Paper Contents
Abstract
Link prediction is a fundamental task in network analysis, aiming to infer missing or future connections between nodes. Numerous link prediction techniques have been proposed, each offering unique insights and methodologies. In this review paper, we comprehensively compare and evaluate five popular link prediction techniques: Common Neighbors, Adamic Adar, Jaccard Index, Preferential Attachment, and Resource Allocation. Through a comprehensive comparison, we discuss the advantages and limitations of each technique, shedding light on their suitability for different network scenarios. We examine their performance in real-world applications, such as social networks, biological networks, and recommendation systems. Moreover, we discuss the potential for future research and improvements in link prediction algorithms. By providing a comprehensive review of these link prediction techniques, we aim to assist researchers, practitioners, and enthusiasts in selecting the most appropriate method for their specific network analysis tasks. Understanding the nuances and trade-offs of these techniques is crucial for advancing link prediction in complex networks.
Copyright
Copyright © 2023 Nirmaljit Singh. This is an open access article distributed under the Creative Commons Attribution License.
