Community detection in error-prone environments based on particle cooperation and competition with distance dynamics

Community detection has attracted a lot of attention in recent decades for understanding structures and functions of complex networks. A plethora of exhaustive studies have proved that community detection methods based only on topology information tend to obtain poor community partition results. Several methods that utilize prior information to improve performance are proposed. However, most of the previous work ignores the influence of the noise from prior information. Prior information can be uncertain, imprecise, or even noisy. The reliability of prior information is a crucial factor, as wrong prior information may propagate throughout the whole network, reducing community detection effectiveness. In this paper, by combining particle cooperation and competition with distance dynamics, we propose a novel algorithm for community detection in error-prone environments (PCCDD), which helps to make full use of prior information. Finally, we conduct extensive experiments on artificial and real-world networks compared with state-of-the-art algorithms. Experimental results show that the PCCDD algorithm improves the accuracy of community detection and has good robustness in error-prone environments for detecting and preventing error propagation. Moreover, the algorithm can also be applied well to large-scale networks with unbalanced community structures due to linear time complexity.