Implementation of "Detecting the Evolving Community Structure in Dynamic Social Networks".
If you find this method helpful for your research, please cite this paper.
@Article{Liu2019,
author = {Fanzhen Liu and Jia Wu and Shan Xue and Chuan Zhou and Jian Yang and Quanzheng Sheng},
journal = {World Wide Web},
title = {Detecting the Evolving Community Structure in Dynamic Social Networks},
year = {2020},
volume = {23},
pages = {715-733},
doi = {10.1007/s11280-019-00710-z}
}
Requirement
- Matlab >= 2013a
Datasets
Datasets used in this paper can be obtain from the original sources.
| Dataset | Source | |
|---|---|---|
| Synthetic datasets | SYN-FIX | M.-S. Kim and J. Han, “A particle-and-density based evolutionary clustering method for dynamic networks,” Proc. VLDB Endow., vol. 2, no. 1, pp. 622–633, 2009. |
| SYN-VAR | ||
| SYN-EVENT | D. Greene, D. Doyle, and P. Cunningham, “Tracking the evolution of communities in dynamic social networks,” in Proc. Int. Conf. Adv. Soc. Netw. Anal. Min. (ASONAM), pp. 176–183, 2010. | |
| Real-world datasets | Cellphone Calls | http://www.cs.umd.edu/hcil/VASTchallenge08/ |
| Enron Mail | http://www.cs.cmu.edu/~enron/ | |
How to use
Before run the run_DECS.m, please choose a network to load and properly set corresponding parameters in the run_DECS.m. Please see the content in run_DECS.m for more information.
The W_Cube.mat records the adjacent matrices of a dynamic network in several time steps, the GT_Cube.mat or GT_Matrix.mat records the ground truth community structures of a synthetic network in all time steps. For Real-world networks without ground truth community structures, the firststep_DYNMOGA_cell.mat and firststep_DYNMOGA_enron.mat record the community structures detected by the first step of DYNMOGA (Folino and Pizzuti 2014) in all timesteps as the ground truth community structures.
Finally, DECS_Result records the community structures detected by DECS in all time steps; DynMod and DynNmi record the modularity of the detected community structure at each time step and the NMI that measures the similarity between a detected community structure and the ground truth at each time step, respectively.
Disclaimer
If you find any bugs, please report them to me.