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Visual analysis for evaluation of community detection algorithms

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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.

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References

  1. Albert R, Barabási AL (2002) Statistical mechanics of complex networks. Rev Mod Phys 74:47–97. https://doi.org/10.1103/RevModPhys.74.47

    Article  MathSciNet  MATH  Google Scholar 

  2. Battista GD, Eades P, Tamassia R, Tollis IG (1994) Algorithms for drawing graphs: an annotated bibliography. Comput Geom 4(5):235–282

    Article  MathSciNet  MATH  Google Scholar 

  3. Blondel VD, Guillaume JL, Lambiotte R, Lefebvre E (2008) Fast unfolding of communities in large networks. J Stat Mech: Theory Exper 2008:P10008

    Article  MATH  Google Scholar 

  4. Burch M (2017) Visual analytics of large dynamic digraphs. Inf Vis 16(3):167–178. https://doi.org/10.1177/1473871616661194

    Article  Google Scholar 

  5. Cattuto C, Van den Broeck W, Barrat A, Colizza V, Pinton JF, Vespignani A (2010) Dynamics of person-to-person interactions from distributed RFID sensor networks. PloS one 5(7):e11596

    Article  Google Scholar 

  6. Crampes M, Plantié M (2014) A unified community detection, visualization and analysis method. Advan Complex Syst, 17

  7. Costa L da F, Oliveira Jr ON, Travieso G, Rodrigues FA, Boas PRV, Antiqueira L, Viana MP, Rocha LEC (2011) Analyzing and modeling real-world phenomena with complex networks: a survey of applications. Adv Phys 60(3):329–412. https://doi.org/10.1080/00018732.2011.572452

    Article  Google Scholar 

  8. Drif A, Boukerram A (2014) Taxonomy and survey of community discovery methods in complex networks. Int J Comput Sci Eng Survey 5(4):1

    Article  Google Scholar 

  9. Dunne C, Shneiderman B (2013) Motif simplification: improving network visualization readability with fan, connector, and clique glyphs. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’13. https://doi.org/10.1145/2470654.2466444. ACM, New York, pp 3247–3256

  10. Ester M, Kriegel HP, Sander J, Xu X (1996) A density-based algorithm for discovering clusters in large spatial databases with noise. In: Kdd, vol 96, pp 226–231

  11. Fortunato S (2010) Community detection in graphs. Phys Rep 486(3):75–174. https://doi.org/10.1016/j.physrep.2009.11.002. http://www.sciencedirect.com/science/article/pii/S0370157309002841

    Article  MathSciNet  Google Scholar 

  12. Fortunato S, Barthélemy M (2007) Resolution limit in community detection. P Natl A Sci 104(1):36–41. https://doi.org/10.1073/pnas.0605965104

    Article  Google Scholar 

  13. Fortunato S, Hric D (2016) Community detection in networks: a user guide. Phys Rep 659:1–44

    Article  MathSciNet  Google Scholar 

  14. Gemmetto V, Barrat A, Cattuto C (2014) Mitigation of infectious disease at school: targeted class closure vs school closure. BMC Infectious Diseases 14(1):695. https://doi.org/10.1186/PREACCEPT-6851518521414365. http://www.biomedcentral.com/1471-2334/14/3841

    Article  Google Scholar 

  15. Génois M, Vestergaard CL, Fournet J, Panisson A, Bonmarin I, Barrat A (2015) Data on face-to-face contacts in an office building suggest a low-cost vaccination strategy based on community linkers. Netw Sci 3:326–347

    Article  Google Scholar 

  16. Gialampoukidis I, Tsikrika T, Vrochidis S, Kompatsiaris I (2016) Community detection in complex networks based on dbscan* and a martingale process. In: 2016 11th international workshop on Semantic and social media adaptation and personalization (SMAP). IEEE, pp 1–6

  17. Jarvis R, Patrick E (1973) Clustering using a similarity measure based on shared near neighbors. IEEE Trans Comput C-22(11):1025–1034

    Article  Google Scholar 

  18. Lancichinetti A, Fortunato S (2009) Community detection algorithms: a comparative analysis. Phys Rev E, 80

  19. Linhares CDG, Ponciano JR, Pereira FSF, Rocha LEC, Paiva JGS, Travençolo BAN (2019) A scalable node ordering strategy based on community structure for enhanced temporal network visualization. Comput Graph 84:185–198. https://doi.org/10.1016/j.cag.2019.08.006

    Article  Google Scholar 

  20. Linhares CDG, Travençolo BAN, Paiva JGS, Rocha LEC (2017) DyNetVis: a system for visualization of dynamic networks. Symposium Appl Comput, 187–194. https://doi.org/10.1145/3019612.3019686

  21. Mastrandrea R, Fournet J, Barrat A (2015) Contact patterns in a high school: a comparison between data collected using wearable sensors, contact diaries and friendship surveys. PLOS ONE 10(9):1–26. https://doi.org/10.1371/journal.pone.0136497

    Article  Google Scholar 

  22. Mothe J, Mkhitaryan K, Haroutunian M (2017) Community detection: comparison of state of the art algorithms. In: 2017 Computer science and information technologies (CSIT), pp 125–129. https://doi.org/10.1109/CSITechnol.2017.8312155

  23. Newman MEJ (2016) Community detection in networks: modularity optimization and maximum likelihood are equivalent. arXiv:https://arxiv.org/abs/1606.02319

  24. Orman GK, Cherifi H, Labatut V (2011) On accuracy of community structure discovery algorithms. J Convergence Inform Technol 6:283–292

    Google Scholar 

  25. Orman GK, Labatut V, Cherifi H (2012) Comparative evaluation of community detection algorithms: a topological approach. J Stat Mech: Theory Exper 2012(08):P08001. https://doi.org/10.1088/1742-5468/2012/08/p08001

    Article  Google Scholar 

  26. Perer A, Shneiderman B (2008) Integrating statistics and visualization: Case studies of gaining clarity during exploratory data analysis. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’08. https://doi.org/10.1145/1357054.1357101. ACM, New York, pp 265–274

  27. Rajpoot K, Riaz A, Majeed W, Rajpoot N (2015) Functional connectivity alterations in epilepsy from resting-state functional mri. PloS one e0134944:10. https://doi.org/10.1371/journal.pone.0134944

    Google Scholar 

  28. Rocha LEC, Liljeros F, Holme P (2011) Simulated epidemics in an empirical spatiotemporal network of 50,185 sexual contacts. PLos Comput Biol 7(3):e1001109. https://doi.org/10.1371/journal.pcbi.1001109

    Article  Google Scholar 

  29. Rosvall M, Bergstrom CT (2008) Maps of random walks on complex networks reveal community structure. Proc National Acad Sci 105(4):1118–1123. https://doi.org/10.1073/pnas.0706851105. http://www.pnas.org/content/105/4/1118.abstract

    Article  Google Scholar 

  30. Rosvall M, Bergstrom CT (2010) Mapping change in large networks. PLoS ONE 5(1):e8694. https://doi.org/10.1371/journal.pone.0008694

    Article  Google Scholar 

  31. Rosvall M, Delvenne J, Schaub MT, Lambiotte R (2017) Different approaches to community detection. arXiv:1712.06468

  32. Shneiderman B (1996) The eyes have it: a task by data type taxonomy for information visualizations. In: Proceedings 1996 IEEE Symposium on Visual Languages, pp 336–343. https://doi.org/10.1109/VL.1996.545307

  33. Stehlé J, Voirin N, Barrat A, Cattuto C, Isella L, Pinton J, Quaggiotto M, Van den Broeck W, Régis C, Lina B, Vanhems P (2011) High-resolution measurements of face-to-face contact patterns in a primary school. PLOS ONE 6(8):e23176. https://doi.org/10.1371/journal.pone.0023176

    Article  Google Scholar 

  34. Tanahashi Y, Ma KL (2012) Design considerations for optimizing storyline visualizations. IEEE Trans Vis Comput Graph 18(12):2679–2688. https://doi.org/10.1109/TVCG.2012.212

    Article  Google Scholar 

  35. Traud AL, Frost C, Mucha PJ, Porter MA (2009) Visualization of communities in networks. Chaos: an interdisciplinary. J Nonlinear Sci 19(4):041104

    Google Scholar 

  36. Vanhems P, Barrat A, Cattuto C, Pinton JF, Khanafer N, Régis C, Kim BA, Comte B, Voirin N (2013) Estimating potential infection transmission routes in hospital wards using wearable proximity sensors. PLoS One 8:e73970

    Article  Google Scholar 

  37. Vehlow C, Beck F, Auwärter P, Weiskopf D (2015) Visualizing the evolution of communities in dynamic graphs. Comput Graph Forum 34(1):277–288. https://doi.org/10.1111/cgf.12512

    Article  Google Scholar 

  38. Wang W, Street WN (2014) A novel algorithm for community detection and influence ranking in social networks. In: 2014 IEEE/ACM international conference on Advances in social networks analysis and mining (ASONAM). IEEE, pp 555–560

  39. Wang W, Wang H, Dai G, Wang H (2006) Visualization of large hierarchical data by circle packing. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’06. https://doi.org/10.1145/1124772.1124851. ACM, New York, pp 517–520

  40. Ware C (2012) Information visualization: Perception for Design, 3 edn. Morgan Kaufmann Series in Interactive Technologies. Morgan Kaufmann, San Francisco, CA USA

  41. Yang Z, Algesheimer R, Tessone CJ (2016) A comparative analysis of community detection algorithms on artificial networks. Scientific Reports, 6

  42. Yin C, Zhu S, Chen H, Zhang B, David B (2015) A method for community detection of complex networks based on hierarchical clustering. IJDSN 2015, 849140:1–849140:9

  43. Zhang QG, Liu HY, Zhang W, Guo YJ (2005) Drawing undirected graphs with genetic algorithms. In: Wang L, Chen K, Ong Y (eds) Advances in Natural Computation, Lecture Notes in Computer Science, vol. 3612, pp. 28–36. Springer Berlin Heidelberg. https://doi.org/10.1007/11539902_4

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Acknowledgements

This research was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico - CNPq [grant number 456855/2014-9] and Coordenação de Aperfeicoamento de Pessoal de Nível Superior (CAPES PrInt - Grant number 88881.311513/2018-01). The authors also thank SocioPatterns (www.sociopatterns.org) for making available the network data sets used in this paper.

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Correspondence to Claudio D. G. Linhares.

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Cláudio D. G. Linhares and Jean R. Ponciano contributed equally to this work.

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Linhares, C.D.G., Ponciano, J.R., Pereira, F.S.F. et al. Visual analysis for evaluation of community detection algorithms. Multimed Tools Appl 79, 17645–17667 (2020). https://doi.org/10.1007/s11042-020-08700-4

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