Predicting links is crucial task for determining future links in complex networks across different real-world domains like information networks, social interactions, and technological networks. The link prediction method utilizes graph topological features to locate common neighborhood, yet it overlooks the importance of nodes within the network. In this context, we seek to utilize the importance of node in the network in the link prediction techniques. Centrality metrics measure a node’s relative importance within the network and demonstrates a strong correlation with future links in complex networks. In our study, we propose a novel link prediction measure called Local-Similarity based on Summation of Degree Centrality (CLP). CLP finds similarity scores for node pairs by considering common neighbors and use the centrality scores of these common neighbors in the prediction task. To assess our approach, we compare it with existing methods like Jaccard coefficient, Preferential Attachment, and a recent measure like Keyword Network Link Prediction based on degree centrality. We conduct experiments on four real-world datasets, and CLP shows significant improvements. On average, there’s a 15% improvement in Area Under the Receiver Operating Characteristic (AUROC) compared to existing methods and a 27% improvement over the recent one. Additionally, there’s an average 20 and 23% enhancement in Area Under Precision Recall (AUPR) compared to existing and recent methods. Our experiments highlight the superior performance of the proposed CLP method.