摘要
为了提高复杂网络链路预测的性能,采用拓扑相似和XGBoost算法来完成复杂网络链路预测。利用复杂网络拓扑结构建立邻接矩阵,求解共同邻居集合,然后根据拓扑相似理论计算复杂网络相似得分函数,将各个时间窗的得分函数和权重参数作为输入,采用XGBoost算法实现复杂网络的链路预测。通过差异化设置XGBoost算法的两个正则化系数,测试其对链路预测准确率的影响,获取最优正则化系数,从而得到稳定的XGBoost链路预测模型。实验证明,时间窗数量设置合理的情况下,相比常用网络链路预测算法,基于拓扑相似和XGBoost算法的预测准确率优势明显,且预测时间性能和其他算法的差距较小,尤其适用于大规模的复杂网络链路预测。
In order to improve the performance of complex network link prediction, topology similarity and XGBoost algorithm are used to complete link prediction in complex network.According to the topological structure of complex network, the adjacency matrix is established to solve the common neighbor set.Then the similarity score function of complex network is calculated according to the topological similarity theory.The score function and weight parameters of each time window are taken as input, and XGBoost algorithm is used to realize the link prediction of complex network.By setting two regularization coefficients of XGBoost algorithm through differentiation, the influence on link prediction accuracy is tested, and the optimal regularization coefficient is obtained, thus a stable XGBoost link prediction model is obtained.The experimental results show that, compared with the common network link prediction algorithms, the prediction accuracy based on topology similarity and XGBoost algorithm has obvious advantages, and the prediction time performance is smaller than other algorithms, especially suitable for large-scale complex network link prediction.
作者
龚追飞
魏传佳
GONG Zhui-fei;WEI Chuan-jia(College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou 310023,China)
出处
《计算机科学》
CSCD
北大核心
2021年第12期226-230,共5页
Computer Science
基金
国家自然科学基金(61773348)
浙江省自然科学基金(LY17F030016)。
