Skip to main content

Generating Scaled Replicas of Real-World Complex Networks

  • Conference paper
  • First Online:
Complex Networks & Their Applications V (COMPLEX NETWORKS 2016 2016)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 693))

Included in the following conference series:

Abstract

Research on generative models plays a central role in the emerging field of network science, studying how statistical patterns found in real networks can be generated by formal rules. During the last two decades, a variety of models has been proposed with an ultimate goal of achieving comprehensive realism for the generated networks. In this study, we (a) introduce a new generator, termed ReCoN; (b) explore how models can be fitted to an original network to produce a structurally similar replica, and (c) aim for producing much larger networks than the original exemplar. In a comparative experimental study, we find ReCoN often superior to many other stateof- the-art network generation methods. Our design yields a scalable and effective tool for replicating a given network while preserving important properties at both microand macroscopic scales and (optionally) scaling the replica by orders of magnitude in size. We recommend ReCoN as a general practical method for creating realistic test data for the engineering of computational methods on networks, verification, and simulation studies. We provide scalable open-source implementations of most studied methods, including ReCoN.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /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
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others

References

  1. Aiello, W., Chung, F., Lu, L.: A random graph model for massive graphs. In: Proceedings of the thirty-second annual ACM symposium on Theory of computing, pp. 171–180. Acm (2000)

    Google Scholar 

  2. Albert, R., Barabási, A.: Statistical mechanics of complex networks. Reviews of modern physics 74(1), 47 (2002)

    Google Scholar 

  3. An, W.: Fitting ERGMs on big networks. Social Science Research 59, 107 – 119 (2016). Special issue on Big Data in the Social Sciences

    Google Scholar 

  4. Boccaletti, S., Latora, V., Moreno, Y., Chavez, M., Hwang, D.U.: Complex networks: Structure and dynamics. Physics reports 424(4), 175–308 (2006)

    Google Scholar 

  5. Caldarelli, G., Vespignani, A.: Large scale structure and dynamics of complex networks. World Scientific (2007)

    Google Scholar 

  6. Chakrabarti, D., Zhan, Y., Faloutsos, C.: R-MAT: A recursive model for graph mining. In: Proc. 4th SIAM Intl. Conf. on Data Mining (SDM). SIAM, Orlando, FL (2004)

    Google Scholar 

  7. Chakrabarti, D., Zhan, Y., Faloutsos, C.: R-MAT: A recursive model for graph mining. Computer Science Department p. 541 (2004)

    Google Scholar 

  8. Clauset, A., Shalizi, C.R., Newman, M.E.: Power-law distributions in empirical data. SIAM review 51(4), 661–703 (2009)

    Google Scholar 

  9. Dasari, N.S., Ranjan, D., Zubair, M.: ParK: An efficient algorithm for k-core decomposition on multicore processors. In: 2014 IEEE International Conference on Big Data, Big Data 2014, Washington, DC, USA, October 27-30, 2014, pp. 9–16. IEEE (2014)

    Google Scholar 

  10. Fortunato, S.: Benchmark graphs to test community detection algorithms. URL https: //sites.google.com/site/santofortunato/inthepress2

    Google Scholar 

  11. Geisberger, R., Sanders, P., Schultes, D.: Better approximation of betweenness centrality. In: ALENEX, pp. 90–100. SIAM (2008)

    Google Scholar 

  12. Goldenberg, A., Zheng, A.X., Fienberg, S.E., Airoldi, E.M.: A survey of statistical network models. Foundations and TrendsR in Machine Learning 2(2), 129–233 (2010)

    Google Scholar 

  13. Gutfraind, A., Meyers, L., Safro, I.: Musketeer: Multiscale entropic network generator. URL https://people.cs.clemson.edu/˜isafro/musketeer/index.html

    Google Scholar 

  14. Gutfraind, A., Safro, I., Meyers, L.A.: Multiscale network generation. In: 18th International Conference on Information Fusion, FUSION 2015, Washington, DC, USA, July 6-9, 2015, pp. 158–165 (2015)

    Google Scholar 

  15. Hamann, M., Lindner, G., Meyerhenke, H., Staudt, C.L., Wagner, D.: Structure-preserving sparsification methods for social networks. Social Netw. Analys. Mining 6(1), 22:1–22:22 (2016)

    Google Scholar 

  16. Kolda, T.G., Pinar, A., Plantenga, T., Seshadhri, C.: A scalable generative graph model with community structure. arXiv preprint arXiv:1302.6636 (2013)

