Skip to main content

Community Detection Algorithm Using Hypergraph Modularity

  • Conference paper
  • First Online:

Part of the book series: Studies in Computational Intelligence ((SCI,volume 943))

Abstract

We propose a community detection algorithm for hypergraphs. The main feature of this algorithm is that it can be adjusted to various scenarios depending on how often vertices in one community share hyperedges with vertices from other community.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   329.99
Price excludes VAT (USA)
  • Durable hardcover 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

Learn about institutional subscriptions

References

  1. Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theor. Exp. 10, P10008 (2008)

    Article  Google Scholar 

  2. Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70, 066111 (2004)

    Article  Google Scholar 

  3. Fortunato, S., Barthelemy, M.: Resolution limit in community detection. Proc. Natl. Acad. Sci. U.S.A. 104, 36–41 (2007)

    Article  Google Scholar 

  4. Kamiński, B., Poulin, V., Prałat, P., Szufel, P., Théberge, F.: Clustering via hypergraph modularity. PLOS ONE 14(11), e0224307 (2019)

    Article  Google Scholar 

  5. Kamiński, B., Prałat, P., Théberge, F.: Artificial Benchmark for Community Detection (ABCD)—Fast Random Graph Model with Community Structure, arXiv:2002.00843

  6. Kumar, T., Vaidyanathan, S., Ananthapadmanabhan, H., Parthasarathy, S., Ravindran, B.: A new measure of modularity in hypergraphs: theoretical insights and implications for effective clustering. In: International Conference on Complex Networks and Their Applications, Complex Networks 2019, pp. 286–297. Springer, Cham (2019)

    Google Scholar 

  7. Kumar, T., Vaidyanathan, S., Ananthapadmanabhan, H., Parthasarathy, S., Ravindran, B.: Hypergraph clustering by iteratively reweighted modularity maximization. Appl. Netw. Sci 5 (2020)

    Google Scholar 

  8. Lancichinetti, A., Fortunato, S.: Limits of modularity maximization in community detection. Phys. Rev. E 84, 066122 (2011)

    Article  Google Scholar 

  9. Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78 (2008)

    Google Scholar 

  10. Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Phys. Rev. E 69, 066133 (2004)

    Article  Google Scholar 

  11. Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026–113 (2004)

    Google Scholar 

  12. Poulin, V., Théberge, F.: Ensemble clustering for graphs. In: Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., Rocha, L. (eds.) Complex Networks and their Applications VII, COMPLEX NETWORKS 2018. Studies in Computational Intelligence, vol. 812. Springer, Cham (2018)

    Google Scholar 

  13. Théberge, F.: Summer School on Data Science Tools and Techniques in Modelling Complex Networks. https://github.com/ftheberge/ComplexNetworks2019/

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Paweł Prałat .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 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

Kamiński, B., Prałat, P., Théberge, F. (2021). Community Detection Algorithm Using Hypergraph Modularity. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications IX. COMPLEX NETWORKS 2020 2020. Studies in Computational Intelligence, vol 943. Springer, Cham. https://doi.org/10.1007/978-3-030-65347-7_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-65347-7_13

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-65346-0

  • Online ISBN: 978-3-030-65347-7

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics