Signed Network Label Propagation Algorithm with Structural Balance Degree for Community Detection

Social networks are usually modeled as signed networks. The community detection is an important problem for the research of signed networks. The time complexity of signed label propagation algorithm is lower than most existing algorithms for community detection in the signed networks. However, bad performance on robustness and accuracy in the algorithm should not be ignored. Thus, we propose a structural balance degree to measure the balance of an edge in the local network and the local network density. Then a novel signed network label propagation algorithm with structural balance degree is proposed for community detection in signed networks. Besides, the algorithm is tested on several real-world social networks. Experimental results prove that the optimized algorithm can enhance both the robustness and the effectiveness. Its convergence rate is also faster than current algorithms.

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