Relative Neighborhood Graphs Uncover the Dynamics of Social Media Engagement

  • Natalie Jane de Vries
  • Ahmed Shamsul Arefin
  • Luke Mathieson
  • Benjamin Lucas
  • Pablo Moscato
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10086)


In this paper, we examine if the Relative Neighborhood Graph (RNG) can reveal related dynamics of page-level social media metrics. A statistical analysis is also provided to illustrate the application of the method in two other datasets (the Indo-European Language dataset and the Shakespearean Era Text dataset). Using social media metrics on the world’s ‘top check-in locations’ Facebook pages dataset, the statistical analysis reveals coherent dynamical patterns. In the largest cluster, the categories ‘Gym’, ‘Fitness Center’, and ‘Sports and Recreation’ appear closely linked together in the RNG. Taken together, our study validates our expectation that RNGs can provide a “parameter-free" mathematical formalization of proximity. Our approach gives useful insights on user behaviour in social media page-level metrics as well as other applications.


Social networking Clustering Proximity graph Minimum spanning tree Relative neighborhood graph 


  1. 1.
    Arefin, A.S., Inostroza-Ponta, M., Mathieson, L., Berretta, R., Moscato, P.: Clustering nodes in large-scale biological networks using external memory algorithms. In: Xiang, Y., Cuzzocrea, A., Hobbs, M., Zhou, W. (eds.) ICA3PP 2011. LNCS, vol. 7017, pp. 375–386. Springer, Heidelberg (2011). doi:10.1007/978-3-642-24669-2_36 CrossRefGoogle Scholar
  2. 2.
    Arefin, A.S., Riveros, C., Berretta, R., Moscato, P.: GPU-FS-\(k\)NN: a software tool for fast and scalable \(k\)NN computation using GPUs. PLOS ONE 7(8), e44000 (2012)CrossRefGoogle Scholar
  3. 3.
    Arefin, A.S., Riveros, C., Berretta, R., Moscato, P.: kNN-Borůvka-GPU: a fast and scalable MST construction from kNN graphs on GPU. In: Murgante, B., Gervasi, O., Misra, S., Nedjah, N., Rocha, A.M.A.C., Taniar, D., Apduhan, B.O. (eds.) ICCSA 2012. LNCS, vol. 7333, pp. 71–86. Springer, Heidelberg (2012). doi:10.1007/978-3-642-31125-3_6 CrossRefGoogle Scholar
  4. 4.
    Arefin, A.S., Riveros, C., Berretta, R., Moscato, P.: The MST-kNN with paracliques. In: Chalup, S.K., Blair, A.D., Randall, M. (eds.) ACALCI 2015. LNCS (LNAI), vol. 8955, pp. 373–386. Springer, Heidelberg (2015). doi:10.1007/978-3-319-14803-8_29 Google Scholar
  5. 5.
    Arefin, A.S., Vimieiro, R., Riveros, C., Craig, H., Moscato, P.: An information theoretic clustering approach for unveiling authorship affinities in Shakespearean era plays and poems. PLOS ONE 9(10), e111445 (2014)CrossRefGoogle Scholar
  6. 6.
    Bryant, D., Filimon, F., Gray, R.D., Untangling our past: languages, trees, splits and networks. In: The Evolution of Cultural Diversity: A Phylogenetic Approach, pp. 67–83 (2005)Google Scholar
  7. 7.
    Capp, A., Inostroza-Ponta, M., Bill, D., Moscato, P., Lai, C., Christie, D., Lamb, D., Turner, S., Joseph, D., Matthews, J.: is there more than one proctitis syndrome? a revisitation using data from the trog 96.01 trial. Radiother. Oncol. 90(3), 400–407 (2009)CrossRefGoogle Scholar
  8. 8.
    Chesler, E.J., Langston, M.A.: Combinatorial genetic regulatory network analysis tools for high throughput transcriptomic data. In: Eskin, E., Ideker, T., Raphael, B., Workman, C. (eds.) RRG/RSB -2005. LNCS, vol. 4023, pp. 150–165. Springer, Heidelberg (2007). doi:10.1007/978-3-540-48540-7_13 CrossRefGoogle Scholar
  9. 9.
    Chilson, J., Ng, R.T., Wagner, A., Zamar, R.H.: Parallel computation of high-dimensional robust correlation and covariance matrices. Algorithmica 45(3), 403–431 (2006)MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Craig, H., Whipp, R.: Old spellings, new methods: automated procedures for indeterminate linguistic data. Literacy Linguist. Comput. 25(1), 37–52 (2010)CrossRefGoogle Scholar
  11. 11.
    Csardi, G., Nepusz, T.: The igraph software package for complex network research. Int. J. Complex Syst. 1695(5), 1–9 (2006)Google Scholar
  12. 12.
    Jane, N., de Vries, A., Arefin, S., Moscato, P.: Gauging heterogeneity in online consumer behaviour data: a proximity graph approach. In: Socialcom and BDCloud, pp. 485–492. IEEE (2014)Google Scholar
  13. 13.
    Dyen, I., Kruskal, J.B., Black, P.: An Indoeuropean classification: a lexicostatistical experiment. Trans. Am. Philos. Soc. 82(5), 1–132 (1992)CrossRefGoogle Scholar
  14. 14.
