Skip to main content
SpringerLink
Log in

Edge Role Discovery via Higher-Order Structures

  • Conference paper
  • First Online:
Advances in Knowledge Discovery and Data Mining (PAKDD 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10234))

Included in the following conference series:

Abstract

Previous work in network analysis has focused on modeling the roles of nodes in graphs. In this paper, we introduce edge role discovery and propose a framework for learning and extracting edge roles from large graphs. We also propose a general class of higher-order role models that leverage network motifs. This leads us to develop a novel edge feature learning approach for role discovery that begins with higher-order network motifs and automatically learns deeper edge features. All techniques are parallelized and shown to scale well. They are also efficient with a time complexity of \(\mathcal {O}(|E|)\). The experiments demonstrate the effectiveness of our model for a variety of ML tasks such as improving classification and dynamic network analysis.

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 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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.

    4-vertex induced subgraphs (graphlets, motifs) and larger.

  2. 2.

    The representation cost of correcting approximation errors.

  3. 3.

    Note \(\log _2(m)\) quantization bins are used.

  4. 4.

    We note that MDL is used in Fig. 1, though AIC/BIC gave similar results.

References

  1. Ahmed, N.K., Neville, J., Rossi, R.A., Duffield, N.: Efficient graphlet counting for large networks. In: ICDM, p. 10 (2015)

    Google Scholar 

  2. Ahmed, N.K., Neville, J., Rossi, R.A., Duffield, N., Willke, T.L.: Graphlet decomposition: framework, algorithms, and applications. KAIS 50(3), 1–32 (2016)

    Google Scholar 

  3. Ahmed, N.K., Willke, T.L., Rossi, R.A.: Estimation of local subgraph counts. In: IEEE BigData, pp. 1–10 (2016)

    Google Scholar 

  4. Akaike, H.: A new look at the statistical model identification. TOAC 19(6), 716–723 (1974)

    MathSciNet  MATH  Google Scholar 

  5. Anderson, C., Wasserman, S., Faust, K.: Building stochastic blockmodels. Soc. Netw. 14(1), 137–161 (1992)

    Article  Google Scholar 

  6. Arabie, P., Boorman, S., Levitt, P.: Constructing blockmodels: how and why. J. Math. Psychol. 17(1), 21–63 (1978)

    Article  MATH  Google Scholar 

  7. Batagelj, V., Mrvar, A., Ferligoj, A., Doreian, P.: Generalized blockmodeling with pajek. Metodoloski Zvezki 1, 455–467 (2004)

    Google Scholar 

  8. Bennett, W.R.: Spectra of quantized signals. Bell Syst. Tech. 27(3), 446–472 (1948)

    Article  MathSciNet  Google Scholar 

  9. Borgatti, S., Everett, M., Johnson, J.: Analyzing Social Networks. SAGE Publications, Thousand Oaks (2013)

    Google Scholar 

  10. Bregman, L.M.: The relaxation method of finding the common point of convex sets. USSR Comput. Math. Math. Phys. 7(3), 200–217 (1967)

    Article  MathSciNet  MATH  Google Scholar 

  11. Doreian, P., Batagelj, V., Ferligoj, A.: Generalized Blockmodeling, vol. 25. Cambridge University Press, Cambridge (2005)

    MATH  Google Scholar 

  12. Getoor, L., Taskar, B. (eds.): Introduction to Statistical Relational Learning. MIT Press, Cambridge (2007)

    MATH  Google Scholar 

  13. Grünwald, P.D.: The Minimum Description Length Principle. MIT Press, Cambridge (2007)

    Google Scholar 

  14. Henderson, K., et al.: Rolx: structural role extraction & mining in large graphs. In: KDD, pp. 1231–1239 (2012)

    Google Scholar 

  15. Holland, P.W., Laskey, K.B., Leinhardt, S.: Stochastic blockmodels: first steps. Soc. Netw. 5(2), 109–137 (1983)

    Article  MathSciNet  Google Scholar 

  16. Huffman, D.A., et al.: A method for the construction of minimum-redundancy codes. Proc. IRE 40(9), 1098–1101 (1952)

    Article  MATH  Google Scholar 

  17. Li, M., Vitányi, P.: An Introduction to Kolmogorov Complexity and Its Applications. Springer Science & Business Media, Heidelberg (2009)

    MATH  Google Scholar 

  18. Lloyd, S.: Least squares quantization in PCM. TOIT 28(2), 129–137 (1982)

    MathSciNet  MATH  Google Scholar 

  19. Lorrain, F., White, H.: Structural equivalence of individuals in social networks. J. Math. Sociol. 1(1), 49–80 (1971)

