Community Detection in Social Networks Considering the Depth of Relationships

The study of social networks analyzes the relationships between humans, organizations, and interactions between entities. The wide-spreading popularity of social media has attracted researchers’ attention on community detection. Communities are defined as clusters of nodes or vertices that have stronger relationships between entities inside a cluster than relationships between clusters. Community detection plays an important role in discovering the underlying structures of social networks and it displays the effects of links’ structures on people and the relationship between them. Usually, clustering algorithms are utilized to identify the communities. In this paper, we propose a new clustering algorithm for community detection that considers the depth of relationships between individuals in the community identification process. Results on two popular datasets indicate that considering the depth of relationships improves the accuracy of the clustering methods. From the compared results, the proposed algorithm outperformed the six state-of-the-art algorithms.