Data for: FIFA: Finding Important Features Automatically for predicting community evolution in dynamic social networks
Description
Time segmentation DBLP dataset is divided into eleven time windows (time span 01/01/2003 to 31/12/2013). Facebook Wall Posts dataset is divided into eight time windows (time span 01/01/2005 to 31/12/2008). Wiki-Talk dataset is segmented into six time windows (time span 24/11/2007 to 31/12/2007). Enron email dataset is segmented into twelve time windows (time span 01/01/2001 to 31/12/2001). Reddit-reply dataset is segmented into six time windows (time span 07/01/2014 to 13/01/2014). Stack Overflow dataset is segmented into six time windows (time span 24/01/2016 to 29/02/2016). Social group discovery Communities of each time window are discovered using Infomap, Label Propagation, and Leiden algorithms. For running the community detection algorithms, we assume that the datasets are undirected and unweighted graphs. The communities whose size was smaller than two members were ignored. Community evolution tracking and chain identification In order to track community evolution, we investigate each community to find its similar community or communities from previous time windows, which is called community matching. We employed ICEM (Identification of Community Evolution by Mapping) (Kadkhoda Mohammadmosaferi & Naderi, 2020) method in order to determine the evolution events because it is a highly efficient approach to track community evolution and considers partial evolution and non-consecutive time windows. ICEM has two parameters which are α and β, in this paper, the thresholds for being partially similar and very similar are set to α=10% and β=90%, respectively. Each uploaded dataset contains chains of evolution for a community detection algorithm. Reference: Kadkhoda Mohammadmosaferi, K., Naderi, H., 2020. Evolution of communities in dynamic social networks: An efficient map-based approach. Expert Syst. Appl. 147, 113221.