Abstract
The investigation of community structures in networks is an important issue in many domains and disciplines. Several types of algorithms exist for revealing the community structure in networks. However, Most of these algorithms consider only structure of the network, ignoring some additional conditions such as direction, weight, semantic, etc. In this paper we consider the behaviors of each individuals and describe an ant colony clustering algorithm for automatically identifying social communities from email network. This algorithm is successfully tested and evaluated on the Enron email dataset of 517,431 emails from 151 users, and shows that the method is effective at identifying true communities, both formal and informal.
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
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks, Physical Review E, 70:066111 (2004)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Physical Review E, 69:026113 (2004)
Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., Parisi, D.: Defining and identifying communities in networks. In: Proceedings of National Academy of Science in USA, vol. 101, pp. 2658–2663 (2004)
Newman, M.E.J.: Fast algorithm for detecting community structure in networks, Physical Review E, 69:066133 (2004)
Duch, J., Arenas, A.: Community detection in complex networks using extremal optimization, Pre-print condmat/0501368 (2005)
Deneubourg, J.-L., Goss, S., Franks, N., Sendova- Franks, A., Detrain, C., Chretien, L.: The Dynamic of Collective Sorting Robot-like Ants and Ant-like Robots. In: Meyer, J.A., Wilson, S.W. (eds.) SAB 1990 - 1st Conf. On Simulation of Adaptive Behavior: From Animals to Animats, pp. 356–365. MIT Press, Cambridge (1991)
Wu, B., Shi, Z.: A clustering algorithm based on swarm intelligence[A]. In: Proceedings IEEE international conferences on info-tech and info-net proceeding[C]. Beijing, pp. 58–66 (2001)
Enron Email Dataset (2005), http://www.cs.cmu.edu/~enron
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, Y., Wang, Q., Wang, Q., Yao, Q., Liu, Y. (2007). Email Community Detection Using Artificial Ant Colony Clustering. In: Chang, K.CC., et al. Advances in Web and Network Technologies, and Information Management. APWeb WAIM 2007 2007. Lecture Notes in Computer Science, vol 4537. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72909-9_33
Download citation
DOI: https://doi.org/10.1007/978-3-540-72909-9_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72908-2
Online ISBN: 978-3-540-72909-9
eBook Packages: Computer ScienceComputer Science (R0)