Skip to main content
SpringerLink
Log in

Prominence and Dominance in Networks

  • Conference paper
  • First Online:
Knowledge Engineering and Knowledge Management (EKAW 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11313))

Included in the following conference series:

Abstract

Topographic prominence and dominance were recently developed to quantify the relative importance of mountain peaks. Instead of simply using the height to characterize a mountain, they provide a more meaningful description based on vertical and horizontal distances in the neighborhood. In this paper, we propose structural prominence and dominance for networks, an adaptation of the topographic measures, for the detection of nodes with strong local importance. We create a network “landscape” which is generated by a node’s height and distance to other nodes in the network. We ground our proposed measures on the task of predicting award winners with high and sustainable impact in a co-authorship network. Our experiments show that our measures provide information about a graph, that is not provided by other graph measures.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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.

    https://awards.acm.org/fellows.

  2. 2.

    https://awards.acm.org/fellows/nominations.

  3. 3.

    http://dblp.org/xml/release/.

  4. 4.

    https://www.openacademic.ai/oag/.

  5. 5.

    https://awards.acm.org/fellows/award-winners.

  6. 6.

    http://web.cs.ucla.edu/~palsberg/h-number.html (accessed on 2018-05-09).

References

  1. Baeza-Yates, R.A., Ribeiro-Neto, B.: Modern Information Retrieval. Addison-Wesley Longman Publishing Co. Inc., Boston (1999)

    Google Scholar 

  2. Brandes, U., Freeman, L.C., Wagner, D.: Social networks. In: Tamassia, R. (ed.) Handbook of Graph Drawing and Visualization, pp. 805–840. CRC Press, Boca Raton (2014)

    Google Scholar 

  3. Brandes, U., Pich, C.: Centrality estimation in large networks. Int. J. Bifurc. Chaos 17(07), 2303–2318 (2007)

    Article  MathSciNet  Google Scholar 

  4. Dong, Y., Johnson, R.A., Chawla, N.V.: Can scientific impact be predicted? IEEE Trans. Big Data 2(1), 18–30 (2016)

    Article  Google Scholar 

  5. Egghe, L.: Theory and practise of the g-index. Scientometrics 69(1), 131–152 (2006)

    Article  MathSciNet  Google Scholar 

  6. Fawcett, T.: ROC graphs: notes and practical considerations for researchers. Mach. Learn. 31(1), 1–38 (2004)

    MathSciNet  Google Scholar 

  7. Freeman, L.C.: A set of measures of centrality based on betweenness. Sociometry 40(1), 35–41 (1977)

    Article  Google Scholar 

  8. Freeman, L.C.: Centrality in social networks conceptual clarification. Soc. Netw. 1(3), 215–239 (1978)

    Article  Google Scholar 

  9. Gould, P.R.: On the geographical interpretation of eigenvalues. Trans. Inst. Br. Geogr. 42, 53–86 (1967)

    Article  Google Scholar 

  10. He, H., Garcia, E.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009). https://doi.org/10.1109/TKDE.2008.239

    Article  Google Scholar 

  11. Helman, A.: The Finest Peaks - Prominence and Other Mountain Measures. Trafford Publishing, Victoria (2005)

    Google Scholar 

  12. Hirsch, J.E.: An index to quantify an individual’s scientific research output. Proc. Natl. Acad. Sci. U.S.A. 102(46), 16569 (2005)

    Article  Google Scholar 

  13. Japkowicz, N., Shah, M.: Evaluating Learning Algorithms: A Classification Perspective. Cambridge University Press, New York (2011)

    Book  Google Scholar 

  14. Knoke, D., Burt, R.S.: Prominence. In: Applied Network Analysis, pp. 195–222 (1983)

    Google Scholar 

  15. Liu, X.Y., Wu, J., Zhou, Z.H.: Exploratory undersampling for class-imbalance learning. IEEE Trans. Syst. Man Cybern. Part B (Cybern.) 39(2), 539–550 (2009)

    Article  Google Scholar 

  16. Macek, B.E., Scholz, C., Atzmueller, M., Stumme, G.: Anatomy of a conference. In: Proceedings of the 23rd ACM Conference on Hypertext and Social Media, pp. 245–254. ACM (2012)

    Google Scholar 

  17. Maizlish, A.: Prominence and orometrics: a study of the measurement of mountains. WWW document (2003). http://www.peaklist.org/theory/theory.html

  18. Mattmüller, C.R.: Zur orographischen gliederung von gebirgen. Zeitschrift für Geomorphologie 55(1), 109–140 (2011)

    Article  Google Scholar 

  19. Rawat, S., Meena, S.: Publish or perish: where are we heading? J. Res. Med. Sci.: Off. J. Isfahan Univ. Med. Sci. 19(2), 87 (2014)

    Google Scholar 

  20. Staff, C.: Acm fellows inducted. Commun. ACM 57(2), 22–22 (2014)

    Article  Google Scholar 

  21. Thöni, C.: Wie viele berge gibt es in den schweizer alpen? - von schartenhöhe und dominanz. wissenschaft und bergwelt. Die Alpen, pp. 26–28 (2003)

    Google Scholar 

  22. Tukey, J.W.: Bias and confidence in not-quite large samples. Ann. Math. Statist. 29, 614 (1958)

    Article  Google Scholar 

  23. Vargas, S., Castells, P.: Rank and relevance in novelty and diversity metrics for recommender systems. In: Proceedings of the Fifth ACM Conference on Recommender Systems, pp. 109–116. ACM (2011)

    Google Scholar 

  24. Vercoustre, A.M., Thom, J.A., Pehcevski, J.: Entity ranking in wikipedia. In: Proceedings of the 2008 ACM Symposium on Applied Computing, pp. 1101–1106. ACM (2008)

    Google Scholar 

  25. Wang, C., Han, J., Jia, Y., Tang, J., Zhang, D., Yu, Y., Guo, J.: Mining advisor-advisee relationships from research publication networks. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 203–212. ACM (2010)

    Google Scholar 

  26. Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications, vol. 8. Cambridge University Press, New York (1994)

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Knowledge and Data Engineering Group, University of Kassel, Wilhelmshöher Allee 73, 34121, Kassel, Germany

    Andreas Schmidt & Gerd Stumme

Authors
  1. Andreas Schmidt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gerd Stumme
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Andreas Schmidt .

Editor information

Editors and Affiliations

  1. Université Côte d’Azur, CNRS, Inria, I3S, Sophia Antipolis, France

    Catherine Faron Zucker

  2. Fondazione Bruno Kessler, Trento, Italy

    Chiara Ghidini

  3. University of Lorraine, CNRS, Inria, LORIA, Nancy, France

    Amedeo Napoli

  4. University of Lorraine, CNRS, Inria, LORIA, Nancy, France

    Yannick Toussaint

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Schmidt, A., Stumme, G. (2018). Prominence and Dominance in Networks. In: Faron Zucker, C., Ghidini, C., Napoli, A., Toussaint, Y. (eds) Knowledge Engineering and Knowledge Management. EKAW 2018. Lecture Notes in Computer Science(), vol 11313. Springer, Cham. https://doi.org/10.1007/978-3-030-03667-6_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-03667-6_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03666-9

  • Online ISBN: 978-3-030-03667-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation