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Visual analysis for evaluation of community detection algorithms

Claudio DG Linhares, Jean R Ponciano, Fabíola SF Pereira, Luis E C Rocha (UGent) , Jose Gustavo S Paiva and Bruno AN Travençolo
(2020) MULTIMEDIA TOOLS AND APPLICATIONS. 79. p.17645-17667
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Abstract
Networks are often used to model the structure of interactions between parts of a system. One important characteristic of a network is the so-called network community structures that are groups of nodes more connected between themselves than with nodes from other groups. Such community structure is fundamental to better understand the organization of networks. Although there are several community detection algorithms in the literature, choosing the most appropriate for a specific task is not always trivial. This paper introduces a methodology to analyze the performance of community detection algorithms using network visualization. We assess the methodology using two widely adopted community detection algorithms: Infomap and Louvain. We apply both algorithms to four real-world networks with a variety of characteristics to demonstrate the usefulness and generality of the methodology. We discuss the performance of these algorithms and show how the user may use statistical and visual analytics to identify the most appropriate network community detection algorithm for a certain network analysis task.
Keywords
Media Technology, Computer Networks and Communications, Hardware and Architecture, Software, Complex networks, Network community detection, Information visualization, COMPLEX NETWORKS

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MLA
Linhares, Claudio DG, et al. “Visual Analysis for Evaluation of Community Detection Algorithms.” MULTIMEDIA TOOLS AND APPLICATIONS, vol. 79, 2020, pp. 17645–67, doi:10.1007/s11042-020-08700-4.
APA
Linhares, C. D., Ponciano, J. R., Pereira, F. S., Rocha, L. E. C., Paiva, J. G. S., & Travençolo, B. A. (2020). Visual analysis for evaluation of community detection algorithms. MULTIMEDIA TOOLS AND APPLICATIONS, 79, 17645–17667. https://doi.org/10.1007/s11042-020-08700-4
Chicago author-date
Linhares, Claudio DG, Jean R Ponciano, Fabíola SF Pereira, Luis E C Rocha, Jose Gustavo S Paiva, and Bruno AN Travençolo. 2020. “Visual Analysis for Evaluation of Community Detection Algorithms.” MULTIMEDIA TOOLS AND APPLICATIONS 79: 17645–67. https://doi.org/10.1007/s11042-020-08700-4.
Chicago author-date (all authors)
Linhares, Claudio DG, Jean R Ponciano, Fabíola SF Pereira, Luis E C Rocha, Jose Gustavo S Paiva, and Bruno AN Travençolo. 2020. “Visual Analysis for Evaluation of Community Detection Algorithms.” MULTIMEDIA TOOLS AND APPLICATIONS 79: 17645–17667. doi:10.1007/s11042-020-08700-4.
Vancouver
1.
Linhares CD, Ponciano JR, Pereira FS, Rocha LEC, Paiva JGS, Travençolo BA. Visual analysis for evaluation of community detection algorithms. MULTIMEDIA TOOLS AND APPLICATIONS. 2020;79:17645–67.
IEEE
[1]
C. D. Linhares, J. R. Ponciano, F. S. Pereira, L. E. C. Rocha, J. G. S. Paiva, and B. A. Travençolo, “Visual analysis for evaluation of community detection algorithms,” MULTIMEDIA TOOLS AND APPLICATIONS, vol. 79, pp. 17645–17667, 2020.
@article{8649882,
  abstract     = {Networks are often used to model the structure of interactions between parts of a system. One important characteristic of a network is the so-called network community structures that are groups of nodes more connected between themselves than with nodes from other groups. Such community structure is fundamental to better understand the organization of networks. Although there are several community detection algorithms in the literature, choosing the most appropriate for a specific task is not always trivial. This paper introduces a methodology to analyze the performance of community detection algorithms using network visualization. We assess the methodology using two widely adopted community detection algorithms: Infomap and Louvain. We apply both algorithms to four real-world networks with a variety of characteristics to demonstrate the usefulness and generality of the methodology. We discuss the performance of these algorithms and show how the user may use statistical and visual analytics to identify the most appropriate network community detection algorithm for a certain network analysis task.},
  author       = {Linhares, Claudio DG and Ponciano, Jean R and Pereira, Fabíola SF and Rocha, Luis E C and Paiva, Jose Gustavo S and Travençolo, Bruno AN},
  issn         = {1380-7501},
  journal      = {MULTIMEDIA TOOLS AND APPLICATIONS},
  keywords     = {Media Technology,Computer Networks and Communications,Hardware and Architecture,Software,Complex networks,Network community detection,Information visualization,COMPLEX NETWORKS},
  language     = {eng},
  pages        = {17645--17667},
  title        = {Visual analysis for evaluation of community detection algorithms},
  url          = {http://dx.doi.org/10.1007/s11042-020-08700-4},
  volume       = {79},
  year         = {2020},
}
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