used to get the lower bound of optimal value, which makes it applicable to large-scale networks. Additionally, by weighting every edge based on Fielder vector, this model can effectively reduce the disadvantage influence of periphery edges of a graph on the result. The experiments on the simulated and the real complex networks show that this algorithm can reduce the time complexity of Newman-Girvan algorithm while preserving the performance of community detection.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yueheng Sun
AU  - Shuo Zhang
AU  - Xingmao Ruan
PY  - 2014/03
DA  - 2014/03
TI  - Community Detection of Complex Networks Based on the Spectrum Optimization Algorithm
BT  - Proceedings of the 2nd International Conference on Software Engineering, Knowledge Engineering and Information Engineering (SEKEIE 2014)
PB  - Atlantis Press
SP  - 188
EP  - 191
SN  - 1951-6851
UR  - https://doi.org/10.2991/sekeie-14.2014.44
DO  - 10.2991/sekeie-14.2014.44
ID  - Sun2014/03
ER  -