Skip to main content
SpringerLink
Log in

Relative Neighborhood Graphs Uncover the Dynamics of Social Media Engagement

  • Conference paper
  • First Online:
Advanced Data Mining and Applications (ADMA 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10086))

Included in the following conference series:

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  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

    Chapter  Google Scholar 

  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)

    Article  Google Scholar 

  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

    Chapter  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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

    Chapter  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  10. Craig, H., Whipp, R.: Old spellings, new methods: automated procedures for indeterminate linguistic data. Literacy Linguist. Comput. 25(1), 37–52 (2010)

    Article  Google Scholar 

  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. 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. Dyen, I., Kruskal, J.B., Black, P.: An Indoeuropean classification: a lexicostatistical experiment. Trans. Am. Philos. Soc. 82(5), 1–132 (1992)

    Article  Google Scholar 

  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. Fan, W., Gordon, M.D.: The power of social media analytics. Commun. ACM 57(6), 74–81 (2014)

    Article  Google Scholar 

  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)

    Article  MathSciNet  MATH  Google Scholar 

  17. Gundecha, P., Liu, H.: Mining social media: a brief introduction. Tutorials Oper. Res. 1(4), 1–17 (2012)

    Google Scholar 

  18. Hollander, M., Wolfe, D.A., Chicken, E.: Nonparametric Statistical Methods, vol. 751. Wiley, New York (2013)

    MATH  Google Scholar 

  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, 2006

    Google Scholar 

  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)

    Article  Google Scholar 

  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

    Chapter  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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

    Chapter  Google Scholar 

  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. 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. Toussaint, G.T.: The relative neighbourhood graph of a finite planar set. Pattern Recogn. 12(4), 261–268 (1980)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Chapter  Google Scholar 

  30. Zeng, D., Chen, H., Lusch, R., Li, S.H.: Social media analytics, intelligence. IEEE Intell. Syst. 25(6), 13–16 (2010)

    Article  Google Scholar 

Download references

Acknowledgments

The authors would like to thank Shannon Hochkins and Socialbakers (www.socialbakers.com) for technical and data collection support. PM is funded by ARC Future Fellowship FT120100060. The funder had no role in this study or the preparation of this manuscript.

Author information

Authors and Affiliations

  1. Faculty of Engineering and Built Environment, School of Electrical Engineering and Computer Science, The University of Newcastle, Callaghan, New South Wales, Australia

    Natalie Jane de Vries, Ahmed Shamsul Arefin, Luke Mathieson & Pablo Moscato

  2. Maastricht University, Limburg, The Netherlands

    Benjamin Lucas

  3. Business Intelligence and Smart Services Institute (BISS) Heerlen, Limburg, The Netherlands

    Benjamin Lucas

Authors
  1. Natalie Jane de Vries
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ahmed Shamsul Arefin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luke Mathieson
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Benjamin Lucas
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Pablo Moscato
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pablo Moscato .

Editor information

Editors and Affiliations

  1. University of Technology , Sydney, New South Wales, Australia

    Jinyan Li

  2. University of Queensland , Brisbane, Australia

    Xue Li

  3. Beijing Institute of Technology , Beijing, China

    Shuliang Wang

  4. University of Western Australia , Crawley, West Australia, Australia

    Jianxin Li

  5. University of Adelaide , Adelaide, South Australia, Australia

    Quan Z. Sheng

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing AG

About this paper

Cite this paper

de Vries, N.J., Arefin, A.S., Mathieson, L., Lucas, B., Moscato, P. (2016). Relative Neighborhood Graphs Uncover the Dynamics of Social Media Engagement. In: Li, J., Li, X., Wang, S., Li, J., Sheng, Q. (eds) Advanced Data Mining and Applications. ADMA 2016. Lecture Notes in Computer Science(), vol 10086. Springer, Cham. https://doi.org/10.1007/978-3-319-49586-6_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-49586-6_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-49585-9

  • Online ISBN: 978-3-319-49586-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation