Skip to main content

Local Pluralistic Homophily in Networks: A New Measure Based on Overlapping Communities

  • Conference paper
  • First Online:
Cloud Computing, Big Data & Emerging Topics (JCC-BD&ET 2023)

Abstract

Pluralistic homophily is an important phenomenon in social network analysis as nodes tend to associate with others that share their same communities. In this work, we present the concept of local pluralistic homophily of a node in a network, along with a method to measure it. It is based on the assortativity index proposed by other authors. We analyze the distribution of local pluralistic homophily in different networks using publicly available datasets. We identify patterns of behavior of the proposed measure that relate to various structural and topological characteristics of a network. These findings are significant because they help better understand how pluralistic homophily affects communities. Furthermore, our results suggest possible applications of local pluralistic homophily in future research.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
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

Notes

  1. 1.

    Dates from datasets are shown on the SNAP web page except for SO which was downloaded from the site https://archive.org/details/stackexchange and communities generated until 2021.

References

  1. Ahn, Y.-Y., Bagrow, J.P., Lehmann, S.: Link communities reveal multiscale complexity in networks. Nature. 466(7307), 761–764 (2010). https://doi.org/10.1038/nature09182

  2. Barabási, A.-L.: Network Science. Cambridge University Press, USA (2016). http://networksciencebook.com/chapter/9

  3. Leskovec, J., Krevl, A.: SNAP Datasets: Stanford Large Network Dataset Collection (2014). http://snap.stanford.edu/data

  4. Newman, M.E.J.: Assortative mixing in networks. Phys. Rev. Lett. 89, 1 (2002). https://doi.org/10.1103/PhysRevLett.89.208701

  5. Newman, M.E.J.: Mixing patterns in networks. Phys. Rev. E. 67(2), 5–7 (2003). https://doi.org/10.1103/PhysRevE.67.026126

  6. Noldus, R., Mieghem, P.V.: Assortativity in complex networks. J. Complex Netw. 3(4), 507-542 (2015). ISSN: 2051-1310. https://doi.org/10.1093/comnet/cnv005, https://academic.oup.com/comnet/article-pdf/3/4/507/2328341/cnv005.pdf, https://doi.org/10.1093/comnet/cnv005

  7. Piraveenan, M., Prokopenko, M., Zomaya, A.Y.: Local assortativeness in scale-free networks. EPL. 84(2), 1–2 (2008). https://doi.org/10.1209/0295-5075/84/28002

  8. Sendiña-Nadal, I., et al.: Assortativity and leadership emerge from antipreferential attachment in heterogeneous networks. Sci. Rep. 6(1), 21297 (2016). https://doi.org/10.1038/srep21297. ISSN: 2045–2322

  9. Tan, F., Xia, Y., Zhu, B.: link prediction in complex networks: a mutual information perspective. PLOS ONE. 9(9), 6 (2014). https://doi.org/10.1371/journal.pone.0107056

  10. Thedchanamoorthy, G.: New approaches and their applications in measuring mixing patterns of complex networks. Ph.D. thesis. (2014). http://hdl.handle.net/2123/13211

  11. Yang, J., Leskovec, J.: Community-affiliation graph model for overlapping network community detection. In: 2012 IEEE 12th International Conference on Data Mining, p. 1179 (2012). https://doi.org/10.1109/ICDM.2012.139

  12. Yang, J., Leskovec, J.: Overlapping communities explain core-periphery organization of networks. Proc. IEEE. 102(12), 1897–1898 (2014). https://doi.org/10.1109/JPROC.2014.2364018

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Fernando Barraza .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Barraza, F., Ramirez, C., Fernández, A. (2023). Local Pluralistic Homophily in Networks: A New Measure Based on Overlapping Communities. In: Naiouf, M., Rucci, E., Chichizola, F., De Giusti, L. (eds) Cloud Computing, Big Data & Emerging Topics. JCC-BD&ET 2023. Communications in Computer and Information Science, vol 1828. Springer, Cham. https://doi.org/10.1007/978-3-031-40942-4_6

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-40942-4_6

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-40941-7

  • Online ISBN: 978-3-031-40942-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics