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Mining Associations in Large Graphs for Dynamically Incremented Marked Nodes

  • Anshul RaiEmail author
  • Zackary CrosleyEmail author
  • Srivignessh Pacham Sri SrinivasanEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10934)

Abstract

The edges between nodes of a graph describe some sort of relationship between the two nodes. In this paper, we would like to efficiently determine the relationship between specific nodes of importance, which we call marked nodes, in a large graph. These relationships obtained must be optimal, which requires us to segregate the marked nodes from the less important nodes and group them together using partitioning algorithms. We introduce an improved algorithm which allows for the efficient addition of new marked nodes to a partition without rerunning the algorithm on previously marked nodes.

Keywords

Marked nodes Partitioning Graph theory 

References

  1. 1.
    Karypis, G., Kumar, V.: METIS-unstructured graph partitioning and sparse matrix ordering system, version 2.0 (1995)Google Scholar
  2. 2.
    Kwatra, V., Schödl, A., Essa, I., Turk, G., Bobick, A.: Graphcut textures: image and video synthesis using graph cuts. In: ACM Transactions on Graphics (ToG), vol. 22, pp. 277–286. ACM (2003)CrossRefGoogle Scholar
  3. 3.
    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)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Berkhin, P., et al.: A survey of clustering data mining techniques. In: Kogan, J., Nicholas, C., Teboulle, M. (eds.) Grouping Multidimensional Data, pp. 25–71. Springer, Heidelberg (2006).  https://doi.org/10.1007/3-540-28349-8_2
  5. 5.
    Ng, R.T., Han, J.: Effcient and effective clustering methods for spatial data mining. In: Proceedings of VLDB, pp. 144–155 (1994)Google Scholar
  6. 6.
    Huang, Z.: A fast clustering algorithm to cluster very large categorical data sets in data mining. DMKD 3(8), 34–39 (1997)Google Scholar
  7. 7.
    Ding, C.H.Q., He, X., Zha, H., Gu, M., Simon, H.D.: A min-max cut algorithm for graph partitioning and data clustering. In: Proceedings IEEE International Conference on Data Mining, ICDM 2001, pp. 107–114. IEEE (2001)Google Scholar
  8. 8.
    Slota, G.M., Madduri, K., Rajamanickam, S.: Pulp: scalable multi-objective multi-constraint partitioning for small-world networks. In: 2014 IEEE International Conference on Big Data (Big Data), pp. 481–490. IEEE (2014)Google Scholar
  9. 9.
    Kruskal, J.B.: On the shortest spanning subtree of a graph and the traveling salesman problem. Proc. Am. Math. Soc. 7(1), 48–50 (1956)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Prim, R.C.: Shortest connection networks and some generalizations. Bell Labs Tech. J. 36(6), 1389–1401 (1957)CrossRefGoogle Scholar
  11. 11.
    Akoglu, L., Chau, D.H., Vreeken, J., Tatti, N., Tong, H., Faloutsos, C.: Mining connection pathways for marked nodes in large graphs. In: Proceedings of the 2013 SIAM International Conference on Data Mining, pp. 37–45. SIAM (2013)Google Scholar
  12. 12.
    Amazon Co-purchasing Network (2003). http://snap.stanford.edu/data/amazon0302.html. Accessed 02 Mar 2003

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computing, Informatics and Decision Systems EngineeringArizona State UniversityTempeUSA

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