Study on similarity indices for link prediction in opportunistic networks. (October 2018)

• Record Type: Journal Article
• Authors: Cai, Xulin; Shu, Jian; Liu, Linlan
• Is Part Of: Advances in mechanical engineering. Volume 10:Number 10(2018)
• Publication Date: 2018-10
• Subjects: Opportunistic network -- link prediction -- similarity indices -- temporal-spatial characteristics -- local information; Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1177/1687814018803190
• Languages: English
• ISSNs: 1687-8132

Abstract

Link prediction aims to estimate the existence of links between nodes, using information of network structures and node properties. According to the characteristics of node mobility, node intermittent contact, and high delay of opportunistic network, novel similarity indices are constructed based on CN, AA, and RA. The indices CN, AA, and RA do not consider the historic information of networks. Similarity indices, T_CN, T_AA, and T_RA, based on temporal characteristics are proposed. These take the historic information of network evolution into consideration. Using historic information of the evolution of opportunistic networks and 2-hop neighbor information of the nodes, similarity indices based on the temporal-spatial characteristics, O_CN, O_AA, and O_RA, are proposed. Based on the imote traces cambridge (ITC) and detected social network (DSN) datasets, the experimental results indicate that similarity indices O_CN, O_AA, and O_RA outperform CN, AA, and RA. Furthermore, index O_AA has superior performance.