Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

Bisociative Knowledge Discovery pp 166–178Cite as

  1. Home
  2. Bisociative Knowledge Discovery
  3. Chapter
Review of BisoNet Abstraction Techniques

Review of BisoNet Abstraction Techniques

  • Fang Zhou5,
  • Sébastien Mahler5 &
  • Hannu Toivonen5 
  • Chapter
  • Open Access
  • 8945 Accesses

  • 5 Citations

Part of the Lecture Notes in Computer Science book series (LNAI,volume 7250)

Abstract

BisoNets represent relations of information items as networks. The goal of BisoNet abstraction is to transform a large BisoNet into a smaller one which is simpler and easier to use, although some information may be lost in the abstraction process. An abstracted BisoNet can help users to see the structure of a large BisoNet, or understand connections between distant nodes, or discover hidden knowledge. In this paper we review different approaches and techniques to abstract a large BisoNet. We classify the approaches into two groups: preference-free methods and preference-dependent methods.

This chapter is a modified version of article “Review of Network Abstraction Techniques” in Workshop on Explorative Analytics of Information Networks, Sep 2009, Bled, Slovenia [1].

Download chapter PDF

References

  1. Zhou, F., Mahler, S., Toivonen, H.: Review of Network Abstraction Techniques. In: Workshop on Explorative Analytics of Information Networks at ECML PKDD 2009, pp. 50–63 (2009)

    Google Scholar 

  2. Dubitzky, W., Kötter, T., Schmidt, O., Berthold, M.R.: Towards Creative Information Exploration Based on Koestler’s Concept of Bisociation. In: Berthold, M.R. (ed.) Bisociative Knowledge Discovery. LNCS (LNAI), pp. 11–32. Springer, Heidelberg (2012)

    CrossRef  Google Scholar 

  3. Toussaint, G.T.: The Relative Neighbourhood Graph of a Finite Planar Set. Pattern Recogn. 12(4), 261–268 (1980)

    CrossRef  MathSciNet  Google Scholar 

  4. Jaromczyk, J., Toussaint, G.: Relative Neighborhood Graphs and Their Relatives. Proc. IEEE 80(9), 1502–1517 (1992)

    CrossRef  Google Scholar 

  5. Freeman, L.C.: Centrality in social networks: Conceptual clarification. Soc. Networks 1(3), 215–239 (1979)

    CrossRef  MathSciNet  Google Scholar 

  6. Stephenson, K.Z.M.: Rethinking centrality: Methods and examples. Soc. Networks 11(1), 1–37 (1989)

    CrossRef  MathSciNet  Google Scholar 

  7. Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)

    CrossRef  Google Scholar 

  8. Katz, L.: A new status index derived from sociometric analysis. Psychometrika 18(1), 39–43 (1953)

    CrossRef  MathSciNet  Google Scholar 

  9. Everett, M., Borgatti, S.P.: Ego network betweenness. Soc. Networks 27(1), 31–38 (2005)

    CrossRef  Google Scholar 

  10. Brandes, U.: A Faster Algorithm for Betweenness Centrality. J. Math. Sociol. 25(2), 163–177 (2001)

    CrossRef  Google Scholar 

  11. Freeman, L.C.: A Set of Measures of Centrality Based on Betweenness. Sociometry 40, 35–41 (1977)

    CrossRef  Google Scholar 

  12. Friedkin, N.E.: Theoretical Foundations for Centrality Measures. Am. J. Sociol. 96(6), 1478–1504 (1991)

    CrossRef  Google Scholar 

  13. Gert, S.: The centrality index of a graph. Psychometrika 31(4), 581–603 (1966)

    CrossRef  MathSciNet  Google Scholar 

  14. Bonacich, P.: Factoring and weighting approaches to status scores and clique identification. J. Math. Sociol. 2(1), 113–120 (1972)

    CrossRef  Google Scholar 

  15. Lawrence, P., Sergey, B., Rajeev, M., Terry, W.: The PageRank Citation Ranking: Bringing Order to the Web. Technical report, Stanford Digital Library Technologies Project (1998)

    Google Scholar 

  16. Brin, S., Page, L.: The anatomy of a large-scale hypertextual Web search engine. Comput. Netw. ISDN Syst. 30, 107–117 (1998)

    CrossRef  Google Scholar 

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

    CrossRef  MathSciNet  Google Scholar 

  18. Li, L., Shang, Y., Zhang, W.: Improvement of HITS-based algorithms on web documents. In: WWW 2002: Proc. 11th International Conf. on World Wide Web, pp. 527–535. ACM, New York (2002)

    Google Scholar 

  19. Haveliwala, T.H.: Topic-Sensitive PageRank. In: WWW 2002: Proc. 11th International Conf. World Wide Web, pp. 517–526. ACM, New York (2002)

    Google Scholar 

  20. Birnbaum, Z.W.: On the importance of different components in a multicomponent system. In: Multivariate Analysis - II, pp. 581–592. Academic Press, New York (1969)

    Google Scholar 

  21. Hong, J., Lie, C.: Joint reliability-importance of two edges in an undirected network. IEEE Trans. Reliab. 42, 17–23, 33 (1993)

    Google Scholar 

  22. Fjällström, P.O.: Algorithms for graph partitioning: A Survey. Linköping Electronic Atricles in Computer and Information Science. Linköping University Electronic Press, Linköping (1998)

    Google Scholar 

  23. Elsner, U.: Graph Partitioning - A Survey. Technical Report SFB393/97-27, Technische Universität Chemnitz (1997)

    Google Scholar 

  24. Pothen, A., Simon, H.D., Liou, K.P.: Partitioning Sparse Matrices with Eigenvectors of Graphs. SIAM J. Matrix Anal. Appl. 11(3), 430–452 (1990)

    CrossRef  MathSciNet  Google Scholar 

  25. Hendrickson, B., Leland, R.: An improved spectral graph partitioning algorithm for mapping parallel computations. SIAM J. Sci. Comput. 16(2), 452–469 (1995)

    CrossRef  MathSciNet  Google Scholar 

  26. Miller, G.L., Teng, S.H., Thurston, W., Vavasis, S.A.: Geometric Separators for Finite-Element Meshes. SIAM J. Sci. Comput. 19(2), 364–386 (1998)

    CrossRef  MathSciNet  Google Scholar 

  27. Berger, M.J., Bokhari, S.H.: A Partitioning Strategy for Nonuniform Problems on Multiprocessors. IEEE Trans. Comput. 36(5), 570–580 (1987)

    CrossRef  Google Scholar 

  28. Karypis, G., Kumar, V.: A Fast and High Quality Multilevel Scheme for Partitioning Irregular Graphs. SIAM J. Sci. Comput. 20, 359–392 (1998)

    CrossRef  MathSciNet  Google Scholar 

  29. Hendrickson, B., Leland, R.: A Multi-Level Algorithm For Partitioning Graphs. In: Proc. 1995 ACM/IEEE Conf. Supercomputing (CDROM). ACM, New York (1995)

    Google Scholar 

  30. Newman, M.E.J.: Detecting community structure in networks. Eur. Phy. J. B - Condensed Matter and Complex Systems 38(2), 321–330 (2004)

    CrossRef  MathSciNet  Google Scholar 

  31. Kernighan, B.W., Lin, S.: An Efficient Heuristic Procedure for Partitioning Graphs. Bell Sys. Tech. J. 49(1), 291–307 (1970)

    CrossRef  Google Scholar 

  32. Fiduccia, C.M., Mattheyses, R.M.: A Linear-Time Heuristic for Improving Network Partitions. In: DAC 1982: P. 19th Conf. Des. Autom., pp. 175–181. ACM, New York (1982)

    Google Scholar 

  33. Diekmann, R., Monien, B., Preis, R.: Using Helpful Sets to Improve Graph Bisections. In: Interconnection Networks and Mapping and Scheduling Parallel Computations, pp. 57–73. American Mathematical Society, USA (1995)

    CrossRef  Google Scholar 

  34. Scott, J.: Social Network Analysis: A Handbook. SAGE Publications, UK (2000)

    Google Scholar 

  35. Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004)

    Google Scholar 

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

    CrossRef  MathSciNet  Google Scholar 

  37. Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D.: Defining and identifying communities in networks. Proc. Natl. Acad. Sci. USA 101, 2658–2663 (2004)

    CrossRef  Google Scholar 

  38. Wu, F., Huberman, B.: Finding Communities in Linear Time: A Physics Approach. Eur. Phys. J. B - Condensed Matter and Complex Systems 38, 331–338 (2004)

    CrossRef  Google Scholar 

  39. Dehaspe, L., Toivonen, H., King, R.D.: Finding Frequent Substructures in Chemical Compounds. In: Agrawal, R., Stolorz, P., Piatetsky-Shapiro, G. (eds.) 4th International Conf. Knowl. Disc. Data Min., USA, pp. 30–36. AAAI Press (1998)

    Google Scholar 

  40. Holder, L.B., Cook, D.J., Djoko, S.: Substructure Discovery in the SUBDUE System. In: Proc. AAAI Workshop Knowl. Disc. Databases, pp. 169–180. AAAI, Menlo Park (1994)

    Google Scholar 

  41. Cook, D.J., Holder, L.B.: Substructure Discovery Using Minimum Description Length and Background Knowledge. J. Artif. Intell. Res. 1, 231–255 (1994)

    CrossRef  Google Scholar 

  42. Yoshida, K., Motoda, H.: CLIP: Concept Learning from Inference Patterns. Artif. Intell. 75(1), 63–92 (1995)

    CrossRef  Google Scholar 

  43. Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proc. 20th International Conf. Very Large Data Bases, VLDB 1994, pp. 487–499. Morgan Kaufmann, San Francisco (1994)

    Google Scholar 

  44. Inokuchi, A., Washio, T., Motoda, H.: An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 13–23. Springer, Heidelberg (2000)

    CrossRef  Google Scholar 

  45. Kuramochi, M., Karypis, G.: Frequent Subgraph Discovery. In: Proc. 2001 IEEE International Conf. Data Min., ICDM 2001, pp. 313–320. IEEE Computer Society, Washington, DC (2001)

    CrossRef  Google Scholar 

  46. Kuramochi, M., Karypis, G.: An Efficient Algorithm for Discovering Frequent Subgraphs. IEEE Trans. on Knowl. and Data Eng. 16(9), 1038–1051 (2004)

    CrossRef  Google Scholar 

  47. Yan, X., Han, J.: gSpan: Graph-Based Substructure Pattern Mining. In: Proceedings of the 2002 IEEE International Conf. Data Min., pp. 721–724. IEEE Computer Society, Washington, DC (2002)

    Google Scholar 

  48. Yan, X., Han, J.: CloseGraph: Mining Closed Frequent Graph Patterns. In: KDD 2003: Proc. 9th ACM SIGKDD International Conf. Knowl. Disc. Data Min., pp. 286–295. ACM, New York (2003)

    Google Scholar 

  49. Huan, J., Wang, W., Prins, J., Yang, J.: SPIN: Mining Maximal Frequent Subgraphs from Graph Databases. In: KDD 2004: Proc. 10th ACM SIGKDD International Conf. Knowl. Disc Data Min., pp. 581–586. ACM, New York (2004)

    CrossRef  Google Scholar 

  50. Bringmann, B., Nijssen, S.: What Is Frequent in a Single Graph? In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 858–863. Springer, Heidelberg (2008)

    CrossRef  Google Scholar 

  51. Fiedler, M., Borgelt, C.: Subgraph Support in a Single Large Graph. In: ICDMW 2007: Proc. 7th IEEE International Conf. Data Min. Workshops, pp. 399–404. IEEE Computer Society, Washington, DC (2007)

