Skip to main content
SpringerLink
Log in
Book cover

International Conference on Complex Networks and Their Applications

COMPLEX NETWORKS 2019: Complex Networks and Their Applications VIII pp 81–93Cite as

Why We Need a Process-Driven Network Analysis

  • Conference paper
  • First Online:

Part of the book series: Studies in Computational Intelligence ((SCI,volume 882))

Abstract

A network representation is a powerful abstraction of a complex system, on which a full range of readily available methods from network analysis can be applied. A network representation is suitable if indirect effects are of interest: if A has an impact on B and B has an impact on C, it is assumed that also A has an impact on C. This implies that some process is flowing through the network. For a meaningful network analysis, the network process, the network representation, and the applied network measure cannot be chosen independently [3, 4, 9, 30]. We propose a process-driven perspective on network analysis, which takes into account the network process additionally to the network representation. In order to show the necessity of this approach, we collected four data sets of real-world processes. As first step, we show that the assumptions of standard network measures about the properties of a network process are not fulfilled by the real-world process data. As second step, we compare the network usage pattern by real-world processes to the usage pattern of the corresponding shortest paths and random walks. Our results support the importance of a process-driven network analysis.

This is a preview of subscription content, 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   169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   219.99
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. Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.-U.: Complex networks: structure and dynamics. Phys. Rep. 424(4–5), 175–308 (2006)

    Article  MathSciNet  Google Scholar 

  2. Bockholt, M., Zweig, K.A.: Process-driven betweenness centrality measures. In: Network Intelligence Meets User Centered Social Media Networks. Lecture Notes in Social Networks, pp. 17–33. Springer (2018)

    Google Scholar 

  3. Borgatti, S.P.: Centrality and network flow. Soc. Netw. 27(1), 55–71 (2005)

    Article  MathSciNet  Google Scholar 

  4. Butts, C.T.: Revisiting the foundations of network analysis. Science 325(5939), 414–416 (2009)

    Article  MathSciNet  Google Scholar 

  5. Choudhury, M.D., Mason, W.A., Hofman, J.M., Watts, D.J.: Inferring relevant social networks from interpersonal communication. In: Proceedings of the 19th International Conference on World Wide Web - WWW 2010. ACM Press (2010)

    Google Scholar 

  6. Christakis, N.A., Fowler, J.H.: The collective dynamics of smoking in a large social network. N. Engl. J. Med. 358(21), 2249–2258 (2008). PMID: 18499567

    Article  Google Scholar 

  7. Colizza, V., Barrat, A., Barthélemy, M., Vespignani, A.: The role of the airline transportation network in the prediction and predictability of global epidemics. Proc. Nat. Acad. Sci. 103(7), 2015–2020 (2006)

    Article  Google Scholar 

  8. De Domenico, M., Lima, A., Mougel, P., Musolesi, M.: The anatomy of a scientific rumor. Sci. Rep. 3, 2980 (2013)

    Article  Google Scholar 

  9. Dorn, I., Lindenblatt, A., Zweig, K.A.: The trilemma of network analysis. In: Proceedings of the International Conference on Advances in Social Networks Analysis and Mining (ASONAM), pp. 9–14 (2012)

    Google Scholar 

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

    Article  Google Scholar 

  11. Friedkin, N.E.: Horizons of observability and limits of informal control in organizations. Soc. Forces 62(1), 54–77 (1983)

    Article  Google Scholar 

  12. Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Nat. Acad. Sci. 99(12), 7821–7826 (2002)

    Article  MathSciNet  Google Scholar 

  13. Guille, A., Hacid, H., Favre, C., Zighed, D.A.: Information diffusion in online social networks: a survey. SIGMOD Rec. 42(2), 17–28 (2013)

    Article  Google Scholar 

  14. Guimerá, R., Amaral, L.A.N.: Modeling the world-wide airport network. Eur. Phys. J. B 38(2), 381–385 (2004)

    Article  Google Scholar 

  15. Jarušek, P., Pelánek, R.: Analysis of a simple model of problem solving times. In: Intelligent Tutoring Systems, volume 7315 of Lecture Notes in Computer Science, pp. 379–388. Springer (2012)

    Google Scholar 

  16. Koschützki, D., Lehmann, K.A., Peeters, L., Richter, S., Tenfelde-Podehl, D., Zlotowski, O.: Centrality indices. In: Brandes, U., Erlebach, T. (eds.) Network Analysis. Lecture Notes in Computer Science, vol. 3418, pp. 16–61. Springer (2005)

    Google Scholar 

  17. Milo, R.: Network motifs: simple building blocks of complex networks. Science 298(5594), 824–827 (2002)

    Article  Google Scholar 

  18. Newman, M.E.: A measure of betweenness centrality based on random walks. Soc. Netw. 27(1), 39–54 (2005)

    Article  Google Scholar 

  19. Page, L., Brin, S., Motwani, R., Winograd, T.: The pagerank citation ranking: Bringing order to the web. Technical report, Stanford InfoLab (1999)

    Google Scholar 

  20. Pastor-Satorras, R., Vespignani, A.: Epidemic spreading in scale-free networks. Phys. Rev. Lett. 86(14), 3200–3203 (2001)

    Article  Google Scholar 

  21. Pons, P., Latapy, M.: Computing communities in large networks using random walks. In: Computer and Information Sciences, pp. 284–293. Springer (2005)

    Google Scholar 

  22. RITA TransStat. Origin and Destination Survey database (DB1B) (2016). https://www.transtats.bts.gov/DatabaseInfo.asp?DB_ID=125

  23. Rocha, L.E.C., Liljeros, F., Holme, P.: Simulated epidemics in an empirical spatiotemporal network of 50,185 sexual contacts. PLoS Comput. Biol. 7(3), e1001109 (2011)

    Article  Google Scholar 

  24. Rosvall, M., Esquivel, A., Lancichinetti, A., et al.: Memory in network flows and its effects on spreading dynamics and community detection. Nat. Commun. 5, 4630 (2014). https://doi.org/10.1038/ncomms5630

    Article  Google Scholar 

  25. Scholtes, I.: When is a network a network? In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2017. ACM Press (2017)

    Google Scholar 

  26. Transport for London. Rolling Origin and Destination Survey (RODS) (2017). http://www.tfl.gov.uk/info-for/open-data-users/our-feeds

  27. Weng, L., Ratkiewicz, J., Perra, N., Gonçalves, B., Castillo, C., Bonchi, F., Schifanella, R., Menczer, F., Flammini, A.: The role of information diffusion in the evolution of social networks. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2013, pp. 356–364. ACM Press (2013)

    Google Scholar 

  28. West, R., Leskovec, J.: Human wayfinding in information networks. In: Proceedings of the 21st International Conference on World Wide Web, pp. 619–628. ACM Press (2012)

    Google Scholar 

  29. Xu, J., Wickramarathne, T.L., Chawla, N.V.: Representing higher-order dependencies in networks. Sci. Adv. 2(5), e1600028 (2016)

    Article  Google Scholar 

  30. Zweig, K.A.: Network Analysis Literacy. Springer, Vienna (2016)

    Book  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Algorithm Accountability Lab, TU Kaiserslautern, Gottlieb-Daimler-Straße 48, 67663, Kaiserslautern, Germany

    Mareike Bockholt & Katharina A. Zweig

Authors
  1. Mareike Bockholt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Katharina A. Zweig
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mareike Bockholt .

Editor information

Editors and Affiliations

  1. University of Burgundy, Dijon Cedex, France

    Hocine Cherifi

  2. Università degli Studi di Milano, Milan, Italy

    Sabrina Gaito

  3. University of Aveiro, Aveiro, Portugal

    José Fernendo Mendes

  4. Universidad Carlos III de Madrid, Leganés, Madrid, Spain

    Esteban Moro

  5. Indiana University, Bloomington, IN, USA

    Luis Mateus Rocha

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bockholt, M., Zweig, K.A. (2020). Why We Need a Process-Driven Network Analysis. In: Cherifi, H., Gaito, S., Mendes, J., Moro, E., Rocha, L. (eds) Complex Networks and Their Applications VIII. COMPLEX NETWORKS 2019. Studies in Computational Intelligence, vol 882. Springer, Cham. https://doi.org/10.1007/978-3-030-36683-4_7

Download citation

Publish with us

Policies and ethics

Search

Navigation