Sage Journals: Discover world-class research

Abstract

Community discovery in complex networks has become a core issue in multidisciplinary cross-disciplinary research and has been successfully applied in many areas, such as social network analysis, protein network analysis, and link prediction. This paper proposes a genetic algorithm based on adaptive cross mutation operator for complex network community mining. The population is generated by establishing fitness value calibration and adaptive cross mutation operator, and a good individual is selected from it. An improved adaptive cross mutation operator is proposed to ensure the convergence of genetic algorithm and accelerate the generation of optimal solution while maintaining the diversity of population. Finally, experiments were carried out in multiple real networks to verify the stability and efficiency of the algorithm.

Keywords

Genetic algorithm complex network community discovery

Get full access to this article

View all access options for this article.

References

Maslov

Kim

and Zaliznyak

, Detection of topological patterns in complex networks: correlation profile of the internet, Physical A: Statistical Mechanics and Its Applications 333(4) (2004), 529–540.

Zachary

W.W.

, An information flow model for conflict and fission in small groups, Journal of Anthropological Research 33(4) (1977), 452–473.

Shimobayashi

and Hall

M.N

, Making new contacts: the mTOR network in metabolism and signaling crosstalk, Nature Reviews Molecular Cell Biology 15(3) (2014), 155–162.

Shang

R.H.

Bai

and Jiao

L.C.

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

Pizzuti

, A Multiobjective genetic algorithm to find communities in complex networks, IEEE Transaction on Evolutionary Computation 16(3) (2012), 418–430.

Gong

M.G.

L.J.

Zhang

Q.F.

et al., Community detection in networks by using multiobjective evolutionary algorithm with decomposition, Physical A: Statistical Mechanics and its Applications 391(15) (2012), 4050–4060.

Shi

Yan

Z.Y.

and Cai

Y.N.

, Multi-objective community detection in complex networks, Applied Soft Computing 12(2) (2012), 850–859.

Alamsyah

and Rahardjo

, Kuspriyanto, Community detection methods in social network analysis, Proceeding of the International Conference on Internet Services Technology and Information Engineering, Bogor, Indonesia, 2014, pp. 25–253.

J.H.

and Havens

T.C.

, Fuzzy community detection in social networks using a genetic algorithm, Proceeding of IEEE International Conference on Fuzzy Systems, IEEE, Beijing, China, 2014, pp. 2039–204.

10.

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.

11.

Newman

M.E.J.

, Fast algotithm for detecting community structure in networks, Physical Review E 69(6) (2004), 066133.

12.

Raghavan

U.N.

Albert

and Kumara

, Near linear time algorithm to detect community sturctures in large-scale networks, Physical Review E 76(3) (2007), 036106.

13.

Pizzuti

, GA-NET:A genetic algorithm for community detection in social networks, Proceeding of the 10th International Conference on Parallel Problem Solving from Nature, Springer, Dortmund, Germany, 2008, pp. 1081–1090.

14.

Newman

M.E.J.

, Scientific collaboration networks. II. Shortest paths, weighted networks and centrality, Phys Rev E 64(1) (2001), 016–132.

15.

Guelzim

Bottani

Bourgine

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

16.

Danon

Diaz-Guilera

and Duch

, Comparing community structure identification, Journal of Statistical Mechanics: Theory and Experiment 2005(9) (2005), P09008.

Research on complex network community discovery based on adaptive cross mutation operator

Abstract

Keywords

Get full access to this article

References