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Geometric Multi-Way Frequent Subgraph Mining Approach to a Single Large Database

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Smart Intelligent Computing and Applications

Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 160))

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Abstract

In the present time, Graph Mining has become the most research-oriented field in the advance technologies for its importance in many areas, such as citation graphs, web data mining, chemical structures, protein interaction, social networks, etc. The rapid change in Graph Mining research work is fully dependent on the field of Graph Partitioning (GP) as well as Frequent Subgraph Mining (FSM). In this paper, we define Geometric Multi-Way Frequent Subgraph Mining (GMFSM) approach, which is based on Geometric Partition of a Single Large Graph Database with Frequent Subgraph Mining (FSM) approach that uses filtration technique to reduce number of candidate subgraphs. After partitioning the large graph database, we execute FSM algorithm simultaneously on each subparts which produce the desire result much faster (one-third to half) than existing algorithms. In addition, we use two-way partitioning algorithm recursively to obtain multi-way partition which drastically changes the performance of the algorithm.

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References

  1. Kuramochi, M., Karypis, G.: Frequent subgraph discovery. In: Proceedings of 2001 International Conference Data Mining (ICDM’01), pp. 313–320. San Jose, CA (2001)

    Google Scholar 

  2. Hollocou, A., Maudet, J., Bonald, T., Lelarge, M.: A streaming algorithm for graph clustering. NIPS 2017—Workshop on Advances in Modeling and Learning Interactions from Complex Data. Dec 2017, pp. 1–12. Long Beach, United States (2017)

    Google Scholar 

  3. Pradhan, S., Chakravarthy, S., Telang, A.: Modeling relational data as graphs for mining. In: 15th International Conference on Management of Data COMAD 2009, Mysore, India, December 9–12 (2009)

    Google Scholar 

  4. Zhang, M., Cui, Z., Neumann, M., Chen, Y.: An end-to-end deep learning architecture for graph classification. In: The Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18) (2018)

    Google Scholar 

  5. Yan, X., Yu, P.S., Han, J.: Graph indexing: a frequent structure-based approach, The ACM Digital Library is published by the Association for Computing Machinery. Copyright © 2018 ACM

    Google Scholar 

  6. Daoud, M., Tamine-Lechani, L., Boughanem, M.: Towards a graph-based user profile modelling for a session-based personalized search. Knowl. Inf. Syst. 21(3), 365–398 (2009)

    Article  Google Scholar 

  7. Hendrickson, B., Kolda, T.G.: Graph Partitioning Models for Parallel Computing. Elsevier, Parallel Comput. 26(12), 1519–1534 (2000)

    Google Scholar 

  8. Hendrickson, B., Leland, R.: A multilevel algorithm for partitioning graphs. In: Proceedings of ACM/IEEE Conference on Supercomputing, pp. 28–28 (1995)

    Google Scholar 

  9. Kernighan, B.W., Lin, S.: An efficient heuristic procedure for partitioning graphs. Bell Syst. Tech. J. 49, 291–307 (1970)

    Article  Google Scholar 

  10. Fiduccia, C.M., Mattheyses, R.M.: A linear time heuristic for improving network partitions. In: Proceedings of IEEE Design Automation Conference, pp. 175–181 (1982)

    Google Scholar 

  11. Monien, B., Preis, R., Diekmann, R.: Quality matching and local improvement for multilevel graph-partitioning. Parallel Comput. 26(12), 1609–1634 (2000)

    Article  MathSciNet  Google Scholar 

  12. Safro, I., Sanders, P., Schulz, C.: Advanced coarsening schemes for graph partitioning. In: Proceedings of International Symposium on Experimental Algorithms (SEA’12), pp. 369–380 (2012)

    Chapter  Google Scholar 

  13. Arora, S., Rao, S., Vazirani, U.: Geometry, flows, and graph-partitioning algorithms. Commun. ACM 51(10) (2008)

    Article  Google Scholar 

  14. Martella, C., Logotheti, D., Siganos, G.: Spinner: Scalable Graph Partitioning for the Cloud (2014). arXiv:1404.3861v1

  15. Tsourakakis, C.E., Gkantsidis, C., Radunovic, B., Vojnovic, M.: FENNEL: Streaming Graph Partitioning for Massive Scale Graphs. Technical Report MSR-TR-2012–113 2012

    Google Scholar 

  16. Wang, L., Xiao, Y., Shao, B., Wang, H.: How to partition a billion-node graph. In: Proceedings of IEEE 30th International Conference on Data Engineering (ICDE), pp. 568–579 (2014)

    Google Scholar 

  17. Lakshmi, K., Meyyappan, T.: Frequent subgraph mining algorithms—a survey and framework for classification. In: Proceedings of Conference on Innovations in Theoretical Computer Science (ITCS 12), pp. 189–202 (2012)

    Google Scholar 

  18. Yan, X., Han, J.: gSpan: graph-based substructure pattern mining. In: Proceeding of 2nd IEEE international Conference on Data nining (ICDM’02), pp. 72 (724)

    Google Scholar 

  19. Saeedy, M.E., Kalnis, P.: “GraMi: generalized frequent pattern mining in a single large graph”, Technical Report, Division of Mathematical and Computer Sciences and Engineering King Abdullah University of Science and Technology (KAUST) (2011)

    Google Scholar 

  20. Walshaw, C., Cross, M.: JOSTLE: parallel multilevel graph-partitioning software—an overview. In: Magoules, F. (ed.) Mesh Partitioning Techniques and Domain Decomposition Techniques, pp. 27–58. Civil-Comp Ltd., (Invited Chapter) (2007)

    Google Scholar 

  21. Andersen, R., Lang, K.J.: An algorithm for improving graph partitions. In: Proceedings of the Nineteenth Annual ACM-SIAM Symposium on Discrete Algorithms, Society for Industrial and Applied Mathematics, pp. 651–660 (2008)

    Google Scholar 

  22. Karypis, G., Kumar, V.: Analysis of multilevel graph partitioning. In: Proceedings of the 1995 ACM/IEEE Supercomputing Conference, pp. 658–677. ACM/IEEE, December 1995 (a more complete version appears at http://www-users.cs.umn.edu/ékarypis/metis/publications/main.html)

  23. Karypis, G., Kumar, V.: Parallel multilevel k-way partitioning scheme for irregular graphs. In: Supercomputing ‘96 Conference Proceedings. ACM/IEEE (1996)

    Google Scholar 

  24. Simon, Horst D.: Partitioning of unstructured problems for parallel processing. Comput. Syst. Eng. 2(2–3), 135–148 (1991)

    Article  Google Scholar 

  25. Sinclair, A., Jerrum, M.: Approximate counting, uniform generation and rapidly mixing markov chains (extended abstract). In: Graph-Theoretic Concepts in Computer Science (Staffelstein, 1987), volume 314 of Lecture Notes in Computer Science pp. 134–148. Berlin, Springer (1988)

    Chapter  Google Scholar 

  26. Orecchia, L., Schulman, L. Vazirani, U., Vishnoin, N.: On partitioning graphs via single commodity flows. In: Proceedings of the 40th Annual ACM Symposium on Theory of Computing, Victoria, British Columbia, Canada, May 17–20, pp. 461–470 (2008)

    Google Scholar 

  27. Fiedler, M., Borgelt, C.: Subgraph support in a single large graph. In: Proceedings of the Seventh IEEE International Conference on Data Mining Workshops (ICDM Workshops 2007), Omaha, NE, USA, 28–31, pp. 399–404 (2007)

    Google Scholar 

  28. Karypis, G., Kumar, V.: A fast and high quality multilevel; scheme for partitioning irregular graphs. SIAM j. Sci. Comput. Soc. Indust. Appl. Math. 20(1), pp. 359–392 (1998)

    Article  MathSciNet  Google Scholar 

  29. Jacquemont, S., Jacquenet, F., Sebban, M.: A lower bound on the sample size needed to perform a significant frequent pattern mining task. Pattern Recogn. Lett. 30(11), 960–967 (2009)

    Article  Google Scholar 

  30. Cheng, J., Yu, J.X., Ding, B., Yu, P.S., Wang, H.: Fast graph pattern matching. In: Proceedings of ICDE, pp. 913–922 (2008)

    Google Scholar 

  31. Aridhi, S., d’Orazio, L., Maddouri, M., Nguifo, E.M.: Density-based data partitioning strategy to approximate large-scale subgraph mining. Inf. Syst. 48, 213–223 (2015)

    Article  Google Scholar 

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Authors and Affiliations

  1. Computer Science and Engineering Department, Gandhi Institute of Technology and Management, Visakhapatnam, AP, India

    Sadhana Priyadarshini & Sireesha Rodda

Authors
  1. Sadhana Priyadarshini
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  2. Sireesha Rodda
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Correspondence to Sadhana Priyadarshini .

Editor information

Editors and Affiliations

  1. School of Computer Engineering, KIIT Deemed to be University, Bhubaneswar, Odisha, India

    Suresh Chandra Satapathy

  2. Department of Electronics and Communication Engineering, Shri Ramswaroop Memorial Group of Professional Colleges, Lucknow, Uttar Pradesh, India

    Vikrant Bhateja

  3. School of Computer Applications, KIIT Deemed to be University, Bhubaneswar, Odisha, India

    J. R. Mohanty

  4. School of Computer and Information Science, University of Hyderabad, Hyderabad, Telangana, India

    Siba K. Udgata

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Priyadarshini, S., Rodda, S. (2020). Geometric Multi-Way Frequent Subgraph Mining Approach to a Single Large Database. In: Satapathy, S., Bhateja, V., Mohanty, J., Udgata, S. (eds) Smart Intelligent Computing and Applications . Smart Innovation, Systems and Technologies, vol 160. Springer, Singapore. https://doi.org/10.1007/978-981-32-9690-9_23

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