Skip to main content

On the Exponential Ranking and Its Linear Counterpart

  • Conference paper
  • First Online:
Complex Networks & Their Applications X (COMPLEX NETWORKS 2021)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1072))

Included in the following conference series:

  • 3899 Accesses

Abstract

This paper deals with ranking algorithms for signed graphs. We analyze the algebraic properties of the exponential ranking algorithm and suggest an alternative ranking scheme that is close to the exponential ranking in several respects, but which also enjoys the property of being linear. We discuss the properties of the introduced scheme and present both algebraic and numerical evidence that it is indeed very close to the exponential ranking.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    Note that some papers (e.g., [21]) take a different notation, such that \(a_{ij}\) is defined as w(ij). We have chosen to define the elements of \(\mathrm {A}\) according to (1) to later multiply \(\mathrm {A}\) with column instead of row vectors.

  2. 2.

    We are grateful to the reviewer for bringing this interpretation to our attention.

References

  1. Agosti, M., Pretto, L.: A theoretical study of a generalized version of Kleinberg’s HITS algorithm. Inf. Retrieval 8(2), 219–243 (2005)

    Article  Google Scholar 

  2. Albert, R., Barabási, A.L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74(1), 47 (2002)

    Article  MathSciNet  MATH  Google Scholar 

  3. Alon, N., Spencer, J.H.: The Probabilistic Method. Wiley, Hoboken (2004)

    MATH  Google Scholar 

  4. Anchuri, P., Magdon-Ismail, M.: Communities and balance in signed networks: a spectral approach. In: 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, pp. 235–242. IEEE (2012)

    Google Scholar 

  5. Bonacich, P.: Power and centrality: a family of measures. Am. J. Sociol. 92(5), 1170–1182 (1987)

    Article  Google Scholar 

  6. Bonacich, P., Lloyd, P.: Calculating status with negative relations. Soc. Netw. 26(4), 331–338 (2004)

    Article  Google Scholar 

  7. Brin, S., Page, L.: The anatomy of a large-scale hypertextual web search engine. Comput. Netw. ISDN Syst. 30(1–7), 107–117 (1998). Proceedings of the Seventh International World Wide Web Conference

    Google Scholar 

  8. Chiang, K.Y., Hsieh, C.J., Natarajan, N., Dhillon, I.S., Tewari, A.: Prediction and clustering in signed networks: a local to global perspective. J. Mach. Learn. Res. 15(1), 1177–1213 (2014)

    MathSciNet  MATH  Google Scholar 

  9. Erdős, P., Rényi, A.: On the strength of connectedness of a random graph. Acta Mathematica Hungarica 12(1), 261–267 (1961)

    MathSciNet  MATH  Google Scholar 

  10. Evmenova, E., Gromov, D.: Analysis of directed signed networks: triangles inventory. In: Antonyuk, A., Basov, N. (eds.) NetGloW 2020. LNNS, vol. 181, pp. 120–132. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-64877-0_8

    Chapter  Google Scholar 

  11. Horn, R.A., Johnson, C.R.: Matrix Analysis. Cambridge University Press, Cambridge (2012)

    Book  Google Scholar 

  12. Kendall, M.G.: A new measure of rank correlation. Biometrika 30(1/2), 81–93 (1938)

    Article  MATH  Google Scholar 

  13. de Kerchove, C., van Dooren, P.: The PageTrust algorithm: how to rank web pages when negative links are allowed? In: Proceedings of the 2008 SIAM International Conference on Data Mining, pp. 346–352. SIAM (2008)

    Google Scholar 

  14. Kirkley, A., Cantwell, G.T., Newman, M.: Balance in signed networks. Phys. Rev. E 99(1), 012320 (2019)

    Article  Google Scholar 

  15. Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–632 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  16. Langville, A.N., Meyer, C.D.: Google’s PageRank and Beyond. Princeton University Press, Princeton (2011)

    MATH  Google Scholar 

  17. Leskovec, J., Huttenlocher, D., Kleinberg, J.: Signed networks in social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1361–1370 (2010)

    Google Scholar 

  18. Mishra, A., Bhattacharya, A.: Finding the bias and prestige of nodes in networks based on trust scores. In: Proceedings of the 20th International Conference on World Wide Web, pp. 567–576 (2011)

    Google Scholar 

  19. Shahriari, M., Jalili, M.: Ranking nodes in signed social networks. Soc. Netw. Anal. Min. 4(1), 172 (2014). https://doi.org/10.1007/s13278-014-0172-x

    Article  Google Scholar 

  20. Shante, V.K., Kirkpatrick, S.: An introduction to percolation theory. Adv. Phys. 20(85), 325–357 (1971)

    Article  Google Scholar 

  21. Traag, V.A., Nesterov, Y.E., Van Dooren, P.: Exponential ranking: taking into account negative links. In: Bolc, L., Makowski, M., Wierzbicki, A. (eds.) SocInfo 2010. LNCS, vol. 6430, pp. 192–202. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-16567-2_14

    Chapter  Google Scholar 

  22. Webber, W., Moffat, A., Zobel, J.: A similarity measure for indefinite rankings. ACM Trans. Inf. Syst. 28(4), 1–38 (2010)

    Article  Google Scholar 

  23. Xing, W., Ghorbani, A.: Weighted PageRank algorithm. In: Proceedings of Second Annual Conference on Communication Networks and Services Research, 2004, pp. 305–314 (2004)

    Google Scholar 

Download references

Acknowledgments

The reported study was funded by the RFBR, project number 21-011-44058.

The authors are grateful to anonymous reviewers for their pointed remarks (especially considering the extremely short time allotted for reviewing).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dmitry Gromov .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 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

Gromov, D., Evmenova, E. (2022). On the Exponential Ranking and Its Linear Counterpart. In: Benito, R.M., Cherifi, C., Cherifi, H., Moro, E., Rocha, L.M., Sales-Pardo, M. (eds) Complex Networks & Their Applications X. COMPLEX NETWORKS 2021. Studies in Computational Intelligence, vol 1072. Springer, Cham. https://doi.org/10.1007/978-3-030-93409-5_22

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-93409-5_22

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-93408-8

  • Online ISBN: 978-3-030-93409-5

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics