A fast algorithm for predicting links to nodes of interest

Abstract

The problem of link prediction has recently attracted considerable attention in various domains, such as sociology, anthropology, information science, and computer science. In many real world applications, we must predict similarity scores only between pairs of vertices in which users are interested, rather than predicting the scores of all pairs of vertices in the network. In this paper, we propose a fast similarity-based method to predict links related to nodes of interest. In the method, we first construct a sub-graph centered at the node of interest. By choosing the proper size for such a sub-graph, we can restrict the error of the estimated similarities within a given threshold. Because the similarity score is computed within a small sub-graph, the algorithm can greatly reduce computation time. The method is also extended to predict potential links in the whole network to achieve high process speed and accuracy. Experimental results on real networks demonstrate that our algorithm can obtain high accuracy results in less time than other methods can.

A Link Prediction Algorithm Based on GAN

“…At present, the traditional link prediction algorithms are mainly based on likelihood analysis and similarity-based analysis methods [7].…”

“…(1) Link prediction method based on similarity of vertex attributes. [7]. Most of these link prediction methods are used in complex networks composed with labeled vertices, such as social networks.…”

“…A greater similarity between the attributes of two vertices indicates a higher probability of connection between them. (2) Link prediction method based on network structure similarity [7]. Most of these link prediction methods are used in complex networks where it is difficult to obtain vertex attribute information.…”

Mobile link prediction: Automated creation and crowdsourced validation of knowledge graphs

“…Similarity-based methods is the most commonly used approach in link prediction [41]. In this approach, a score is assigned to new candidate links, and the top-k links with the highest score are recommended [42].…”

A Weighted path based Link Prediction in Social Networks using Bounded Length of Separation between Nodes

“…At each time we randomly choose a non existing link and a missing link to compare their scores. We perform n independent comparisons and if there are n' times missing edges having higher score and n'' times both have the same scores then AUC score is calculated as ' 0.5 '' nn AUC n   Precision: Given the ranking of all the non existing edges by the algorithm, the precision is defined as the ratio of m right links taken from the top L predicted links precision [21] is calculated as m precision L  Data Sets: In this paper we have considered real world datasets [22] like Network of US political Blogs (PB), US airport network (USAir), electrical power grid of the western US (power grid). The following table summarizes basic topological features of the networks.…”