    Google Scholar 

  52. Fiedler, M., Borgelt, C.: Support Computation for Mining Frequent Subgraphs in a Single Graph. In: Proc. 5th Int. Workshop on Mining and Learning with Graphs, MLG 2007, Florence, Italy, pp. 25–30 (2007)

    Google Scholar 

  53. Kuramochi, M., Karypis, G.: Finding Frequent Patterns in a Large Sparse Graph. Data Min. Knowl. Disc. 11(3), 243–271 (2005)

    CrossRef  MathSciNet  Google Scholar 

  54. Grötschel, M., Monma, C.L., Stoer, M.: Design of survivable networks. In: Handbooks in Operations Research and Management Science, vol. 7, pp. 617–672 (1995)

    Google Scholar 

  55. Ramakrishnan, C., Milnor, W.H., Perry, M., Sheth, A.P.: Discovering Informative Connection Subgraphs in Multi-relational Graphs. SIGKDD Explor. Newsl. 7(2), 56–63 (2005)

    CrossRef  Google Scholar 

  56. Faloutsos, C., McCurley, K.S., Tomkins, A.: Fast Discovery of Connection Subgraphs. In: KDD 2004: Proc. 10th ACM SIGKDD International Conf. Knowl. Disc. Data Min., pp. 118–127. ACM, New York (2004)

    CrossRef  Google Scholar 

  57. Tong, H., Faloutsos, C.: Center-Piece Subgraphs: Problem Definition and Fast Solutions. In: KDD 2006: Proc. 12th ACM SIGKDD International Conf. Knowl. Disc. Data Min., pp. 404–413. ACM, New York (2006)

    CrossRef  Google Scholar 

  58. Sevon, P., Eronen, L., Hintsanen, P., Kulovesi, K., Toivonen, H.: Link Discovery in Graphs Derived from Biological Databases. In: Leser, U., Naumann, F., Eckman, B. (eds.) DILS 2006. LNCS (LNBI), vol. 4075, pp. 35–49. Springer, Heidelberg (2006)

    CrossRef  Google Scholar 

  59. Hintsanen, P., Toivonen, H.: Finding reliable subgraphs from large probabilistic graphs. Data Min. Knowl. Discov. 17, 3–23 (2008)

    CrossRef  MathSciNet  Google Scholar 

  60. Raedt, L.D., Kimmig, A., Toivonen, H.: ProbLog: A Probabilistic Prolog and its Application in Link Discovery. In: Proc. 20th International Joint Conf. Artif. Intel., pp. 2468–2473. AAAI Press, Menlo Park (2007)

    Google Scholar 

  61. Raedt, L., Kersting, K., Kimmig, A., Revoredo, K., Toivonen, H.: Compressing probabilistic Prolog programs. Mach. Learn. 70(2-3), 151–168 (2008)

    CrossRef  Google Scholar 

  62. Lin, S., Chalupsky, H.: Unsupervised Link Discovery in Multi-relational Data via Rarity Analysis. In: ICDM 2003: Proc. 3rd IEEE International Conf. Data Min., p. 171. IEEE Computer Society, Washington, DC (2003)

    Google Scholar 

  63. White, S., Smyth, P.: Algorithms for Estimating Relative Importance in Networks. In: KDD 2003: Proc. 9th ACM SIGKDD International Conf. Knowl. Disc. Data Min., pp. 266–275. ACM, New York (2003)

    Google Scholar 

  64. Jeh, G., Widom, J.: Scaling Personalized Web Search. In: WWW 2003: Proc. 12th International Conf. World Wide Web, pp. 271–279. ACM, New York (2003)

    Google Scholar 

  65. Forgaras, D., Rácz, B., Csalogány, K., Sarlós, T.: Towards Scaling Fully Personalized PageRank: Algorithms, Lower Bounds and Experiments. Internet Mathematics 2(3), 335–358 (2005)

