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Pacific-Asia Conference on Knowledge Discovery and Data Mining

PAKDD 2017: Advances in Knowledge Discovery and Data Mining pp 486–498Cite as

An Enhanced Markov Clustering Algorithm Based on Physarum

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10234))

Abstract

Community mining is a vital problem for complex network analysis. Markov chains based algorithms are known as its easy-to-implement and have provided promising solutions for community mining. Existing Markov clustering algorithms have been optimized from the aspects of parallelization and penalty strategy. However, the dynamic process for enlarging the inhomogeneity attracts little attention. As the key mechanism of Markov chains based algorithms, such process affects the qualities of divisions and computational cost directly. This paper proposes a hybrid algorithm based on Physarum, a kind of slime. The new algorithm enhances the dynamic process of Markov clustering algorithm by embedding the Physarum-inspired feedback system. Specifically, flows between vertexes can enhance the corresponding transition probability in Markov clustering algorithms, and vice versa. Some networks with known and unknown community structures are used to estimate the performance of our proposed algorithms. Extensive experiments show that the proposed algorithm has higher NMI, Q values and lower computational cost than that of the typical algorithms.

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Notes

  1. 1.

    http://www-personal.umich.edu/~mejn/netdata/.

  2. 2.

    http://vlado.fmf.uni-lj.si/vlado/vladonet.htm.

References

  1. Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)

    Article  MathSciNet  Google Scholar 

  2. Centola, D.: The spread of behavior in an online social network experiment. Science 329(5996), 1194–1197 (2010)

    Article  Google Scholar 

  3. Satuluri, V., Parthasarathy, S.: Scalable graph clustering using stochastic flows: applications to community discovery. In: The 15th International Conference on Knowledge Discovery and Data Mining, pp. 737–746. ACM (2009)

    Google Scholar 

  4. Niu, Q., Lai, P.W., Faisal, S.M., Parthasarathy, S., Sadayappan, P.: A fast implementation of MLR-MCL algorithm on multi-core processors. In: The 21st International Conference on High Performance Computing, pp. 1–10. IEEE (2014)

    Google Scholar 

  5. Satuluri, V., Parthasarathy, S., Ucar, D.: Markov clustering of protein interaction networks with improved balance and scalability. In: The 1st ACM International Conference on Bioinformatics and Computational Biology, BCB 2010, pp. 247–256. ACM, New York (2010)

    Google Scholar 

  6. Nakagaki, T., Yamada, H., Tóth, Á.: Intelligence: Maze-solving by an amoeboid organism. Nature 407(6803), 470 (2000)

    Article  Google Scholar 

  7. Tero, A., Takagi, S., Saigusa, T., Ito, K., Bebber, D.P., Fricker, M.D., Yumiki, K., Kobayashi, R., Nakagaki, T.: Rules for biologically inspired adaptive network design. Science 327(5964), 439–442 (2010)

    Article  MathSciNet  MATH  Google Scholar 

  8. Liu, Y., Gao, C., Zhang, Z., Wu, Y., Liang, M., Tao, L., Lu, Y.: A new multi-agent system to simulate the foraging behaviors of Physarum. Nat. Comput. 16, 15–29 (2017)

    Article  MathSciNet  Google Scholar 

  9. Tero, A., Kobayashi, R., Nakagaki, T.: A mathematical model for adaptive transport network in path finding by true slime mold. J. Theor. Biol. 244(4), 553–564 (2007)

    Article  MathSciNet  Google Scholar 

  10. Liu, Y.X., Gao, C., Zhang, Z.L., Lu, Y.X., Chen, S., Liang, M.X., Li, T.: Solving NP-hard problems with Physarum-based ant colony system. IEEE/ACM Trans. Comput. Biol. Bioinf. 14(1), 108–120 (2017)

    Article  Google Scholar 

  11. Van Dongen, S.M.: Graph clustering by flow simulation. Ph.D. thesis, Universiteit Utrecht (2001)

    Google Scholar 

  12. Karrer, B., Newman, M.E.: Stochastic blockmodels and community structure in networks. Phys. Rev. E 83(1), 016107 (2011)

    Article  MathSciNet  Google Scholar 

  13. Tabrizi, S.A., Shakery, A., Asadpour, M., Abbasi, M., Tavallaie, M.A.: Personalized pagerank clustering: a graph clustering algorithm based on random walks. Phys. A 392(22), 5772–5785 (2013)

    Article  MathSciNet  Google Scholar 

  14. Jin, D., Chen, Z., He, D., Zhang, W.: Modeling with node degree preservation can accurately find communities. In: The 24th International Conference on Artificial Intelligence, AAAI, pp. 160–167 (2015)

    Google Scholar 

  15. Sobolevsky, S., Campari, R., Belyi, A., Ratti, C.: General optimization technique for high-quality community detection in complex networks. Phys. Rev. E 90, 012811 (2014)

    Article  Google Scholar 

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Acknowledgement

Prof. Zili Zhang and Dr. Chao Gao are the corresponding authors. This work is supported by the National Natural Science Foundation of China (Nos. 61403315, 61402379), CQ CSTC (No. cstc2015gjhz40002), Fundamental Research Funds for the Central Universities (Nos. XDJK2016A008, XDJK2016B029, XDJK2016D053) and Chongqing Graduate Student Research Innovation Project (No. CYS16067).

Author information

Authors and Affiliations

  1. School of Computer and Information Science, Southwest University, Chongqing, 400715, China

    Mingxin Liang, Chao Gao, Xianghua Li & Zili Zhang

  2. Potsdam Institute for Climate Impact Research (PIK), 14473, Potsdam, Germany

    Chao Gao

  3. Institute of Physics, Humboldt-University zu Berlin, 12489, Berlin, Germany

    Chao Gao

  4. School of Information Technology, Deakin University, Geelong, VIC, 3220, Australia

    Zili Zhang

Authors
  1. Mingxin Liang
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  2. Chao Gao
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  3. Xianghua Li
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  4. Zili Zhang
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Corresponding authors

Correspondence to Chao Gao or Zili Zhang .

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

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Liang, M., Gao, C., Li, X., Zhang, Z. (2017). An Enhanced Markov Clustering Algorithm Based on Physarum . 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 10234. Springer, Cham. https://doi.org/10.1007/978-3-319-57454-7_38

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  • DOI: https://doi.org/10.1007/978-3-319-57454-7_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-57453-0

  • Online ISBN: 978-3-319-57454-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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