World Scientific
  • Search
    Quick Search in Journals
    Access type:
    Quick Search anywhere
    Access type:
    Advanced Search
  • My Cart
  •   
Skip main navigation

Cookies Notification

We use cookies on this site to enhance your user experience. By continuing to browse the site, you consent to the use of our cookies. Learn More
×
Regular Research ArticlesNo Access

MULTILAYER STOCHASTIC BLOCK MODELS FOR COMMUNITY DETECTION IN HETEROGENEOUS NETWORKS

    https://doi.org/10.1142/S0219525925500031Cited by:0 (Source: Crossref)
    Previous
    Next

    Abstract

    Heterogeneous networks have multiple types of nodes and edges. Single-layer stochastic block model (SBM), bipartite SBM, and multiplex SBM have been proposed as a tool for detecting community structure in networks and generating synthetic networks for use as benchmarks. Yet, any SBM has not been introduced specifically for detecting community in heterogeneous networks. In this paper, we introduce heterogeneous multilayer SBMs for detecting communities in heterogeneous networks. According to these models, we look at heterogeneous networks as multilayer networks, which means each edge type shows one layer. We can categorize these models into two broad groups, those based on the independent degree principle and other based on the shared degree principle. According to our results, in general, the independent degree model has better performance in networks that have less common communities between nodes types. In contrast, the shared degree model has better performance in networks which have more common communities between nodes types. Also, we show that our models outperform in real-world networks. If we put aside the exception case, simulation results and real data applications show the effectiveness of these proposed models compared to single-layer models that are applied to heterogeneous networks.

    References

    • [1] Danonand, L., Diaz-Guilera, A., Duch, J. and Arenas, A., Comparing community structure identification, J. Stat. Mech. Theory Exp. 2005 (2005) 219–228. Google Scholar
    • [2] Decelle, A., Krzakala, F., Moore, C. and Zdeborova, L., Asymptotic analysis of the stochastic block model for modular networks and its algorithmic applications, Phys. Rev. E 84 (2011) 066106. Crossref, Web of Science, Google Scholar
    • [3] Gao, J., Liang, F., Fan, W., Sun, Y. and Han, J., Graph-based consensus maximization among multiple supervised and unsupervised models, NIPS’09: Proc. 22nd Int. Conf. Neural Information Processing Systems (Vancouver, BC, Canada, 2009), pp. 585–593. Google Scholar
    • [4] Guimera, R., Llorente, A., Moro, E. and Sales-Pardo, M., Predicting human preferences using the block structure of complex social networks, PLoS One 7(9) (2012) e44620. Crossref, Web of Science, Google Scholar
    • [5] Han, Q., Xu, K. and Airoldi, E., Consistent estimation of dynamic and multilayer block models, ICML’15: Proc. 32nd Int. Conf. International Conf. Machine Learning, Lille, France, Vol. 37, pp. 1511–1520. Google Scholar
    • [6] Holland, P., Laskey, K. B. and Leinhardt, S., Stochastic blockmodels: First steps, Soc. Netw. 5 (1983) 109–137. Crossref, Web of Science, Google Scholar
    • [7] Hu, B., Fang, Y. and Shi, C. Adversarial learning on heterogeneous information networks, KDD’19: Proc. 25th ACM SIGKDD Int. Conf. Knowledge Discovery & Data Mining, Alaska, USA, pp. 120–129. Google Scholar
    • [8] Karrer, B. and Newman, M. E. J., Stochastic block models and community structure in networks, Phys. Rev. E 83 (2011) 016107. Crossref, Web of Science, Google Scholar
    • [9] Kernighan, B. W. and Lin, S., An efficient heuristic procedure for partitioning graphs, Bell Syst. Tech. J. 49 (1970) 291–307. Crossref, Google Scholar
    • [10] Larremore, D. B., Clauset, A. and Jacobs, A. Z., Efficiently inferring community structure in bipartite networks, Phys. Rev. E 90 (2014) 012805. Crossref, Web of Science, Google Scholar
    • [11] Liu, X., Liu, W., Murata, T. and Wakita, K., A framework for community detection in heterogeneous multi-relational networks, Adv. Complex Syst. 17 (2014) 20–30. Link, Web of Science, Google Scholar
    • [12] Nowicki, K. and Snijders, T. A. B., Estimation and prediction for stochastic blockstructures, J. Am. Stat. Assoc. 96 (2001) 1077–1087. Crossref, Web of Science, Google Scholar
    • [13] Paul, S. and Chen, Y., Community detection in multi-relational data with restricted multi-layer stochastic blockmodel, Electron. J. Statist. 10 (2015) 3807–3870. Google Scholar
    • [14] Paul, S. and Chen, Y., Null models and community detection in multi-layer networks, Sankhya A 84 (2022) 163–217. Crossref, Google Scholar
    • [15] Peixoto, T. P., Parsimonious module inference in large networks, Phys. Rev. Lett. 110 (2013) 148701. Crossref, Web of Science, Google Scholar
    • [16] Peixoto, T. P., Hierarchical block structures and high-resolution model selection in large networks, Phys. Rev. X 4 (2014) 011047. Web of Science, Google Scholar
    • [17] Peixoto, T. P., Inferring the mesoscale structure of layered, edge-valued, and time-varying networks, Phys. Rev. E 92 (2015) 042807. Crossref, Web of Science, Google Scholar
    • [18] Pramanik, S., Tackx, R., Navelkar, A., Guillaume, J. L. and Mitra, B., Discovering community structure in multilayer networks, 2017 IEEE Int. Conf. Data Sci. Advanced Analytics (DSAA), Tokyo, Japan, pp. 611–620. Google Scholar
    • [19] Shen, H., Cheng, X. and Guo, J., A dimensionality reduction framework for detection of multiscale structure in heterogeneous networks, J. Comput. Sci. Technol. 27 (2012) 341–357. Crossref, Web of Science, Google Scholar
    • [20] Song, J., Tang, S., Liu, X., Gao, Y., Yang, H. and Lu, P., A modularity-based method reveals mixed modules from chemical-gene heterogeneous network, PLoS One 10(4) (2015) e0125585. Crossref, Web of Science, Google Scholar
    • [21] Stanley, N., Shai, S., Taylor, D. and Mucha, P. J., Clustering network layers with the strata multilayer stochastic block model, IEEE Trans. Netw. Sci. Eng. 3 (2015) 95–105. Crossref, Google Scholar
    • [22] Taylor, D., Shai, S., Stanley, N. and Mucha, P. J., Enhanced detectability of community structure in multilayer networks through layer aggregation, Phys. Rev. Lett. 116 (2016) 228–301. Crossref, Web of Science, Google Scholar
    • [23] Valles-Catala, T., Massucci, F., Guimera, R. and Sales-Pardo, M., Multilayer stochastic block models reveal the multilayer structure of complex networks, Phys. Rev. X 6 (2016) 011036. Crossref, Web of Science, Google Scholar
    • [24] Wang, Y. J. and Wong, G. Y., Stochastic blockmodels for directed graphs, J. Am. Stat. Assoc. 82 (1987) 8–19. Crossref, Web of Science, Google Scholar
    • [25] Zhang, J. and Chen, Y., Modularity based community detection in heterogeneous networks, Stat. Sin. 30 (2018) 601–629. Google Scholar