Detecting Communities Using Network Embedding and Graph Clustering Approach

Complex network community structure has been shown effective in many fields, including biology, social media, health, and more. Researchers have explored many different techniques for studying complex networks and discovering communities within them. Most of them, however, lack the expressiveness necessary to learn the node and edge representations of complicated networks. This study has aimed to improve the performance of the Hierarchical Clustering algorithm by analyzing a framework for learning continuous feature representations using node embedding methods for nodes in networks. The proposed method improves upon previous methods by training a map from nodes to a feature space with fewer dimensions. The proposed approach employs the Hierarchical Clustering method to accomplish community identification in the benchmark networks by determining the level of similarity between any pair of node embeddings. Substantial exploratory research on a variety of real-world social networks has shown the efficacy of the proposed approach compared to existing state-of-the-art community discovery techniques. The proposed method has provided high-accuracy results when applied to graph datasets.