Affiliations: [a] School of Computer and Artificial Intelligence, Zhengzhou University, 450001, China
| [b] Prince Sattam Bin Abdulaziz University, Al-Kharj, Saudi Arabia
Correspondence:
[*]
Corresponding author. Zhengzheng Lou, School of Computer and Artificial Intelligence, Zhengzhou University, 450001, China. E-mail: [email protected].
Abstract: In a real-world, networked system, the ability to detect communities or clusters has piqued the concern of researchers in a wide range of fields. Many existing methods are simply meant to detect the membership of communities, not the structures of those groups, which is a limitation. We contend that community structures at the local level can also provide valuable insight into their detection. In this study, we developed a simple yet prosperous way of uncovering communities and their cores at the same time while keeping things simple. Essentially, the concept is founded on the theory that the structure of a community may be thought of as a high-density node surrounded by neighbors of minor densities and that community centers are located at a significant distance from one another. We propose a concept termed “community centrality” based on finding motifs to measure the probability of a node becoming the community center in a setting like this and then disseminate multiple, substantial center probabilities all over the network through a node closeness score mechanism. The experimental results show that the proposed method is more efficient than many other already used methods.
Keywords: Community detection, node density, node closeness, motifs, community center
