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基于CP张量分解识别多层网络的关键节点论文被Chaos接受

已有 1751 次阅读 2017-7-4 16:17 |个人分类:科研论文|系统分类:论文交流|关键词:Multilayer,networks,,Tensor,decomposition,,Centrality,measure| networks, Multilayer

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.



       



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