Performance of Local Information Based Link Prediction: A Sampling Perspective

Abstract

Link prediction is pervasively employed to uncover the missing links in the snapshots of real-world networks, which are usually obtained from kinds of sampling methods. Contrarily, in the previous literature, in order to evaluate the performance of the prediction, the known edges in the sampled snapshot are divided into the training set and the probe set randomly, without considering the diverse sampling approaches beyond. However, different sampling methods might lead to different missing links, especially for the biased ones. For this reason, random partition based evaluation of performance is no longer convincing if we take the sampling method into account. Hence, in this paper, aim at filling this void, we try to reevaluate the performance of local information based link predictions through sampling methods governed division of the training set and the probe set. It is interesting that we find for different sampling methods, each prediction approach performs unevenly. Moreover, most of these predictions perform weakly when the sampling method is biased, which indicates that the performance of these methods is overestimated in the prior works.

19 pages, 5 figures

References in corpus (8)

• Finding community structure in networks using the eigenvectors of matrices
• Link Prediction in Complex Networks: A Survey
• Hierarchical structure and the prediction of missing links in networks
• Predicting Missing Links via Local Information
• Missing and spurious interactions and the reconstruction of complex networks
• Effective and Efficient Similarity Index for Link Prediction of Complex Networks
• Link prediction in complex networks: a local na\"ıve Bayes model
• Link Prediction in Complex Networks: A Clustering Perspective