2016/01/20 by Chuang Ma, Tao Zhou, Hai-Feng Zhang +1
Biochemistry, Genetics and Molecular Biology · Computer Science · Mathematics · Physics and Astronomy · #Advanced Graph Neural Networks #Artificial intelligence #Bioinformatics and Genomic Networks #Clique #Complex Network Analysis Techniques #Computer network #Computer science #Data mining #Index (typography) #Jaccard index #Link (geometry) #Machine learning #Mathematics #Pattern recognition (psychology) #Property (philosophy) #Similarity (geometry) #World Wide Web #cs.SI #physics.soc-ph
paper · pdf · doi:10.1038/srep30098
published as Scientific Reports, 6, (2016), 30098 · 7 figures, 4 tables
arxiv created 2016/01/20 · openalex publication_date 2016/07/21 · arxiv updated 2016/08/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
An important fact in studying link prediction is that the structural properties of networks have significant impacts on the performance of algorithms. Therefore, how to improve the performance of link prediction with the aid of structural properties of networks is an essential problem. By analyzing many real networks, we find a typical structural property: nodes are preferentially linked to the nodes with the weak clique structure (abbreviated as PWCS to simplify descriptions). Based on this PWCS phenomenon, we propose a local friend recommendation (FR) index to facilitate link prediction. Our experiments show that the performance of FR index is better than some famous local similarity indices, such as Common Neighbor (CN) index, Adamic-Adar (AA) index and Resource Allocation (RA) index. We then explain why PWCS can give rise to the better performance of FR index in link prediction. Finally, a mixed friend recommendation index (labelled MFR) is proposed by utilizing the PWCS phenomenon, which further improves the accuracy of link prediction.