基于高阶自包含协同过滤的有向网络链路预测

针对大部分现存有向网络链路预测方法仅关注有向局部结构及互惠链接信息而忽略有向全局结构的问题,提出高阶自包含协同过滤(HSCF)链路预测框架。首先,利用随机游走方法计算高阶相似度矩阵去保持有向网络的高阶路径信息;其次,将高阶相似度矩阵与协同过滤方法相融合构建HSCF框架;最后,把所提框架分别与有向共同邻居(DCN)、有向Adamic-Adar(DAA)、有向资源分配(DRA)和势能理论Bifan 4个典型有向结构相似度相融合,并由此提出HSCF-DCN、HSCF-DAA、HSCF-DRA和HSCF-Bifan 4个有向网络预测指标。在10个真实有向网络上的实验结果表明,与基准指标相比,HSCF-DCN、HSCF-DAA、HSCF-DRA和HSCF-Bifan的受试者工作特征（ROC）曲线下方面积（AUC）值分别平均提高了8.16%、8.85%、9.64%和10.33%,且F分数值分别平均提高了66.62%、68.32%、68.95%和76.18%。

Link prediction in directed network based on high-order self-included collaborative filtering

Aiming at the problem that most existing directed network link prediction methods only focus on the directed local and reciprocal link information and ignore the directed global structure information, a High-order Self-included Collaborative Filtering （HSCF） link prediction framework was proposed. Firstly, random walk method was used to calculate the high-order similarity matrix to preserve the high-order path information of the directed network. Secondly, an HSCF framework was constructed by combining the high-order similarity matrix with collaborative filtering method. Finally, the proposed framework was integrated with four typical directed structure similarity indices including Directed Common Neighbor （DCN）, Directed Adamic-Adar （DAA）, Directed Resource Allocation （DRA） and potential theory （Bifan）, and four directed network prediction indices HSCF-DCN, HSCF-DAA, HSCF-DRA and HSCF-Bifan were proposed on this basis. Compared with the baseline indices on ten real directed networks, the experimental results show that the AUC （Area Under Curve of Receiver Operating Characteristic （ROC）） values of HSCF-DCN, HSCF-DAA, HSCF-DRA and HSCF-Bifan are increased by an average of 8.16%, 8.85%, 9.64% and 10.33% respectively and the F-score values of them are increased by an average of 66.62%, 68.32%, 68.95% and 76.18% respectively.