Finding community of brain networks based on artificial bee colony with uniform design

  • Jie Zhang
  • Xiaoshu ZhuEmail author
  • Junhong Feng
  • Yifang Yang


The brain networks can offer fundamental insights into healthy human cognition and the alteration in disease. Some neural unit modules in brain networks can provide us a great deal of useful information. It is appealing how to find these neural unit modules and how to partition the brain network into several dense modules. There are as high within-module densities as possible and as low between-module densities as possible in these dense modules. The modularity metrics can well evaluate whether a community is good or not. Therefore, we present a novel method to find community modules of brain networks in this study. It integrates uniform design into artificial bee colony (abbreviated as UABC) in order to maximize the modularity metrics. The difference between UABC and the other existing methods lies in that UABC is presented at the first time for detecting community modules. Several brain networks generated from functional MRI for studying Autism are used to test the proposed algorithm. Experimental results performing on these brain networks demonstrate that the proposed algorithm UABC can acquire better modularity and higher stability than other competing methods.


Brain networks Modularity Artificial bee colony Uniform design 



This research was supported by National Natural Science Foundation of China (No.61841603, No. 61762087), Guangxi Natural Science Foundation (No. 2018JJA170050, No.2018JJA130028, No.2018JJA170175), Scientific Research Plan Projects of Shaanxi Education Department (No. 17JK0610), and Open Foundation for Guangxi Colleges and Universities Key Lab of Complex System Optimization and Big Data Processing (No. 2017CSOBDP0301).


