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Abstract

The research of complex networks has become a hot research field, and has been successfully applied in many fields such as social network analysis, protein network analysis, and link prediction. In this paper, the traditional genetic algorithm has strong randomness and weak search ability in the community identification method. A complex network community recognition algorithm based on local optimization genetic algorithm is proposed. The method uses label propagation strategy to produce a certain precision. The initial population; a combined one-way crossover strategy is proposed to avoid destroying the original excellent community structure in the cross process; the local heuristic mutation strategy is used to ensure the accuracy of the identification community, and the overall optimization ability of the algorithm is realized. Finally, the effectiveness of the algorithm is verified by experiments.

Keywords

Genetic algorithm complex network community identification

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References

Girvan

and Newman

M.E.J.

, Community structure in social and biological networks, Proceedings of the National Academy of Science 9(12) (2012), 7821–7826.

Palla

Derenyi

and Farkas

, Uncovering the overlapping community structure of complex networks in nature and society, Nature 435(7043) (2005), 814–818.

Ahn

Y.Y.

Bagrow

J.P.

and Lehmann

, Link communities reveal multiscale complexity in networks, Nature 466(7307) (2010), 761–764.

Raghavan

U.N.

Albert

and Kumara

, Near linear time algorithm to detect community sturctures in large-scale networks, Physical Review E 76(3) (2007), 36–106.

Lancichinetti

Fortunato

and Kertesz

, Detecting the overlapping and hierarchical community structure in complex networks, New Journal of Physic 11(3) (2009), 632–636.

Blondel

V.D.

Guillaume

J.L.

and Lambiotte

, Fast unfolding of communities in large networks, Journal of Statistical Mechanics Theory and Experiment 10(3) (2008), 10–18.

Newman

M.E.J.

, Fast algotithm for detecting community structure in networks, Physical Review E 69(6) (2004), 66–133.

Tasgin

Herdagdelen

and Bingol

, Community detection in complex networks using genetic algorithms, Eprint Arxiv 7(11) (2007), 49–101.

Pizzuti

, A genetic algorithm for community detection in social networks, in: Proceeding of the 10th International Conference on Parallel Problem Solving from Nature, 36(302), 2008, pp. 1081–1090.

10.

Gong

M.G.

and Jiao

L.C.

, Memetic algorithm for community detection in networks, Physical Review E 84(5) (2011), 56–101.

11.

Shang

R.H.

Bai

and Jiao

L.C.

, Community detection based on modularity and an improved genetic algorithm, Statistical Mechanics and its Applications 392(5) (2013), 1215–1231.

12.

Shi

Yan

and Wang

, A genetic algorithm for detecting communities in large-scale complex networks, Advances in Complex Systems 13(1) (2010), 3–17.

13.

Mezura-Montes

and Coello

C.A.C.

, A simple multimember evolution strategy to solve constrained optimization problems, IEEE Transactions on Evolutionary Computation 9(1) (2005), 1–17.

14.

Newman

M.E.J.

, Scientific collaborationnetworks, Physical Review E 64(1) (2001), 16–132.

15.

Guelzim

Bottani

and Bourgine

, Topological and causal structure of the yeast transcriptional regulatory network, Nature Genetics 31(1) (2002), 60–63.

16.

Lancichinetti

Fortunato

and Radicchi

, Benchmark graphs for testing community detection algorithms, Physical Review E 78(4) (2008), 46–110.

17.

Danon

Diaz-Guilera

and Duch

, Comparing community structure identification, Journal of Statistical Mechanics 2005(9) (2005), 9–18.

18.

Tasgin

Herdagdelen

and Bingol

, Community detection in complex networks using genetic algorithms, Eprint Arxiv 7(11) (2007), 4–91.

Research on complex network community identification based on partially optimized genetic algorithm

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