Link Prediction in Complex Networks Using Average Centrality-Based Similarity Score

NANDINI, Y.V., LAKSHMI, T. Jaya, ENDURI, Murali Krishna and SHARMA, Hemlata (2024). Link Prediction in Complex Networks Using Average Centrality-Based Similarity Score. Entropy, 26 (6): 433.

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Link prediction plays a crucial role in identifying future connections within complex networks, facilitating the analysis of network evolution across various domains such as biological networks, social networks, recommender systems, and more. Researchers have proposed various centrality measures, such as degree, clustering coefficient, betweenness, and closeness centralities, to compute similarity scores for predicting links in these networks. These centrality measures leverage both the local and global information of nodes within the network. In this study, we present a novel approach to link prediction using similarity score by utilizing average centrality measures based on local and global centralities, namely Similarity based on Average Degree (

Item Type: Article
Uncontrolled Keywords: 01 Mathematical Sciences; 02 Physical Sciences; Fluids & Plasmas; 49 Mathematical sciences; 51 Physical sciences
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SWORD Depositor: Symplectic Elements
Depositing User: Symplectic Elements
Date Deposited: 24 May 2024 10:41
Last Modified: 31 May 2024 13:20

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