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Abstract
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One of the popular categories of community detection methods are label propagation-base 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 paper presents a fast and simple label diffusion method, called 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 two low degree have 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.
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