摘要
时序网络中的重要节点评估一直是社交网络领域中的热门话题,在病毒传播、信息挖掘等方面有着诸多应用。现有的算法虽然考虑到节点的邻居信息对节点产生的影响,但建模时仅仅考虑节点是否存在关系,对于链接强度的考虑不够全面。针对此问题,从时间层面去考虑节点链接强度,提出一种新的层内邻接矩阵。同时,综合考虑节点自身的邻居和跨层节点的公共邻居来衡量层间耦合关系,提出多指标交互算法;其次,构建加权超邻接模型(WSAM);最后,通过计算时序网络中每个时间层节点的特征向量中心性来评估时序网络中节点的重要性。实验结果表明,TWCR算法在时序最大连通分量、网络性能、容错性三个方面优于SAM、SSAM和WPA方法。
The evaluation of essential nodes in temporal networks was a hot topic in social networks.It was widely used in virus transmission,information mining and so on.Although the current algorithms considered the influence of neighbour information on nodes,they only focused on whether nodes had relationships without fully considering the link strength.A new intra-layer adjacency matrix was proposed to solve the problem by considering the node-link power from the time level.At the same time,a multi-index interaction algorithm was used to measure the coupling relationship between layers by considering the neighbours of nodes themselves and the common neighbours of cross-layer nodes comprehensively.Secondly,the weighted super-adjacency model(WSAM)was constructed on this basis.Finally,the importance of nodes in the temporal network was evaluated by calculating the eigenvector centrality of each node in the temporal network.Experimental results showed that the TWCR algorithm outperformed SAM,SSAM and WPA regarding the maximum connected component,network performance and fault tolerance.
作者
杨晔彬
姜久雷
邹鹏
YANG Yebin;JIANG Jiulei;ZOU Peng(School of Computer Science and Engineering,North Minzu University,Yinchuan 750021,China;School of Computer Science and Engineering,Changshu Institute of Technology,Changshu 215500,China)
出处
《郑州大学学报（理学版）》
CAS
北大核心
2024年第2期59-65,共7页
Journal of Zhengzhou University:Natural Science Edition
基金
国家自然科学基金项目(6172002)。
关键词
多层网络
加权时序网络
节点重要性
多指标交互指数
最大连通分量
multi-layer network
weighted temporal network
node importance
multi-index interaction index
largest connected component
