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In this work, we propose an extended local naïve Bayes (ELNB) model to implement link prediction in complex networks. In the model, an extended clustering coefficient (ECC) is defined to represent the posterior connection probability in ELNB model, which is composed of the link clustering coefficient (LCC) and the node clustering coefficient (NCC). Our method not only overcomes the shortcoming of the common neighbor similarity index—each common neighbor of two nodes contributes equally to the connection likelihood—but also guarantees that the contributions of a common neighbor on the connection likelihood of different pair nodes in its neighbor set are different. Through the in-depth analysis, we find that the LCC plays a positive role on link prediction. On the contrary, the NCC often yields a negative effect. Therefore, we can find the optimal parameter in ELNB yielding the best link prediction. More …