Abstract
Dynamic complex social network is always mixed with noisy data. It is important to discover and model community structure for understanding real social network and predicting its evolution. In this paper, we propose a novel algorithm NDCD(Neighborhood-based Dynamic Community Detection with graph transform for 0-1 observed networks) to discover dynamic community structure in unweighted networks. It first calculates nodes’ shared neighborhood relationship in a snapshot network and deduces the weighted directed graph; then computes both historic information and current information and deduces updated weighted undirected graphs. A greedy algorithm is designed to find the community structure snapshot at each time step. One evaluation formula is proposed to measure the community similarity. Based on this evaluation, the latent communities can be found. Experiments on both synthetic and real datasets demonstrate that our algorithm not only discovers the real community structure but also eliminates the influence of noisy data for better understanding of real network structure and its evolution.
Supported by the Major State Basic Research Development Program of China (973) No. 2013CB329602, the science research foundation for the returned overseas Chinese Scholars, NO. 2010-31, International Collaborative project of Shanxi Province, NO.2011081034, US National Science Foundation (NSF) under Grant no. CNS-1016320 and CCF-0829993.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Newman, M.E.J.: Detecting community structure in networks. Eur. Phy. J. B 38 (2004)
Chakrabarti, D., Kumar, R., Tomkins, A.: Evolutionary clustering. In: ACM SIGKDD (2006)
Chi, Y., Song, X., et al.: Evolutionary spectral clustering by incorporating temporal smoothness. In: Proc. of the 13th ACM SIGKDD Conference (2007)
Lin, Y.-R., Chi, Y., Zhu, S., et al.: Facetnet: a framework for analyzing communities and their evolutions in dynamic networks. In: WWW 2008, pp. 685–694 (2008)
Chayant, T., Tanya, B.-W., David, K.: A Framework For Community Identification in Dynamic Social Networks. In: KDD 2007, pp. 717–726 (2007)
Tang, L., Liu, H., et al.: Community evolution in dynamic multi-mode networks. In: KDD (2008)
Kumar, R., Novak, J., Raghavan, P., et al.: On the bursty evolution of blogspace. In: WWW (2003)
Leskovec, J., Kleinberg, J., Faloutsos, C.: Graphs over time: densification laws, shrinking diameters and possible explanations. In: KDD (2005)
Zhao, Q., Bhowmick, S.S., et al.: Characterizing and predicting community members from evolutionary and heterogeneous networks. In: CIKM 2008, pp. 309–318 (2008)
Palla, G., Barabasi, A.-L., Vicsek, T.: Quantifying social group evolution. Nature 446 (2007)
Spiliopoulou, M., Ntoutsi, I., et al.: Monic: modeling and monitoring cluster transitions. In: SIGKDD (2006)
Asur, S., Parthasarathy, S., Ucar, D.: An event-based framework for characterizing the evolutionary behavior of interaction graphs. In: Proc.of the 13th ACM SIGKDD Conf. (2007)
Sun, J., et al.: GraphScope: parameter-free mining of large time-evolving graphs. In: KDD (2007)
Gomez-Rodriguez, M., Leskovec, J.: Structure and Dynamics of Info. Pathways in Online Media. In: WSDM (2013)
Kumar, R., Novak, J., Tomkins, A.: Structure and evolution of online social networks. In: SIGKDD (2006)
Fortunato, S.: Community detection in graphs. Phy. Reports 486, 75–174 (2010)
Wang, L.: SoFA: An expert-driven, self-organization peer-to-peer semantic communities for network resource management. Expert Systems and Applications (January 2011)
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
Wang, L., Bi, Y., Wu, W., Lian, B., Xu, W. (2013). Neighborhood-Based Dynamic Community Detection with Graph Transform for 0-1 Observed Networks. In: Du, DZ., Zhang, G. (eds) Computing and Combinatorics. COCOON 2013. Lecture Notes in Computer Science, vol 7936. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38768-5_75
Download citation
DOI: https://doi.org/10.1007/978-3-642-38768-5_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38767-8
Online ISBN: 978-3-642-38768-5
eBook Packages: Computer ScienceComputer Science (R0)