    Article  Google Scholar 

  20. Macskassy, S., Provost, F.: A simple relational classifier. In: KDD MRDM (2003)

    Google Scholar 

  21. Macskassy, S.A., Provost, F.: Classification in networked data: a toolkit and a univariate case study. JMLR 8, 935–983 (2007)

    Google Scholar 

  22. Max, J.: Quantizing for minimum distortion. TOIT 6(1), 7–12 (1960)

    MathSciNet  Google Scholar 

  23. Nowicki, K., Snijders, T.: Estimation and prediction for stochastic blockstructures. J. Am. Stat. Assoc. 96(455), 1077–1087 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  24. Oliver, B., Pierce, J., Shannon, C.E.: The philosophy of PCM. IRE 36(11), 1324–1331 (1948)

    Article  Google Scholar 

  25. Rahman, M., Hasan, M.A.: Link prediction in dynamic networks using graphlet. In: Frasconi, P., Landwehr, N., Manco, G., Vreeken, J. (eds.) ECML PKDD 2016. LNCS (LNAI), vol. 9851, pp. 394–409. Springer, Cham (2016). doi:10.1007/978-3-319-46128-1_25

    Chapter  Google Scholar 

  26. Rissanen, J.: Modeling by shortest data description. Automatica 14(5), 465–471 (1978)

    Article  MATH  Google Scholar 

  27. Rossi, R.A., Ahmed, N.K.: The network data repository with interactive graph analytics and visualization. In: AAAI (2015). http://networkrepository.com

  28. Rossi, R.A., Ahmed, N.K.: Role discovery in networks. TKDE 27(4), 1112 (2015)

    Google Scholar 

  29. Rossi, R.A., Gallagher, B., Neville, J., Henderson, K.: Role-dynamics: fast mining of large dynamic networks. In: WWW Companion, pp. 997–1006 (2012)

    Google Scholar 

  30. Rossi, R.A., Gallagher, B., Neville, J., Henderson, K.: Modeling dynamic behavior in large evolving graphs. In: WSDM, pp. 667–676 (2013)

    Google Scholar 

  31. Rossi, R.A., McDowell, L.K., Aha, D.W., Neville, J.: Transforming graph data for statistical relational learning. JAIR 45(1), 363–441 (2012)

    MATH  Google Scholar 

  32. Rossi, R.A., Zhou, R.: Parallel collective factorization for modeling large heterogeneous networks. Soc. Netw. Anal. Mining 6(1), 30 (2016)

    Article  Google Scholar 

  33. Schwarz, G., et al.: Estimating the dimension of a model. Ann. Stat. 6(2), 461–464 (1978)

    Article  MathSciNet  MATH  Google Scholar 

  34. Shannon, C.E.: A mathematical theory of communication. Bell Syst. Tech. 27(1), 379–423 (1948)

    Article  MathSciNet  MATH  Google Scholar 

  35. Van Leeuwen, J.: On the construction of Huffman trees. In: ICALP, p. 382 (1976)

    Google Scholar 

  36. Vishwanathan, S.V.N., Schraudolph, N.N., Kondor, R., Borgwardt, K.M.: Graph kernels. JMLR 11, 1201–1242 (2010)

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Intel Labs, Santa Clara, USA

    Nesreen K. Ahmed & Theodore L. Willke

  2. Palo Alto Research Center (Xerox PARC), Palo Alto, USA

    Ryan A. Rossi & Rong Zhou

Authors
  1. Nesreen K. Ahmed
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ryan A. Rossi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Theodore L. Willke
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Rong Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nesreen K. Ahmed .

Editor information

Editors and Affiliations

  1. Kangwon National University, Chuncheon, Korea (Republic of)

    Jinho Kim

  2. Seoul National University, Seoul, Korea (Republic of)

    Kyuseok Shim

  3. University of Technology Sydney, Sydney, New South Wales, Australia

    Longbing Cao

  4. KAIST, Daejeon, Korea (Republic of)

    Jae-Gil Lee

  5. University of New South Wales, Sydney, New South Wales, Australia

    Xuemin Lin

  6. Kangwon National University, Chuncheon, Korea (Republic of)

    Yang-Sae Moon

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Ahmed, N.K., Rossi, R.A., Willke, T.L., Zhou, R. (2017). Edge Role Discovery via Higher-Order Structures. In: Kim, J., Shim, K., Cao, L., Lee, JG., Lin, X., Moon, YS. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2017. Lecture Notes in Computer Science(), vol 10234. Springer, Cham. https://doi.org/10.1007/978-3-319-57454-7_23

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-57454-7_23

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57453-0

  • Online ISBN: 978-3-319-57454-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation