Ma Chuang, Zhou Tao, Zhang Hai-Feng
School of Mathematical Science, Anhui University, Hefei 230601, China.
Web Sciences Center, University of Electronic Science and Technology of China, Chengdu 610054, China.
Sci Rep. 2016 Jul 21;6:30098. doi: 10.1038/srep30098.
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.
研究链接预测中的一个重要事实是，网络的结构属性对算法性能有重大影响。因此，如何借助网络的结构属性提高链接预测性能是一个关键问题。通过分析许多真实网络，我们发现一种典型的结构属性：节点优先与具有弱团结构的节点相连（为简化描述，简称为PWCS）。基于这种PWCS现象，我们提出一种局部朋友推荐（FR）指标以促进链接预测。我们的实验表明，FR指标的性能优于一些著名的局部相似性指标，如共同邻居（CN）指标、亚当斯 - 阿达（AA）指标和资源分配（RA）指标。然后我们解释了为什么PWCS能在链接预测中使FR指标具有更好的性能。最后，通过利用PWCS现象提出了一种混合朋友推荐指标（标记为MFR），它进一步提高了链接预测的准确性。
