A novel three-phase expansion algorithm for uncovering communities in social networks using local influence and similarity in embedding space

Community detection can help uncover and understand complex networks' underlying patterns and structures. It involves identifying cohesive groups with similar entities while being separated from other groups. Social networks are a prime example of an area where community detection is particularly relevant, as it can provide insight into the behaviors of individuals. Several community detection methods have been proposed, each addressing the problem from different perspectives. However, the rise of vast and intricate networks from diverse domains has necessitated the development of community detection methods that can effectively handle large-scale graphs. This study introduces a novel three-phase expansion algorithm for community discovery based on nodes' local information and similarity in embedding space. The proposed model consists of three steps. In the first stage, we generate an embedding space in which nodes are represented as vectors, and we extract nodes that greatly influence others and have an extraordinary ability to create communities using degree centrality measures. Then, based on cosine similarity in the embedding space, we group the most similar nodes to the influential ones in the same community and create an initial community structure. In the last phase, we extract the weak communities from the initial community structure generated in the second phase and merge them with the strong ones. We conduct extensive experiments on both real-world and synthetic networks to demonstrate the effectiveness of our proposal. The experimental results show that the proposed algorithm performs better than other widely used algorithms and is highly reliable and efficient in large-scale graphs.