    MathSciNet  MATH  Google Scholar 

  66. Ullmann, J.R.: An Algorithm for Subgraph Isomorphism. J. ACM 23(1), 31–42 (1976)

    CrossRef  MathSciNet  Google Scholar 

  67. Cordella, L.P., Foggia, P., Sansone, C., Vento, M.: A (Sub)Graph Isomorphism Algorithm for Matching Large Graphs. IEEE Trans. Pattern Anal. 26(10), 1367–1372 (2004)

    CrossRef  Google Scholar 

  68. Shasha, D., Wang, J.T.L., Giugno, R.: Algorithmics and Applications of Tree and Graph Searching. In: PODS 2002: Proc. 21st ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 39–52. ACM, New York (2002)

    Google Scholar 

  69. Yan, X., Yu, P.S., Han, J.: Graph Indexing: A Frequent Structure-based Approach. In: SIGMOD 2004: Proc. 2004 ACM SIGMOD International Conf. Management of Data, pp. 335–346. ACM, New York (2004)

    CrossRef  Google Scholar 

  70. He, H., Singh, A.K.: Closure-Tree: An Index Structure for Graph Queries. In: ICDE 2006: Proc. 22nd International Conf. Data Eng., p. 38. IEEE Computer Society, Los Alamitos (2006)

    Google Scholar 

  71. Tian, Y., Mceachin, R.C., Santos, C., States, D.J., Patel, J.M.: SAGA: a subgraph matching tool for biological graphs. Bioinformatics 23(2), 232–239 (2007)

    CrossRef  Google Scholar 

  72. Bunke, H., Shearer, K.: A graph distance metric based on the maximal common subgraph. Pattern Recogn. Lett. 19(3-4), 255–259 (1998)

    CrossRef  Google Scholar 

  73. Fernández, M.-L., Valiente, G.: A graph distance metric combining maximum common subgraph and minimum common supergraph. Pattern Recogn. Lett. 22(6-7), 753–758 (2001)

    CrossRef  Google Scholar 

  74. Yan, X., Yu, P.S., Han, J.: Substructure Similarity Search in Graph Databases. In: SIGMOD 2005: Proc. 2005 ACM SIGMOD International Conf. Management of Data, pp. 766–777. ACM, New York (2005)

    CrossRef  Google Scholar 

  75. Yan, X., Zhu, F., Han, J., Yu, P.S.: Searching Substructures with Superimposed Distance. In: ICDE 2006: Proc. 22nd International Conf. Data Eng. IEEE Computer Society, Washington, DC (2006)

    Google Scholar 

  76. Tian, Y., Patel, J.M.: TALE: A Tool for Approximate Large Graph Matching. In: Proc. 2008 IEEE 24th International Conf. Data Eng., pp. 963–972. IEEE Computer Society, Los Alamitos (2008)

    CrossRef  Google Scholar 

  77. Williams, D., Huan, J., Wang, W.: Graph Database Indexing Using Structured Graph Decomposition. In: Proc. 2007 IEEE 23rd International Conf. Data Eng, pp. 976–985. IEEE Computer Society Press, Los Alamitos (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Helsinki Institute for Information Technology HIIT, University of Helsinki, P.O. Box 68, FI-00014, Finland

    Fang Zhou, Sébastien Mahler & Hannu Toivonen

Authors
  1. Fang Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sébastien Mahler
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hannu Toivonen
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer and Information Science, University of Konstanz, Konstanz, Germany

    Michael R. Berthold

Rights and permissions

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 2.5 International License (http://creativecommons.org/licenses/by-nc/2.5/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

Reprints and Permissions

Copyright information

© 2012 The Author(s)

About this chapter

Cite this chapter

Zhou, F., Mahler, S., Toivonen, H. (2012). Review of BisoNet Abstraction Techniques. In: Berthold, M.R. (eds) Bisociative Knowledge Discovery. Lecture Notes in Computer Science(), vol 7250. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31830-6_12

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-31830-6_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31829-0

  • Online ISBN: 978-3-642-31830-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

Not affiliated

Springer Nature

© 2023 Springer Nature