Abstract
In this paper, we give a new definition of community which is composed of two parts: community core and the periphery. Community core consists of highly densely connected nodes. And we propose LGSM (Local Greedy Search Method) for discovering community structures in social networks. LGSM sorts node according to weighted degree. For each node, LGSM derives a maximal weighted clique as a seed cluster. Then, LGSM adds new nodes into the seed cluster until the weighted edge density is smaller than the threshold value. After all community cores are detected, LGSM allots isolated nodes to the detected cores, and optimizes the community structure based on modularity. Our method is an integrative method, which is applicable not only to discovering overlapping communities, but also to discovering non-overlapping community. Experiments illustrate that LGSM can achieve good community structure on synthetic and real-world networks and the time complexity is O(|E|lg(|V|)).
This research is supported by NSFC Projects (61070011 and 61272275).
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
References
Newman, M.: Communities, modules and large-scale structure in networks. Nature Physics 8, 25–31 (2012)
Newman, M.: Modularity and community structure in networks. PNAS 103(23), 8577–8582 (2006)
Girvan, M., Newman, M.: Community structure in social and biological networks. PNAS 99(12), 7821–7826 (2002)
Newman, M.: Fast algorithm for detecting community structure in networks. Phys. Rev. E 69, 066133 (2004)
Lancichinetti, A., Fortunato, S.: Community detection algorithms: A comparative analysis. Phys. Rev. E 80(5), 056117 (2009)
Xie, J., Kelley, S., Szymanski, B.K.: Overlapping Community Detection in Networks: The State of the Art and Comparative Study. ACM Computing Surveys 4 (2013)
Fortunato, S.: Community detection in graphs. arXiv:0906.0612 (2009)
Shen, H., Cheng, X., Cai, K., Hu, M.-B.: Detect overlapping and hierarchical community structure. Physica A 388, 1706 (2009)
Chen, W., Liu, Z., Sun, X., Wang, Y.: A game-theoretic framework to identify overlapping communities in social networks. Data Min. Knowl. Discov. 21, 224–240 (2010)
Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature 435, 814–818 (2005)
Jianbin, H., Heli, S., Jiawei, H., Hongbo, D., Yizhou, S., Yaguang, L.: SHRINK: A Structural Clustering Algorithm for Detecting Hierarchical Communities in Networks. In: CIKM, pp. 219–228 (2009)
Clauset, A., Newman, M., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70, 066111 (2004)
Medus, A., Acuna, G., Dorso, C.O.: Detection of community structures in networks via global optimization. Physical A 358, 593–604 (2005)
Duch, J., Alex Arenas, A.: Community detection in complex networks using extremal optimization. Phys. Rev. E 72, 027104 (2005)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, B., Qian, T. (2013). A Local Greedy Search Method for Detecting Community Structure in Weighted Social Networks. In: Motoda, H., Wu, Z., Cao, L., Zaiane, O., Yao, M., Wang, W. (eds) Advanced Data Mining and Applications. ADMA 2013. Lecture Notes in Computer Science(), vol 8346. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-53914-5_31
Download citation
DOI: https://doi.org/10.1007/978-3-642-53914-5_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-53913-8
Online ISBN: 978-3-642-53914-5
eBook Packages: Computer ScienceComputer Science (R0)