A fast community detection algorithm using a local and multi-level label diffusion method in social networks
Asgarali Bouyer, Khatereh Azad, Alireza Rouhi
Abstract
One of the popular categories of community detection methods are label propagation-based algorithms. Label propagation-based algorithms use local criteria and have a near-linear time complexity. However, these algorithms have problems such as low accuracy, instability, and high computational time in comparison with other local methods. This article presents a fast and simple label diffusion method (FSLD), using local criteria to discover communities accurately in large-scale networks. In FSLD method, community formation is initially started from a low-degree periphery node and then it diffuses its label from outer to inner side of community in a multi-level way. In next step, using a label updating step, all nodes from high-degree to low-degree have the potential to update and finalize their label to obtain initial communities. The experimental results reveal the higher accuracy and performance of the proposed FSLD algorithm in comparison to other state-of-the-art algorithms.