Community detection method is significant to character statistics of complex network.Community detection in inhomogeneous structured network is an attractive research problem while most previous approaches attempted to divide networks into communities which are based on node or edge measurement.The label propagation algorithm (LPA) adopts semi-supervised machine learning and implemented community detection in an intelligent way with automatic convergent process of network node label iteration but it often results in low efficiency in the final convergent process.In this article

aiming to promote low efficiency and stagnant converging rate of LPA in network with overlapping communities

we propose a new method (ESLPA) for community detection using epidemic spreading model combined with network connection matrix's largest Eigenvalue as label propagation threshold.Extensive experiments in synthetic signed network and real-life large networks derived from online social media are conducted to explore optimal mechanism of the most suitable community detecting virus infection threshold.Experiments result prove that our method is more accurate and faster than several traditional modularity based community detection methods such as COPRA

GN

FastGN and CPM.