Link Prediction Methods and Their Accuracy for Different Social Networks and Network Metrics

Abstract

Currently, we are experiencing a rapid growth of the number of social-based online systems. The availability of the vast amounts of data gathered in those systems brings new challenges that we face when trying to analyse it. One of the intensively researched topics is theprediction of social connections between users. Although a lot of effort has been made to develop new prediction approaches, the existing methods are not comprehensively analysed. In this paper we investigate the correlation between network metrics and accuracy of different prediction methods. We selected six time-stamped real-world social networks and ten most widely used link prediction methods. The results of the experiments show that the performance of some methods has a strong correlation with certain network metrics. We managed to distinguish “prediction friendly” networks, for which most of the prediction methods give good performance, as well as “prediction unfriendly” networks, for which most of the methods result in high prediction error. Correlation analysis between network metrics and prediction accuracy of prediction methods may form the basis of a metalearning system where based on network characteristics it will be able to recommend the right prediction method for a given network.

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“…• Jaccard: This was introduced to compare similarity and diversity in sample sets. It is calculated as the ratio of common neighbors of node x and node y to all the neighbors of nodes x and y [10].…”

Two-Phase Preference Disclosure in Attributed Social Networks

“…We use the area under the ROC curve (AUC) as dened in [4] to measure the accuracy of the infered links. The amount that AUC exceeds 0.5 tells how much the inference algorithm is better than random guessing.…”

Link Prediction in Complex Networks using Embedding Techniques and Similarity Measures

“…We also use other features along with embeddings to improve the accuracy of our model [9]. The other similarity measures used are : First Set of Features Jaccard Index: It is the ratio of common neighbors between two nodes to the total number of neighbors of the node [10].…”

“…Adamic Adar: It is defined as the addition of the inverse logarithmic degree centrality of the common neighbors of the two nodes [10].…”