Background: One of the great challenges in social network analysis is community detection.
Community is a group of users which have high intra connections and sparse inter connections.
Community detection or clustering reveals community structure of social networks and
hidden relationships among their constituents. Nowadays, many different methods are proposed
to detect community structures in social networks from different perspectives, but none of them
can be a constant winner. Therefore, an ensemble of different methods can potentially improve
the final result.
Methods: In this paper, we present a framework for different methods to be combined for community
detection. This method is a combination of genetic algorithms, particle swarm optimization, kmeans
clustering and Louvain clustering algorithms. Our method uses topological and demographic
information to identify communities and can automatically determine the number of optimal communities.
Results and Conclusion: Quantitative evaluations based on extensive experiments on Ego-
Facebook social network dataset reveals that the method presented in this study achieves favorable
results, which are quite superior to other relevant algorithms in the literature.
• Discovering relationships between individuals by analyzing social networks.
• Providing identifying communities algorithms based on different clustering methods.
• An ensemble of community detection consisting of GA, PSO, k-means and Louvain clustering.
• The proposed method is better than the TSA method at silhouette and modularity criterion.
Demographic information also relates to the profile of users and their shared tweets.