Published Paper Details:

Social network analysis has gained much attention in recent times. A graph can be used to depict social networks. In the analysis of social networks, every individual is denoted as a node, and the connections between them are represented as edges. Identifying communities and representing the interactions between entities and individuals in real-world network graphs is a difficult task. There are numerous established methods for locating the linked nodes that eventually result in the discovery of communities. This paper presents a novel method for community detection in social networks by integrating unnormalized spectral clustering with the k-nearest neighbors (KNN) algorithm. The approach leverages the strengths of spectral clustering for global structure analysis and KNN for local neighborhood refinement. The primary objective of the algorithm proposed in this study is to identify and eliminate any noisy nodes from the identified communities, hence enhancing the quality of the identified communities. Experimental results on synthetic and real-world datasets demonstrate the method's effectiveness in accurately identifying community structures.