Abstract

Community detection in complex networks has become an important step in understanding the structure and behaviour of networks in many fields. However, both standard node clustering and the relatively new link clustering methods have problems that make it hard to find combined clusters. Networks have been used to depict many types of real-world systems, such as those involving the transmission of information, the movement of funds, and biological processes. Communities are key structures for comprehending the structure of actual networks. The purpose of community detection is to identify meaningful subsets of these networks. Mesoscopically, a community consists of highly interconnected nodes within each subcommunity yet less strong connections across subcommunities. Communities can share a node or numerous nodes with overlapping. Evaluating the performance of a community detection method is crucial. Grouping the network's nodes into a family of subsets called clusters such that each cluster comprises similar nodes concerning the overall network structure is the problem of detecting overlapping communities in a network. Meanwhile, it has been shown that many methods for finding cluster centers have inherent flaws. Most methods are vulnerable to initial seeding and built-in variables, while others fail to highlight substantial overlaps. This article proposes the Structural Centrality Approach for Local Overlapping Community Detection (SCA-LOCD). It provides a novel approach to regional development that emphasizes the role of systems in identifying cluster centers. The fundamental concept behind this strategy is to identify structural centers in societies with coherent structures and then increase these centers using weighted methods and search engine techniques. Experimental results on synthetic and network systems show that the suggested technique is efficient and fascinating for detecting overlapped communities. It shows the success of regional extension strategies in identifying coherent groups and producing reliable classification results.