近日， 在导师武汉大学邹秀芬教授的悉心指导下， 自已最新的一篇论文被数学权威期刊 Applied Mathematical Modelling 杂志接受并Online, 有点小激动，这是7月份接受的第二篇关于多层网络关键节点识别的论文，希望大家喜欢。在这篇论文中，我们考虑多层网络的四个中心性指标：Authority and hub centralities of nodes, Authority and hub centralities of layers。基于多层网络的4阶张量表示和单层网络HITS中心性算法的迭代思想，我设计一个新颖的基于张量计算的迭代格式去获得上面四个中心性指标，并且我们从理论上证明了这个迭代格式的收敛性。最后通过整合这四个指标，我们提出了一个新颖的中心性指标Singular Vector of Tensors (SVT) centrality去识别多层网络的关键节点。数值实验证明我们的算法具有较好的性能。 Title: A new centrality measure of nodes in multilayer networks under the framework of tensor computation Online Web Page: http://www.sciencedirect.com/science/article/pii/S0307904X1730450X Abstract: One challenging issue in information science, biological systems and many other field is to determine the most central agents in multilayer networked systems characterized by different types of interrelationships. In this paper, using a fourth-order tensor to represent multilayer networks, we propose a new centrality measure, referred to as the Singular Vector of Tensor (SVT) centrality, which is used to quantitatively evaluate the importance of nodes connected by different types of links in multilayer networks. First, we present a novel iterative method to obtain four alternative metrics that can quantify the hub and authority scores of nodes and layers in multilayer networked systems. Moreover, we use the theory of multilinear algebra to prove that the four metrics converge to four singular vectors of the adjacency tensor of the multilayer network under reasonable conditions. Furthermore, a novel SVT centrality measure is obtained by integrating these four metrics. The experimental results demonstrate that the proposed method is a new centrality measure that significantly outperforms six other published centrality methods on two real-world multilayer networks related to complex diseases, i.e., gastric and colon cancers. Keywords: Multilayer networks, tensor representation, centrality, essential nodes, tensor iterative computation, singular vector of tensor (SV T)
标签: tensor
