A Method for Improving the Accuracy of Link Prediction Algorithms. (24th May 2021)

• Record Type: Journal Article
• Authors: Li, Jie; Peng, Xiyang; Wang, Jian; Zhao, Na
• Other Names: Chakraborti Anirban Academic Editor.
• Is Part Of: Complexity. Volume 2021(2021)
• Publication Date: 2021-05-24
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2021/8889441
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : Link prediction is a key tool for studying the structure and evolution mechanism of complex networks. Recommending new friend relationships through accurate link prediction is one of the important factors in the evolution, development, and popularization of social networks. At present, scholars have proposed many link prediction algorithms based on the similarity of local information and random walks. These algorithms help identify actual missing and false links in various networks. However, the prediction results significantly differ in networks with various structures, and the prediction accuracy is low. This study proposes a method for improving the accuracy of link prediction. Before link prediction, k -shell decomposition method is used to layer the network, and the nodes that are in 1-shell and the nodes that are not linked to the high-shell in the 2-shell are deleted. The experiments on four real network datasets verify the effectiveness of the proposed method.