  1. 1.
    Almeida H et al (2011) Is there a best quality metric for graph clusters?[C]. the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part I, ECML PKDD’11. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 44–59Google Scholar
  2. 2.
    Azevedo FAC et al (2009) Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled-up primate brain [J]. J Comp Neurol 513(5):532–541Google Scholar
  3. 3.
    Betzel RF et al (2014) Changes in structural and functional connectivity among resting-state networks across the human lifespan[J]. NeuroImage 102:345–357Google Scholar
  4. 4.
    Bharti K, Singh P (2015) Chaotic gradient artificial bee colony for text clustering[J]. Soft Comput, 1–14Google Scholar
  5. 5.
    Bilal S, Abdelouahab M (2017) Evolutionary algorithm and modularity for detecting communities in networks[J]. Physica A: Stat Mech Applic 473:89–96zbMATHGoogle Scholar
  6. 6.
    Blondel VD et al (2008) Fast unfolding of communities in large networks[J]. J Stat Mech: Theor Experiment 2008(10):P10008Google Scholar
  7. 7.
    Brown JA et al (2012) The UCLA multimodal connectivity database: a web-based platform for brain connectivity matrix sharing and analysis[J]. Front Neuroinform 6:28Google Scholar
  8. 8.
    Cao Y et al (2018) An improved global best guided artificial bee colony algorithm for continuous optimization problems[J]. Clust Comput 2018(2018):1–9Google Scholar
  9. 9.
    Cui L et al (2018) Modified Gbest-guided artificial bee colony algorithm with new probability model[J]. Soft Comput 22(7):2217–2243Google Scholar
  10. 10.
    Da L, Costa F et al (2007) Characterization of complex networks: a survey of measurements[J]. Adv Phys 56(1):167–242Google Scholar
  11. 11.
    Dai C, Wang Y (2015) A new decomposition based evolutionary algorithm with uniform designs for many-objective optimization[J]. Appl Soft Comput 30(1):238–248Google Scholar
  12. 12.
    Duch J, Arenas A (2005) Community detection in complex networks using extremal optimization[J]. Phys Rev E 72(2):027104–1-027104-4Google Scholar
  13. 13.
    Feng J et al (2017) A novel chaos optimization algorithm[J]. Multimed Tools Appl 76(16):17405–17436Google Scholar
  14. 14.
    Fortunato S (2010) Community detection in graphs[J]. Phys Rep 486(3):75–174MathSciNetGoogle Scholar
  15. 15.
    Garcia JO, et al. (2018) Applications of community detection techniques to brain graphs: algorithmic considerations and implications for neural function[J]. Proc IEEEGoogle Scholar
  16. 16.
    Girvan M, Newman M (2002) Community structure in social and biological networks[J]. Proc Natl Acad Sci U S A 99(12):7821–7826MathSciNetzbMATHGoogle Scholar
  17. 17.
    Herculano-Houzel S (2009) The human brain in numbers: a linearly scaled-up primate brain[J]. Front Hum Neurosci 3:31Google Scholar
  18. 18.
    Jia L, Wang Y, Fan L (2016) An improved uniform design-based genetic algorithm for multi-objective bilevel convex programming[J]. Int J Comput Sci Eng 12(1):38–46Google Scholar
  19. 19.
    Juneja A, Rana B, Agrawal RK (2018) FMRI based computer aided diagnosis of schizophrenia using fuzzy kernel feature extraction and hybrid feature selection[J]. Multimed Tools Appl 77(3):3963–3989Google Scholar
  20. 20.
    Karaboga D, (2005) An idea based on honey bee swarm for numerical optimization, technical report - TR06[M]Google Scholar
  21. 21.
    Koenis MMG et al (2018) Association between structural brain network efficiency and intelligence increases during adolescence[J]. Hum Brain Mapp 39(2):822–836Google Scholar
  22. 22.
    Leung Y-W, Wang Y (2000) Multiobjective programming using uniform design and genetic algorithm[J]. IEEE Trans Syst Man Cybern Part C Appl Rev 30(3):293–304Google Scholar
  23. 23.
    Li Z et al (2008) Quantitative function for community detection[J]. Phys Rev E 77(3):036109–1-036109-10Google Scholar
  24. 24.
    Li Y et al (2018) Local spectral clustering for overlapping community detection[J]. ACM Trans Knowl Discov Data (TKDD) 12(2):17Google Scholar
  25. 25.
    Liu J, Liu T (2010) Detecting community structure in complex networks using simulated annealing with k-means algorithms[J]. Physica A: Stat Mech Applic 389(11):2300–2309Google Scholar
  26. 26.
    Liu J et al (2017) Complex brain network analysis and its applications to brain disorders: a survey[J]. Complexity 2017Google Scholar
  27. 27.
    Liu X, Wang Y, Liu H (2017) A hybrid genetic algorithm based on variable grouping and uniform design for global optimization[J]. J Comput 28(3):93–107Google Scholar
  28. 28.
    Mears D, Pollard HB (2016) Network science and the human brain: using graph theory to understand the brain and one of its hubs, the amygdala, in health and disease[J]. J Neurosci Res 94(6):590–605Google Scholar
  29. 29.
    Newman MEJ (2004) Fast algorithm for detecting community structure in networks[J]. Phys Rev E 69(6):066133–1-066133-5Google Scholar
  30. 30.
