Abstract
Community detection in networks is an important problem with applications in various scientific fields. Most state-of-the-art methods rely on the assumption that the algorithm will be executed in a sequential manner. As the size of networks under study grows, faster methods must be developed in order to obtain results in reasonable amounts of time. In this work, we extend parallel community detection methods based on current fast state-of-the-art algorithms to exploit the sparse structure of real networks, and evaluate their effectiveness.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bae, S.H., Halperin, D., West, J., Rosvall, M., Howe, B.: Scalable flow-based community detection for large-scale network analysis. In: 2013 IEEE 13th International Conference on Data Mining Workshops, pp. 303–310. IEEE (2013). https://doi.org/10.1109/ICDMW.2013.138
Barber, M.J., Clark, J.W.: Detecting network communities by propagating labels under constraints. Phys. Rev. E 80(2) (2009). https://doi.org/10.1103/PhysRevE.80.026129
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Statistical Mech. Theor. Exper. 2008(10), P10008–12 (2008). https://doi.org/10.1088/1742-5468/2008/10/P10008
Heldens, S., Varbanescu, A.L., Prat-Pérez, A., Larriba-Pey, J.L.: Towards community detection on heterogeneous platforms. In: Euro-Par 2015: Parallel Processing Workshops, pp. 209–220. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-27308-2_18
Jin, S., Yu, P.S., Li, S., Yang, S.: A parallel community structure mining method in big social networks. Math. Prob. Eng. 2015, 1–13 (2015). https://doi.org/10.1155/2015/934301
Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E Statistical Nonlinear Soft Matter Phys. 78(4), 1–5 (2008). https://doi.org/10.1103/PhysRevE.78.046110
Le Martelot, E., Hankin, C.: Fast multi-scale detection of overlapping communities using local criteria. Computing 96(11), 1011–1027 (2014). https://doi.org/10.1007/s00607-014-0401-1
Leskovec, J., Krevl, A.: SNAP Datasets: Stanford Large Network Dataset Collection (2014)
Lu, H., Halappanavar, M., Kalyanaraman, A.: Parallel heuristics for scalable community detection. Parallel Comput. 47, 19–37 (2015). https://doi.org/10.1016/j.parco.2015.03.003
Moradi, E., Fazlali, M., Malazi, H.T.: Fast parallel community detection algorithm based on modularity. In: 2015 18th CSI International Symposium on Computer Architecture and Digital Systems (CADS). IEEE (2015). https://doi.org/10.1109/CADS.2015.7377794
Niu, F., Recht, B., Re, C., Wright, S.J.: HOGWILD!: a lock-free approach to parallelizing stochastic gradient descent. Adv. Neural Inf. Process. Syst. (1), 21 (2011)
Prat-Pérez, A., Dominguez-Sal, D., Larriba-Pey, J.L.: High quality, scalable and parallel community detection for large real graphs. In: Proceedings of the 23rd International Conference on World Wide Web - WWW 2014, pp. 225–236. ACM Press (2014). https://doi.org/10.1145/2566486.2568010
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). https://doi.org/10.1103/PhysRevE.76.036106
Staudt, C.L., Meyerhenke, H.: Engineering parallel algorithms for community detection in massive networks. IEEE Trans. Parallel Distrib. Syst. 27(1), 171–184 (2016). https://doi.org/10.1109/TPDS.2015.2390633
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Gagnon, P., Caporossi, G., Perron, S. (2018). Parallel Community Detection Methods for Sparse Complex Networks. In: Cherifi, C., Cherifi, H., Karsai, M., Musolesi, M. (eds) Complex Networks & Their Applications VI. COMPLEX NETWORKS 2017. Studies in Computational Intelligence, vol 689. Springer, Cham. https://doi.org/10.1007/978-3-319-72150-7_24
Download citation
DOI: https://doi.org/10.1007/978-3-319-72150-7_24
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-72149-1
Online ISBN: 978-3-319-72150-7
eBook Packages: EngineeringEngineering (R0)