Abstract
Recently, the applications of community detection have increased because of their effectiveness in identifying communities correctly. Many methods and algorithms have been introduced to bring new insights that will improve community detection in social networks. While such algorithms can find useful communities, they tend to focus on network structure and ignore node interests and interconnections. However, accurate community detection requires the consideration of both network structure and node interests. The best method to achieve this is by utilizing unsupervised models. In this article, we introduce a new approach for social network clustering, termed Interest-based Clustering, which clusters nodes in social networks based on a measure of interest similarity. It considers structure, interaction, and node interest along with nodes friends' interests. The empirical evaluation of this new approach was done using real dataset crawled from Twitter. The approach outperforms well-known community detections algorithms, SCAN, Fast Modularity, Zhao et al., in terms of modularity, connectivity, and overlapping.