    Google Scholar 

  17. Krioukov, D., Papadopoulos, F., Kitsak, M., Vahdat, A., Boguñá, M.: Hyperbolic geometry of complex networks. Physical Review E 82, 036,106 (2010)

    Google Scholar 

  18. Lancichinetti, A., Fortunato, S.: Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Physical Review E 80(1), 016,118 (2009)

    Google Scholar 

  19. Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Physical Review E 78(4), 046,110 (2008)

    Google Scholar 

  20. Leskovec, J., Faloutsos, C.: Scalable modeling of real graphs using kronecker multiplication. In: Proc. 24th Intl. Conference on Machine learning, pp. 497–504. ACM (2007)

    Google Scholar 

  21. von Looz, M., Meyerhenke, H., Prutkin, R.: Generating random hyperbolic graphs in subquadratic time. In: Algorithms and Computation - 26th International Symposium, ISAAC 2015, Nagoya, Japan, December 9-11, 2015, Proceedings, pp. 467–478 (2015)

    Google Scholar 

  22. Milo, R., Kashtan, N., Itzkovitz, S., Newman, M.E.J., Alon, U.: On the uniform generation of random graphs with prescribed degree sequences. eprint arXiv:cond-mat/0312028 (2003)

    Google Scholar 

  23. Newman, M.: Networks: an introduction. Oxford University Press (2010)

    Google Scholar 

  24. P. Erdős, A.R.: On the Evolution of Random Graphs. Publication of the Mathematical Institute of the Hungarian Academy of Sciences (1960)

    Google Scholar 

  25. Robins, G., Pattison, P., Kalish, Y., Lusher, D.: An introduction to exponential random graph (p*) models for social networks. Social Networks 29(2), 173 – 191 (2007). Special Section: Advances in Exponential Random Graph (p*) Models

    Google Scholar 

  26. Schlauch, W.E., Horvát, E.Á ., Zweig, K.A.: Different flavors of randomness: comparing random graph models with fixed degree sequences. Social Network Analysis and Mining 5(1), 1–14 (2015). DOI 10.1007/s13278-015-0267-z

    Google Scholar 

  27. Snijders, T.A.: The statistical evaluation of social network dynamics. Sociological methodology 31(1), 361–395 (2001)

    Google Scholar 

  28. Staudt, C., Hamann, M., Safro, I., Gutfraind, A., Meyerhenke, H.: Generating Scaled Replicas of Real-World Complex Networks. Tech. rep., arXiv (2016). URL http://arxiv.org/abs/1609.02121. ArXiv:1609.02121

    Google Scholar 

  29. Staudt, C.L.: Algorithms and software for the analysis of large complex networks. Ph.D. thesis, Karlsruhe Institute of Technology (2016). DOI 10.5445/IR/1000056470

    Google Scholar 

  30. Staudt, C.L., Meyerhenke, H.: Engineering parallel algorithms for community detection in massive networks. IEEE Trans. on Parallel and Distributed Systems 27(1), 171–184 (2016)

    Google Scholar 

  31. Staudt, C.L., Sazonovs, A., Meyerhenke, H.: NetworKit: A tool suite for large-scale network analysis. Network Science To appear

    Google Scholar 

  32. Traud, A.L., Mucha, P.J., Porter, M.A.: Social structure of facebook networks. Physica A: Statistical Mechanics and its Applications 391(16), 4165–4180 (2012)

    Google Scholar 

  33. Viger, F., Latapy, M.: Random generation of large connected simple graphs with prescribed degree distribution. In: 11th International Conference on Computing and Combinatorics. Kunming, Yunnan, Chine (2005)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to Christian L. Staudt , Michael Hamann , Ilya Safro , Alexander Gutfraind or Henning Meyerhenke .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Staudt, C.L., Hamann, M., Safro, I., Gutfraind, A., Meyerhenke, H. (2017). Generating Scaled Replicas of Real-World Complex Networks. In: Cherifi, H., Gaito, S., Quattrociocchi, W., Sala, A. (eds) Complex Networks & Their Applications V. COMPLEX NETWORKS 2016 2016. Studies in Computational Intelligence, vol 693. Springer, Cham. https://doi.org/10.1007/978-3-319-50901-3_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-50901-3_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-50900-6

  • Online ISBN: 978-3-319-50901-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics