Community Structure Division based on Immune Algorithm

doi:10.23940/ijpe.19.04.p5.11031111

Abstract

Abstract: Recently, the characteristics of complex networks and community structure have attracted attention from academia and society, and their research and applications have become increasingly important. Community structure division makes complex networks easy to understand. However, most community structure division methods often need the number of communities and have low efficiency. In this paper, an efficient method of community structure division in complex networks based on the immune algorithm is proposed. The method aims to find the core members of communities and classify other members according to core members. The individual evaluation of the core member is obtained by the affinity degree of the immune algorithm. In addition, the clone and mutation operation in the traditional immune algorithm is improved to be affected not only by the affinity but also by the iterative process. The improved immune algorithm can guarantee antibody diversity in the early stage of search and convergence in the later stage, and it then achieves faster convergence and higher precision of community structure division. Compared with traditional methods, the proposed method does not need the number of communities in advance, can get better results on real datasets, and has greater efficiency.

Key words: complex network, community structure, community division, immune algorithm

Yuling Tian. Community Structure Division based on Immune Algorithm [J]. Int J Performability Eng, 2019, 15(4): 1103-1111.

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References

1 X. Lin, M. Wang,Z. Tian, “Influence of Crops and Hedges on Soil Microbial Community Structure and Diversity of the Sloping Agricultural Land,” Safety and Environmental Engineering, Vol. 2, pp. 244-249, February 2017
2 L. Y.Zhang and P. Marbach, “Stable and Efficient Structures for the Content Production and Consumption in Information Communities,”Springer, pp. 163-173, August 2018
3 Z. Su, W. Wang,L. X. Li, “Optimal Community Structure for Social Contagions,” New Journal of Physics, Vol. 20, No. 5, pp. 1-10, May 2018
4 P. Tasgave and A. Dani, “Friend-Space: Cluster-Based Users Similar Post Friend Recommendation Technique in Social Networks,” inProceedings of the 2015 International Conference on Information Processing, pp. 658-663, Pune, India, June 2016
5 S. Jarukasemratana, T. Murata,X. Liu, “Community Detection Algorithm based on Centrality and Node Distance in Scale-Free Networks,” inProceedings of the 24th ACM Conference on Hypertext and Social Media, pp. 258-262, Paris, France, May 2013
6 A. Anand, V. K. Sihag,P. Svss, “Community Structure based on Node Traffic in Networks,” International Journal of Computer Applications, Vol. 69, No. 13, pp. 15-20, May 2013
7 B. Cecile, C. David, M. Matteo,M. Barbora, “Clustering Attributed Graphs: Models, Measures and Methods,” Network Science, Vol. 3, No. 3, pp. 408-444, January 2015
8 D. Hric, T. Peixoto,S. Fortunato, “Network Structure, Metadata and the Prediction of Missing Nodes and Annotations,” ar Xiv: 1604.00255v1, September 2016
9 M. Atzmueller, S. Doerfel,F. Mitzlaff, “Description-Oriented Community Detection using Exhaustive Subgroup Discovery,” Information Sciences, Vol. 329, pp. 965-984, February 2016
10 S. Moon, J. G. Lee,M. Kang, “Parallel Community Detection on Large Graphs with MapReduce and GraphChi,” Data and Knowledge Engineering, Vol. 104, pp. 17-31, July 2016
11 Y. Xin, Z. Q. Xie,J. Yang, “An Adaptive Random Walk Sampling Method on Dynamic Community Detection,” Expert Systems with Applications, Vol. 58, pp. 10-19, October 2016
12 M. R.Mirsaleh and M. R. Meybodi, “A Michigan Memetic Algorithm for Solving the Community Detection Problem in Complex Network,” Neurocomputing, Vol. 214, pp. 535-545, November 2016
13 C. De Bacco, E. A. Power, D. B. Larremore,C. Moore, “Community Detection, Link Prediction, and Layer Interdependence in Multilayer Networks,” Physical Review E, Vol. 95, pp. 042317, January 2017
14 R. Z.Krista and Z. Borut, “Multi-Objective Evolutionary Algorithm using Problem-Specific Genetic Operators for Community Detection in Networks,” Neural Computing and Applications, Vol. 30, pp. 1-14, February 2017
15 H. Mensah and S. Soundarajan, “Sampling Community Structure in Dynamic Social Networks,”Springer, pp. 114-126, May 2018
16 X. Zhou, X. H. Zhao, Y. H. Liu,G. Sun, “A Game Theoretic Algorithm to Detect Overlapping Community Structure in Networks,” Physics Letters A, Vol. 382, No. 13, pp. 872-879, April 2018
17 D. Lizondo, S. Rodriguez,A. Will, “An Artificial Immune Network for Distributed Demand-Side Management in Smart Grids,” Information Sciences, Vol. 438, pp. 32-45, April 2018
18 L. Sharmila and U. Sakthi, “An Artificial Immune System-based Algorithm for Abnormal Pattern in Medical Domain,” Journal of Supercomputing, Vol. 4, pp. 1-15, April 2018
19 A. Louati, S. Darmoul,S. Elkosantini,“An Artificial Immune Network to Control Interrupted Flow at a Signalized Intersection,” Information Sciences, Vol. 433-434, pp. 70-95, April 2018
20 G. Samigulina and Z. I. Samigulina, “Modified Immune Network Algorithm based on The Random Forest Approach for The Complex Objects Control,” Artificial Intelligence Review, Vol. 1, pp. 1-17, February 2018

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