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Centrality measures-based algorithm to visualize a maximal common induced subgraph in large communication networks

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Abstract

Communication networks are ubiquitous, increasingly complex, and dynamic. Predicting and visualizing common patterns in such a huge graph data of communication network is an essential task to understand active patterns evolved in the network. In this work, the problem is to find an active pattern in a communication network which is modeled as detection of a maximal common induced subgraph (CIS). The state of the communication network is captured as a time series of graphs which has periodic snapshots of logical communications within the network. A new centrality measure is proposed to assess the variation in successive graphs and to identify the behavior of each node in the time series graph. It extents help in the process of selecting a suitable candidate vertex for maximality in each step of the proposed algorithm. This paper is a pioneer attempt in using centrality measures to detect a maximal CIS of the huge graph database, which gives promising effect in the resultant graph in terms of large number of vertices. The algorithm has polynomial time complexity, and the efficiency of the algorithm is demonstrated by a series of experiments with synthetic graph datasets of different orders. The performance of real-time datasets further ensured the competence of the proposed algorithm.

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References

  1. Akutsu T, Tamura T (2012) A polynomial-time algorithm for computing the maximum common subgraph of outerplanar graphs of bounded degree. In: Mathematical foundations of computer science 2012. Springer, Berlin, Heidelberg, pp 76–87

  2. Berlo RJV, Winterbach W, Groot MJD, Bender A, Verheijen PJ, Reinders MJ, Ridder DD (2013) Efficient calculation of compound similarity based on maximum common subgraphs and its application to prediction of gene transcript levels. Int J Bioinfor Res Appl 9(4):407–432

    Article  Google Scholar 

  3. Bunke H, Foggia P, Guidobaldi C, Sansone C, Vento M (2002) A comparison of algorithms for maximum common subgraph on randomly connected graphs. In: Structural, syntactic, and statistical pattern recognition. Springer, Berlin, Heidelberg, pp. 123–132

  4. Conte D, Foggia P, Vento M (2007) Challenging complexity of maximum common subgraph algorithms: a performance analysis of three algorithms on a wide database of graphs. J Graph Algorithms Appl 11(1):99–143

    Article  MATH  MathSciNet  Google Scholar 

  5. Cook DJ, Holder LB (2006) Mining graph data. Wiley, Hoboken

    Book  Google Scholar 

  6. Dickinson PJ, Bunke H, Dadej A, Kraetzl M (2004) Matching graphs with unique node labels. Pattern Anal Appl 7(3):243–254

    Article  MathSciNet  Google Scholar 

  7. Freeman LC (1979) Centrality in social networks conceptual clarification. Soc Netw 1(3):215–239

    Article  Google Scholar 

  8. Gross JL, Yellen J (2004) Handbook of graph theory. CRC Press, Boca Raton

    MATH  Google Scholar 

  9. McGregor JJ (1982) Backtrack search algorithms and the maximal common subgraph problem. Softw Pract Exp 12(1):23–34

    Article  MATH  Google Scholar 

  10. Rocha J, Pavlidis T (1994) A shape analysis model with applications to a character recognition system. IEEE Trans Pattern Anal Mach Intell 16(4):393–404

    Article  Google Scholar 

  11. Rutgers JH, Wolkotte PT, Holzenspies PK, Kuper J, Smit GJ (2010) An approximate maximum common subgraph algorithm for large digital circuits. In: 2010 13th Euromicro conference on digital system design: architectures, methods and tools (DSD), pp 699–705

  12. Schenker A, Last M, Bunke H, Kandel A (2004) Classification of web documents using graph matching. Int J Pattern Recognit Artif Intell 18(03):475–496

    Article  Google Scholar 

  13. Shokoufandeh A, Dickinson S (2001) A unified framework for indexing and matching hierarchical shape structures. In: Visual Form 2001. Springer, Berlin, pp 67–84

  14. Shoubridge P, Kraetzl M, WALLIS W, Bunke H (2002) Detection of abnormal change in a time series of graphs. J Interconnection Netw 3(01n02):85–101

  15. Sleiman HA, Corchuelo R (2013) A survey on region extractors from web documents. IEEE Trans Knowl Data Eng 25(9):1960–1981

    Article  Google Scholar 

  16. Sleiman HA, Corchuelo R (2014) Trinity: on using trinary trees for unsupervised web data extraction. IEEE Trans Knowl Data Eng 26(6):1544–1556

    Article  Google Scholar 

  17. Suganthan PN, Yan H (1998) Recognition of handprinted Chinese characters by constrained graph matching. Image Vis Comput 16(3):191–201

    Article  Google Scholar 

  18. Suters WH, Abu-Khzam FN, Zhang Y, Symons CT, Samatova NF, Langston MA (2005) A new approach and faster exact methods for the maximum common subgraph problem. In: Computing and combinatorics. Springer, Berlin, Heidelberg, pp 717–727 (2005)

  19. Vijayalakshmi R, Nadarajan R, Nirmala P, Thilaga M (2011) Performance monitoring of large communication networks using maximum common subgraphs. Int J Artif Intell 6(S11):72–86

    Google Scholar 

  20. Vijayalakshmi R, Nadarajan R, Nirmala P, Thilaga M (2011) A divisive clustering algorithm for performance monitoring of large networks using maximum common subgraphs. Int J Artif Intell 7(A11):92–109

    Google Scholar 

  21. Yang Y, Yu JX, Gao H, Pei J, Li J (2014) Mining most frequently changing component in evolving graphs. World Wide Web 17(3):351–376

    Article  Google Scholar 

  22. Zhai Y, Liu B (2006) Structured data extraction from the web based on partial tree alignment. IEEE Trans Knowl Data Eng 18(12):1614–1628

    Article  Google Scholar 

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Correspondence to Parisutham Nirmala.

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Nirmala, P., Ramasubramony Sulochana, L. & Rethnasamy, N. Centrality measures-based algorithm to visualize a maximal common induced subgraph in large communication networks. Knowl Inf Syst 46, 213–239 (2016). https://doi.org/10.1007/s10115-015-0844-5

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  • DOI: https://doi.org/10.1007/s10115-015-0844-5

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