【摘要】 链接预测属于复杂网络分析的研究分支,它根据网络历史结构信息预测未来节点间会产生链接的可能性,从而挖掘网络的传播和演化方式。通过引入差分化节点的贡献权重并结合经典的节点和共邻节点网络拓扑结构特征,分别应用七类有监督学习-分类模型对社交、生物、交通等不同领域的八个真实复杂网络数据集进行实验,并采用Precision和ROC曲线对实验结果进行分析与评价。实验表明,引入基于差分化节点的贡献特征能够在深入挖掘网络结构信息的基础上比其余特征有更优的预测精确度,同时差异化的分类模型和特征选择对链接预测性能有相异的影响。更多还原

【Abstract】 Link prediction is a branch of complex network analysis.It predicts future linkage between nodes according to historical network structure information in order to reveal the network evolution and diffusion.This paper introduces a novel feature based on differentiated node contribution and provides seven supervised learning frameworks to carry the classification task via our introduced features and two categories of classic topological features which include node information and common neighbors’ property.The experimental results on eight real-world networks demonstrate interesting finding on the prediction influence by different classification models and topological diverse features via precision and ROC curve.In addition,we prove that our newly introduced features outperform the classic ones,which can mine the latent network information and enhance accuracy of link prediction.更多还原