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An important fact in studying the link prediction is that the structuralproperties of networks have significant impacts on the performance ofalgorithms. Therefore, how to improve the performance of link prediction withthe aid of structural properties of networks is an essential problem. Byanalyzing many real networks, we find a common structure property: nodes arepreferentially linked to the nodes with the weak clique structure (abbreviatedas PWCS to simplify descriptions). Based on this PWCS phenomenon, we propose alocal friend recommendation (FR) index to facilitate link prediction. Ourexperiments show that the performance of FR index is generally better than somefamous local similarity indices, such as Common Neighbor (CN) index,Adamic-Adar (AA) index and Resource Allocation (RA) index. We then explain whyPWCS can give rise to the better performance of FR index in link prediction.Finally, a mixed friend recommendation index (labelled MFR) is proposed byutilizing the PWCS phenomenon, which further improves the accuracy of linkprediction.

Playing the role of weak clique property in link prediction: A friend recommendation model