最近， 在导师武汉大学邹秀芬教授的悉心指导下， 自已关于多层网络控制能量和能控性工作（ Control energy and controllability of multilayer networks.pdf ） 被Advances in Complex Systems接受发表，希望大家喜欢。 Title: Control energy and controllability of multilayer networks Published Online: http://www.worldscientific.com/doi/abs/10.1142/S0219525917500084 Abstract: The controllability of multilayer networks has become increasingly important in many areas of science and engineering. In this paper, we identify the general rules that determine the controllability and control energy cost of multilayer networks. First, we quantitatively estimate the control energy cost of multilayer networks and investigate the impacts of different coupling strength and coupling patterns on the control energy cost for multilayer networks. The results indicate that the average energy and the coupling strength have an approximately linear relationship in multilayer networks with two layers. Second, we study how the coupling strength and the connection patterns between different layers affect the controllability of multilayer networks from both theoretical and numerical aspects. The obtained piecewise function relations between the controllability’s measure and coupling strength reveal that the existence of an optimal coupling strength for the different interconnection strategies in multilayer networks. In particular, the numerical experiments demonstrate that there exists a tradeoff between the optimal controllability and optimal control energy for selecting inter-layer connection patterns in multilayer networks. These results provide a comprehensive understanding of the impact of interlayer couplings on the controllability and control energy cost for multilayer networks and provide a methodology for selecting the control nodes and coupling strength to maximize the controllability and minimize the control energy cost. Keywords: Multilayer networks; controllability; control energy; coupling strength; coupling patterns.
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)
好久没有发博文了，今天汇报一下自已最近的一个工作吧。近一年的时间，我在关注多层网络模型的相关工作，主要想从多层网络的角度去开发一些多维数据整合的方法。这里有一个热点问题：如何定义多层网络的中心性标准来识别多层网络的关键节点。近日，基于多层网络的张量表示和CP张量分解，我们提出一个新的中心性标准（EDCPTD centrality）去识别多层网络的关键节点，相关成果近日发表在 Chaos: An Interdisciplinary Journal of Nonlinear Science ( Identifying key nodes in multilayer networks based on tensor decomposition.pdf ) 杂志上，希望大家喜欢。感谢导师武汉大学邹秀芬教授在论文发表过程中的精心指导，鼓励和帮助！ Title: Identifying key nodes in multilayer networks based on tensor decomposition Published online: http://aip.scitation.org/doi/abs/10.1063/1.4985185 Abstract: The identification of essential agents in multilayer networks characterized by different types of interactions is a crucial and challenging topic, one that is essential for understanding thetopological structure and dynamic processes of multilayer networks. In this paper, we use the fourth-order tensor to represent multilayer networks and propose a novel method to identify essential nodes based on CANDECOMP/PARAFAC (CP) tensor decomposition, referred to as the EDCPTD centrality. This method is based on the perspective of multilayer networked structures, which integrate the information of edges among nodes and links between different layers to quantify the importance of nodes in multilayer networks. Three real world multilayer biological networks are used to evaluate the performance of the EDCPTD centrality. The bar chart and ROC curves of these multilayer networks indicate that the proposed approach is a good alternative index to identify real important nodes. Meanwhile, by comparing the behavior of both the proposed method and the aggregated single layer methods, we demonstrate that neglecting the multiple relationships between nodes may lead to incorrect identification of the most versatile nodes. Furthermore, the Gene Ontology functional annotation demonstrates that the identified top nodes based on the proposed approach play a significant role in many vital biological processes. Finally, we have implemented many centrality methods of multilayer networks (including our method and the published methods) and created a visual software based on the MATLAB GUI, called ENMNFinder, which can be used by other researchers.
标签: Multilayer