    Escalante, O., Perez, T., Solano, J., Stojmenovic, I.: RNG-based searching and broadcasting over internet graphs and peerto-peer computing systems. In: The 3rd ACS/IEEE International Conference on Computer Systems and Applications. IEEE (2005)Google Scholar
  15. 15.
    Fan, W., Gordon, M.D.: The power of social media analytics. Commun. ACM 57(6), 74–81 (2014)CrossRefGoogle Scholar
  16. 16.
    Grosse, I., Bernaola-Galván, P., Carpena, P., Román-Roldán, R., Oliver, J., Stanley, H.E.: Analysis of symbolic sequences using the Jensen-Shannon divergence. Phys. Rev. E 65(4), 041905 (2002)MathSciNetCrossRefMATHGoogle Scholar
  17. 17.
    Gundecha, P., Liu, H.: Mining social media: a brief introduction. Tutorials Oper. Res. 1(4), 1–17 (2012)Google Scholar
  18. 18.
    Hollander, M., Wolfe, D.A., Chicken, E.: Nonparametric Statistical Methods, vol. 751. Wiley, New York (2013)MATHGoogle Scholar
  19. 19.
    Inostroza-Ponta, M., Berretta, R., Mendes, A., Moscato, P.: An automatic graph layout procedure to visualize correlated data. In: Bramer, M. (ed.) Artificial Intelligence in Theory and Practice, IFIP 19th World Computer Congress, TC 12: IFIP AI 2006 Stream, IFIP, vol. 217, 21-24 August 2006, Santiago, Chile (2006)pages 179–188. Springer, 2006Google Scholar
  20. 20.
    Inostroza-Ponta, M., Berretta, R., Moscato, P.: QAPgrid: a two level QAP-based approach for large-scale data analysis and visualization. PLOS ONE 6(1), e14468 (2011)CrossRefGoogle Scholar
  21. 21.
    Inostroza-Ponta, M., Mendes, A., Berretta, R., Moscato, P.: An integrated QAP-based approach to visualize patterns of gene expression similarity. In: Randall, M., Abbass, H.A., Wiles, J. (eds.) ACAL 2007. LNCS (LNAI), vol. 4828, pp. 156–167. Springer, Heidelberg (2007). doi:10.1007/978-3-540-76931-6_14 CrossRefGoogle Scholar
  22. 22.
    Jones, J.J., Settle, J.E., Bond, R.M., Fariss, C.J., Marlow, C., Fowler, J.H.: Inferring tie strength from online directed behavior. PLOS One 8(1), 1–6 (2013)Google Scholar
  23. 23.
    Liu, Y., Sui, Z., Kang, C., Gao, Y.: Uncovering patterns of inter-urban trip and spatial interaction from social media check-in data. PLOS ONE 9(1), e86026 (2014)CrossRefGoogle Scholar
  24. 24.
    Lucas, B., Arefin, A.S., Jane, N., de Vries, R., Beretta, J.C., Moscato, P.: Engagement in motion: exploring short term dynamics in page-level social media metrics. In: SocialCom and BDCloud, pp. 334–341. IEEE (2014)Google Scholar
  25. 25.
    Mahata, P., Costa, W., Cotta, C., Moscato, P.: Hierarchical clustering, languages and cancer. In: Rothlauf, F., Branke, J., Cagnoni, S., Costa, E., Cotta, C., Drechsler, R., Lutton, E., Machado, P., Moore, J.H., Romero, J., Smith, G.D., Squillero, G., Takagi, H. (eds.) EvoWorkshops 2006. LNCS, vol. 3907, pp. 67–78. Springer, Heidelberg (2006). doi:10.1007/11732242_7 CrossRefGoogle Scholar
  26. 26.
    Muhlenbach, F., Lallich, S.: Discovering research communities by clustering bibliographical data. In: Huang, J.X. et al. (eds.) 2010 IEEE/WIC/ACM International Conference on Web Intelligence, Toronto, Canada, 31 August–03 September 2010, pp. 500–507. Computer Society (2010)Google Scholar
  27. 27.
    Naeni, L.M., Jane, N., de Vries, R., Reis, A.S., Arefin, R.B., Moscato, P., Identifying communities of trust, confidence in the charity, not-for-profit sector: a memetic algorithm approach. In: BDCLOUD, pp. 500–507. IEEE (2014)Google Scholar
  28. 28.
    Toussaint, G.T.: The relative neighbourhood graph of a finite planar set. Pattern Recogn. 12(4), 261–268 (1980)MathSciNetCrossRefMATHGoogle Scholar
  29. 29.
    Wiese, R., Eiglsperger, M., Kaufmann, M.: yFiles-visualization and automatic layout of graphs. In: Junger, M., Mutzel, P. (eds.) Graph Drawing Software, pp. 173–191. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  30. 30.
    Zeng, D., Chen, H., Lusch, R., Li, S.H.: Social media analytics, intelligence. IEEE Intell. Syst. 25(6), 13–16 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Natalie Jane de Vries
    • 1
  • Ahmed Shamsul Arefin
    • 1
  • Luke Mathieson
    • 1
  • Benjamin Lucas
    • 2
    • 3
  • Pablo Moscato
    • 1
  1. 1.Faculty of Engineering and Built Environment, School of Electrical Engineering and Computer ScienceThe University of NewcastleCallaghanAustralia
  2. 2.Maastricht UniversityLimburgThe Netherlands
  3. 3.Business Intelligence and Smart Services Institute (BISS) HeerlenLimburgThe Netherlands

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