摘要
复杂网络中重要节点的挖掘对分析和治理现实复杂系统有着重要的指导意义。设计能反映节点重要性的有效计算方法,是高效准确挖掘重要节点的关键。该文基于节点的邻居信息,采用特征工程中的特征提取、特征重构等方法提取能有效反映节点局部结构的特征向量。利用局部特征向量,通过回归模型建立节点局部结构和重要性的关系模型。在13个真实网络上的实验结果表明,相比于已有的重要节点挖掘基准方法,该方法具有更优的性能。
To mine important nodes in complex networks is very important for analyzing and governing real complex systems.Designing a good indicator that reflects the importance of nodes is a key issue on efficiently and accurately mining critical nodes.On the bases of the neighbor information of nodes,the features that can effectively reflect the local structure of nodes are extracted through feature extraction and reconstruction.The relational model between local structure and real importance of nodes is established by utilizing regression model based on the extracted features.The experimental results on 13 real networks show that the proposed method outperforms the benchmark methods of critical nodes identification.
作者
潘侃
尹春林
王磊
陈端兵
PAN Kan;YIN Chunlin;WANG Lei;CHEN Duanbing(Electric Power Research Institute,Yunnan Power Grid Co.Ltd.,Kunming 650217;Union Big Data Tech.Inc.,Chengdu 610041;Big Data Research Center,University of Electronic Science and Technology of China,Chengdu 611731)
出处
《电子科技大学学报》
EI
CAS
CSCD
北大核心
2021年第6期930-937,共8页
Journal of University of Electronic Science and Technology of China
基金
国家自然科学基金(61673085)。
关键词
复杂网络
重要节点
特征工程
局部结构
complex network
local structure
critical nodes
feature engineering
