Journal of Software Engineering and Applications, 2014, 7, 872-882 Published Online September 2014 in SciRes. http://www.scirp.org/journal/jsea http://dx.doi.org/10.4236/jsea.2014.710078 How to cite this paper: Aston, N., Hertzler, J. and Hu, W. (2014) Overlapping Community Detection in Dynamic Networks. Journal of Software Engineering and Applications, 7, 872-882. http://dx.doi.org/10.4236/jsea.2014.710078 Overlapping Community Detection in Dynamic Networks Nathan Aston, Jacob Hertzler, Wei Hu * Department of Computer Science, Houghton College, Houghton, USA Email: * wei.hu@houghton.edu Received 11 July 2014; revised 8 August 2014; accepted 2 September 2014 Copyright © 2014 by authors and Scientific Research Publishing Inc. This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/ Abstract Due to the increasingly large size and changing nature of social networks, algorithms for dynamic networks have become an important part of modern day community detection. In this paper, we use a well-known static community detection algorithm and modify it to discover communities in dynamic networks. We have developed a dynamic community detection algorithm based on Spea- ker-Listener Label Propagation Algorithm (SLPA) called SLPA Dynamic (SLPAD). This algorithm, tested on two real dynamic networks, cuts down on the time that it would take SLPA to run, as well as produces similar, and in some cases better, communities. We compared SLPAD to SLPA, Label- RankT, and another algorithm we developed, Dynamic Structural Clustering Algorithm for Net- works Overlapping (DSCAN-O), to further test its validity and ability to detect overlapping com- munities when compared to other community detection algorithms. SLPAD proves to be faster than all of these algorithms, as well as produces communities with just as high modularity for each network. Keywords Community Detection, Modularity, Dynamic Networks, Overlapping Community Detection, Label Propagation 1. Introduction In the current world we live, interactions between individuals have become far easier than before due to online media. Massive social networks, such as Facebook, Twitter, and LinkedIn, have allowed individuals to connect with one another on a vast level. These interactions can be modelled with networks by turning the individuals on the network into nodes, and showing their communication between other individuals as edges between nodes, as * Corresponding author.