    Newman MEJ (2006) Finding community structure in networks using the eigenvectors of matrices[J]. Phys Rev E 74(3):036104–1-036104-22MathSciNetGoogle Scholar
  31. 31.
    Newman MEJ (2006) Modularity and community structure in networks[J]. Proc Natl Acad Sci U S A 103(23):8577–8582Google Scholar
  32. 32.
    Newman MEJ (2013) Spectral methods for community detection and graph partitioning[J]. Phys Rev E 88(4):042822–1-042822-10Google Scholar
  33. 33.
    Newman MEJ, Girvan M (2004) Finding and evaluating community structure in networks[J]. Phys Rev E 69(2):026113–1-026113-15Google Scholar
  34. 34.
    Ning J et al (2018) A food source-updating information-guided artificial bee colony algorithm[J]. Neural Comput & Applic 30(3):775–787Google Scholar
  35. 35.
    Papadakis H, Panagiotakis C, Fragopoulou P (2014) Distributed detection of communities in complex networks using synthetic coordinates[J]. Journal of Statistical Mechanics: Theory and Experiment 2014(3):P03013Google Scholar
  36. 36.
    Power JD et al (2011) Functional network organization of the human brain[J]. Neuron 72(4):665–678Google Scholar
  37. 37.
    Rahimi S, Abdollahpouri A, Moradi P (2017) A multi-objective particle swarm optimization algorithm for community detection in complex networks[J]. Swarm and Evolutionary ComputationGoogle Scholar
  38. 38.
    Reichardt J, Bornholdt S (2004) Detecting fuzzy community structures in complex networks with a Potts model[J]. Phys Rev Lett 93(21):218701–1-218701-4Google Scholar
  39. 39.
    Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations[J]. Neuroimage 52(3):1059–1069Google Scholar
  40. 40.
    Rubinov M, Sporns O (2011) Weight-conserving characterization of complex functional brain networks[J]. Neuroimage 56(4):2068–2079Google Scholar
  41. 41.
    Rudie JD et al (2013) Altered functional and structural brain network organization in autism[J]. NeuroImage: Clin 2:79–94Google Scholar
  42. 42.
    Sporns O (2011) The human connectome: a complex network [J]. Ann N Y Acad Sci 1224(1):109–125Google Scholar
  43. 43.
    Sporns O et al (2004) Organization, development and function of complex brain networks[J]. Trends Cogn Sci 8(9):418–425Google Scholar
  44. 44.
    Tian L-P et al (2018) CASNMF: a converged algorithm for symmetrical nonnegative matrix factorization[J]. Neurocomputing 275:2031–2040Google Scholar
  45. 45.
    Wang G, Shen Y, Luan E (2008) A measure of centrality based on modularity matrix[J]. Prog Nat Sci 18(8):1043–1047Google Scholar
  46. 46.
    Wang Y et al. (2009) A clustering multi-objective evolutionary algorithm based on orthogonal and uniform design: 2927–2933Google Scholar
  47. 47.
    Wu X et al (2018) GA-ADE: a novel approach based on graph algorithm to improves the detection of adverse drug events[J]. Multimed Tools Appl 77(3):3493–3507Google Scholar
  48. 48.
    Zalesky A et al (2012) Connectivity differences in brain networks[J]. Neuroimage 60(2):1055–1062Google Scholar
  49. 49.
    Zhang J, Wang Y, Feng J (2013) Attribute index and uniform design based multiobjective association rule mining with evolutionary algorithm[J]. Sci World J 2013(2013):1–16Google Scholar
  50. 50.
    Zhang J, Wang Y, Feng J (2014) A hybrid clustering algorithm based on PSO with dynamic crossover[J]. Soft Comput 18(5):961–979Google Scholar
  51. 51.
    Zheng W et al (2018) Dynamic graph learning for spectral feature selection[J]. Multimed Tools Appl 77(22):29739–29755Google Scholar
  52. 52.
    Zhou X, Zhao X, Liu Y (2018) A multiobjective discrete bat algorithm for community detection in dynamic networks[J]. Appl Intell : 1–13Google Scholar
  53. 53.
    Zhu X, Zhang J, Feng J (2015) Multi-objective particle swarm optimization based on PAM and uniform design[J]. Math Probl Eng 2015(2):1–17Google Scholar
  54. 54.
    Zhu X et al (2017) Graph PCA hashing for similarity search[J]. IEEE Trans Multimed 19(9):2033–2044Google Scholar
  55. 55.
    Zhu X, et al. (2018) One-step multi-view spectral clustering[J]. IEEE Trans Knowl Data Eng, .Google Scholar
  56. 56.
    Zhu X et al. (2018) Low-rank sparse subspace for spectral clustering[J]. IEEE Trans Knowl Data Eng: 1–12Google Scholar
  57. 57.
    Zhu X et al (2018) Local and global structure preservation for robust unsupervised spectral feature selection[J]. IEEE Trans Knowl Data Eng 30(3):517–529Google Scholar
  58. 58.
    Zhu X et al (2019) A hybrid clustering algorithm for identifying cell types from single-cell RNA-Seq data[J]. Genes 10(2):98. Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Jie Zhang
    • 1
  • Xiaoshu Zhu
    • 1
    Email author
  • Junhong Feng
    • 1
  • Yifang Yang
    • 2
  1. 1.School of Computer Science and Engineering; Guangxi Colleges and Universities Key Lab of Complex System Optimization and Big Data ProcessingYulin Normal UniversityYulinPeople’s Republic of China
  2. 2.College of Science of Xi’an Shiyou UniversityXi’anPeople’s Republic of China

Personalised recommendations