Skip to main content
SpringerLink
Log in
Book cover

Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2017: Advances in Knowledge Discovery and Data Mining pp 762–773Cite as

CCCG: Clique Conversion Ratio Driven Clustering of Graphs

  • Conference paper
  • First Online:

  • 3189 Accesses

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10235))

Abstract

Networks have become ubiquitous in many real world applications and to cluster similar networks is an important problem. There are various properties of graphs such as clustering coefficient (CC), density, arboricity, etc. We introduce a measure, Clique Conversion Coefficient (CCC), which captures the clique forming tendency of nodes in an undirected graph. CCC could either be used as a weighted average of the values in a vector or as the vector itself. Our experiments show that CCC provides additional information about a graph in comparison to related measures like CC and density. We cluster the real world graphs using a combination of the features CCC, CC, and density and show that without CCC as one of the features, graphs with similar clique forming tendencies are not clustered together. The clustering with the use of CCC would have applications in the areas of Social Network Analysis, Protein-Protein Interaction Analysis, etc., where cliques have an important role. We perform the clustering of ego networks of the YOUTUBE network using values in CCC vector as features. The quality of the clustering is analyzed by contrasting the frequent subgraphs in each cluster. The results highlight the utility of CCC in clustering subgraphs of a large graph.

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   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

Learn about institutional subscriptions

References

  1. Luce, R.D., Perry, A.D.: A method of matrix analysis of group structure. Pschometrica 14(1), 95–116 (1949)

    Article  MathSciNet  Google Scholar 

  2. Altaf-Ul-Amin, M., Nishikata, K., Koma, T., Miyasato, T., Shinbo, Y., Arifuzzaman, M., Wada, C., Maeda, M., Oshima, T.: Prediction of protein functions based on k-cores of protein-protein interaction networks and amino acid sequences. Genome Inf. 14, 498–499 (2003)

    Google Scholar 

  3. Leskovec, J., Kleinberg, J.M., Faloutsos, C.: Graph evolution: densification and shrinking diameters. TKDD 1(1) (2007)

    Google Scholar 

  4. Schwikowski, B., Uetz, P., Fields, S.: A network of protein-protein interactions in yeast. Nat. Biotechnol. 18(12), 1257–1261 (2000)

    Article  Google Scholar 

  5. McAuley, J.J., Leskovec, J.: Discovering social circles in ego networks. TKDD 8, 4:1–4:28 (2014)

    Article  Google Scholar 

  6. Chen, B., Matsumoto, M., Wang, J., Zhang, Z., Zhang, J.: A short proof of Nash-Williams’ theorem for the arboricity of a graph. Graphs Comb. 10(1), 27–28 (1994)

    Article  MathSciNet  MATH  Google Scholar 

  7. Leskovec, J., Lang, K.J., Dasgupta, A., Mahoney, M.W.: Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters. Internet Math. 6(1), 29–123 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  8. Leskovec, J., Krevl, A.: SNAP Datasets: Stanford Large Network Dataset Collection, June 2014. http://snap.stanford.edu/data

  9. Holton, D.A., Sheehan, J.: The Petersen Graph. Cambridge University Press, Cambridge (1993). doi:10.2277/0521435943. ISBN 0-521-43594-3

  10. Kreyszig, E.: Advanced Engineering Mathematics, 4th edn. Wiley, New York (1979)

    MATH  Google Scholar 

  11. Rosen, K.H.: Discrete Mathematics and Its Applications, 7th edn. McGraw-Hill (2011). p. 655

    Google Scholar 

  12. Erdos, P., Renyi, A., Sos, V.: On a problem of graph theory. Stud. Sci. Math. 1, 215–235 (1966)

    MATH  Google Scholar 

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

    Article  Google Scholar 

  14. Awodey, S.: Isomorphisms. Oxford University Press, Category theory (2006)

    Google Scholar 

  15. Gao, X., Xiao, B., Tao, D., Li, X.: A survey of graph edit distance. Pattern Anal. Appl. 13(1), 113–129 (2010)

    Article  MathSciNet  Google Scholar 

  16. Nijssen, S., Kok, J.: A quickstart in frequent structure mining can make a difference. In: Proceedings of the SIGKDD (2004). http://www.liacs.nl/home/snijssen/gaston

  17. Mislove, A., Marcon, M., Gummadi, K.P., Druschel, P., Bhattacharjee, B.: Measurement and analysis of online social networks. In: Proceedings of the 5th ACM/Usenix Internet Measurement Conference (IMC 2007), San Diego, CA, October 2007

    Google Scholar 

  18. Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)

    Article  Google Scholar 

  19. Ester, M., Kriegel, H.-P., Sander, J., Xu, X., Simoudis, E., Han, J., Fayyad, U.M. (eds.): A density-based algorithm for discovering clusters in large spatial databases with noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96), pp. 226–231. AAAI Press (1996)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Data Science and Analytics Centre IIIT Hyderabad, Hyderabad, 500 032, India

    Prathyush Sambaturu & Kamalakar Karlapalem

Authors
  1. Prathyush Sambaturu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kamalakar Karlapalem
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Prathyush Sambaturu .

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

Sambaturu, P., Karlapalem, K. (2017). CCCG: Clique Conversion Ratio Driven Clustering of Graphs. 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 10235. Springer, Cham. https://doi.org/10.1007/978-3-319-57529-2_59

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-57529-2_59

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57528-5

  • Online ISBN: 978-3-319-57529-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation