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An Overlapping Community Detection Algorithm Based on Triangle Reduction Weighted for Large-Scale Complex Network

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Book cover Algorithms and Architectures for Parallel Processing (ICA3PP 2020)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 12452))

Abstract

In this digital age, dramatically developed internet makes the data of complex networks appear an explosive growth, which aggrandizes the importance of multilevel community detection used in large-scale complex networks. Nowadays, there are so many community detection algorithms that could perform well on accuracy. However, none of them has an expected function to handle the time increasing problem which is caused by the inflated network scale. Hence, we propose An Overlapping Community Detection Algorithm based on Triangle Reduction Weighted for Large-scale Complex Network (TRWLPA). It consists of two main steps: 1) Transforming the original network to a small-scale triangle reduction network. This network could not only dramatically reduce the running time of community detection, but recover the original network structure by the inverse transformation. Moreover, the scale of the triangle reduction network could be controlled by setting the iteration times. 2) Doing the multi-label propagation on the reduced networks where the weight of each node is the number of initial nodes it contains. The experiments illustrate that the TRWLPA algorithm significantly reduces the running time of community detection on Youtube, DBLP, and LiveJournal datasets. Particularly, comparing with the MOSES algorithm, it achieves 98.1% running time reduction on the Youtube dataset. Furthermore, our algorithm performs well on both modularity and Normalized Mutual Information measure (NMI).

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References

  1. Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3(Jan), 993–1022 (2003)

    MATH  Google Scholar 

  2. Chen, J., Yuan, B.: Detecting functional modules in the yeast protein-protein interaction network. Bioinform. 22(18), 2283–2290 (2006)

    Article  Google Scholar 

  3. Erétéo, G., Gandon, F., Buffa, M.: Semtagp: semantic community detection in folksonomies. In: 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology. 1, pp. 324–331. IEEE (2011)

    Google Scholar 

  4. Gregory, S.: Finding overlapping communities in networks by label propagation. New J. Phys. 12(10), 103018 (2010)

    Article  Google Scholar 

  5. Hu, W.: Finding statistically significant communities in networks with weighted label propagation (2013)

    Google Scholar 

  6. Kernighan, B.W., Lin, S.: An efficient heuristic procedure for partitioning graphs. Bell Syst. Tech. J. 49(2), 291–307 (1970)

    Article  Google Scholar 

  7. Kumpula, J.M., Kivelä, M., Kaski, K., Saramäki, J.: Sequential algorithm for fast clique percolation. Phys. Rev. E 78(2), 026109 (2008)

    Article  Google Scholar 

  8. Lee, C., Reid, F., McDaid, A., Hurley, N.: Detecting highly overlapping community structure by greedy clique expansion. arXiv preprint arXiv:1002.1827 (2010)

  9. Liu, X., Li, D., Wang, S., Tao, Z.: Effective algorithm for detecting community structure in complex networks based on GA and clustering. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007. LNCS, vol. 4488, pp. 657–664. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-72586-2_95

    Chapter  Google Scholar 

  10. McDaid, A., Hurley, N.: Detecting highly overlapping communities with model-based overlapping seed expansion. In: 2010 International Conference on Advances in Social Networks Analysis and Mining, pp. 112–119. IEEE (2010)

    Google Scholar 

  11. Newman, M.E.: Fast algorithm for detecting community structure in networks. Phys. Rev. E 69(6), 066133 (2004)

    Article  Google Scholar 

  12. Newman, M.E., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)

    Article  Google Scholar 

  13. Pizzuti, C.: GA-Net: a genetic algorithm for community detection in social networks. In: Rudolph, G., Jansen, T., Beume, N., Lucas, S., Poloni, C. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 1081–1090. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-87700-4_107

    Chapter  Google Scholar 

  14. Pizzuti, C.: A multiobjective genetic algorithm to find communities in complex networks. IEEE Trans. Evol. Comput. 16(3), 418–430 (2011)

    Article  Google Scholar 

  15. Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76(3), 036106 (2007)

    Article  Google Scholar 

  16. Rattigan, M.J., Maier, M., Jensen, D.: Graph clustering with network structure indices. In: Proceedings of the 24th international conference on Machine learning, pp. 783–790 (2007)

    Google Scholar 

  17. Wu, Z.H., Lin, Y.F., Gregory, S., Wan, H.Y., Tian, S.F.: Balanced multi-label propagation for overlapping community detection in social networks. J. Comput. Sci. Technol. 27(3), 468–479 (2012)

    Article  MathSciNet  Google Scholar 

  18. Xie, J., Chen, M., Szymanski, B.K.: Labelrankt: incremental community detection in dynamic networks via label propagation. In: Proceedings of the Workshop on Dynamic Networks Management and Mining, pp. 25–32 (2013)

    Google Scholar 

  19. Xie, J., Szymanski, B.K.: Towards linear time overlapping community detection in social networks. In: Tan, P.N., Chawla, S., Ho, C.K., Bailey, J. (eds.) PAKDD 2012. LNCS (LNAI), vol. 7302, pp. 25–36. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-30220-6_3

    Chapter  Google Scholar 

  20. Xie, J., Szymanski, B.K.: Labelrank: A stabilized label propagation algorithm for community detection in networks. In: 2013 IEEE 2nd Network Science Workshop (NSW), pp. 138–143. IEEE (2013)

    Google Scholar 

  21. Xie, J., Szymanski, B.K., Liu, X.: Slpa: uncovering overlapping communities in social networks via a speaker-listener interaction dynamic process. In: 2011 ieee 11th international conference on data mining workshops, pp. 344–349. IEEE (2011)

    Google Scholar 

  22. Yang, J., Leskovec, J.: Defining and evaluating network communities based on ground-truth. Knowl. Inf. Syst. 42(1), 181–213 (2013). https://doi.org/10.1007/s10115-013-0693-z

    Article  Google Scholar 

  23. Ying, K., Gu, X., Bo, Y., et al.: A multilevel community detection algorithm for large-scale social information networks. Chin. J. Comput. 1, 169–182 (2016)

    MathSciNet  Google Scholar 

  24. Yu, X., Yang, J., Xie, Z.Q.: A semantic overlapping community detection algorithm based on field sampling. Expert Syst. Appl. 42(1), 366–375 (2015)

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, 710049, China

    Hanning Zhang, Boqin Feng & Haiyu Wu

  2. Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Technology Research and Development, Xi’an Jiaotong University, Xi’an, 710049, China

    Hanning Zhang

  3. School of Continuing Education, Xi’an Jiaotong University, Xi’an, 710049, China

    Bo Dong

  4. National Engineering Lab for Big Data Analytics Xi’an Jiaotong University, Xi’an, 710049, China

    Bo Dong

  5. Xi’an Network Computing Data Technology Co., Ltd., Xi’an, 710049, China

    Hanning Zhang, Bo Dong, Boqin Feng & Haiyu Wu

Authors
  1. Hanning Zhang
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  2. Bo Dong
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  3. Boqin Feng
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  4. Haiyu Wu
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Corresponding author

Correspondence to Bo Dong .

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Editors and Affiliations

  1. Columbia University, New York, NY, USA

    Meikang Qiu

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Zhang, H., Dong, B., Feng, B., Wu, H. (2020). An Overlapping Community Detection Algorithm Based on Triangle Reduction Weighted for Large-Scale Complex Network. In: Qiu, M. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2020. Lecture Notes in Computer Science(), vol 12452. Springer, Cham. https://doi.org/10.1007/978-3-030-60245-1_43

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