影响力最大化问题在社交网络中有着广泛的应用,一般地可以将社交网络抽象为静态图,影响力最大化问题是指在图中找出k个最有影响力的顶点,使得信息最大化传播.近年来对此问题的研究主要基于静态图,但是在现实中某些特定网络不可简单地被...影响力最大化问题在社交网络中有着广泛的应用,一般地可以将社交网络抽象为静态图,影响力最大化问题是指在图中找出k个最有影响力的顶点,使得信息最大化传播.近年来对此问题的研究主要基于静态图,但是在现实中某些特定网络不可简单地被抽象为静态图,如社交网络及路网中节点间只在某些特定时间存在联系,即节点间的联系是具有时序性的.因此,本文研究了时序图影响力最大化问题,即在时序图上寻找k个顶点使得信息在特定的时间段内最大化传播.传播模型的选择和节点间传播概率的计算是影响力最大化问题的基础,由于基于静态图的IC(Independent Cascade model)传播模型无法应用于时序图,因此本文首先对IC模型进行改进,并提出了ICT(Independent Cascade model on Temporal graph)传播模型,使信息可以通过ICT传播模型在时序图上进行传播.而后通过改进PageRank算法来进行计算节点间的传播概率.然后在此基础上将时序图影响力最大化问题分为两步来进行实现.第一步首先研究时序图节点影响力的计算,并提出了用来计算节点影响力的SIC(Single Node Influence Computation)算法,然后通过对时序图中节点联系时序性这一特性的研究提出了一种改进算法ISIC(Improved SIC).第二步是在第一步结果的基础上来寻找k个种子节点,首先提出了一种基本的时序图影响力最大化算法BIMT(Basic Method for IMTG).但BIMT难以高效解决大规模时序图影响力最大化问题,因此通过优化节点边际效应的计算时间,提出了高效的AIMT(Advanced Method for IMTG)算法,然后通过避免某些节点边际效应的重复计算,对AIMT算法进行改进,从而提出了IMIT(Improved Method for IMTG)算法.最后通过大量实验验证了AIMT和IMIT两种算法高效性和扩展性,相比于BIMT算法,AIMT和IMIT可以更加快速地解决大规模时序图影响力最大化问题.展开更多
Noise-assisted multivariate empirical mode decomposition(NA-MEMD) is suitable to analyze multichannel electroencephalography(EEG) signals of non-stationarity and non-linearity natures due to the fact that it can provi...Noise-assisted multivariate empirical mode decomposition(NA-MEMD) is suitable to analyze multichannel electroencephalography(EEG) signals of non-stationarity and non-linearity natures due to the fact that it can provide a highly localized time-frequency representation.For a finite set of multivariate intrinsic mode functions(IMFs) decomposed by NA-MEMD,it still raises the question on how to identify IMFs that contain the information of inertest in an efficient way,and conventional approaches address it by use of prior knowledge.In this work,a novel identification method of relevant IMFs without prior information was proposed based on NA-MEMD and Jensen-Shannon distance(JSD) measure.A criterion of effective factor based on JSD was applied to select significant IMF scales.At each decomposition scale,three kinds of JSDs associated with the effective factor were evaluated:between IMF components from data and themselves,between IMF components from noise and themselves,and between IMF components from data and noise.The efficacy of the proposed method has been demonstrated by both computer simulations and motor imagery EEG data from BCI competition IV datasets.展开更多
文摘影响力最大化问题在社交网络中有着广泛的应用,一般地可以将社交网络抽象为静态图,影响力最大化问题是指在图中找出k个最有影响力的顶点,使得信息最大化传播.近年来对此问题的研究主要基于静态图,但是在现实中某些特定网络不可简单地被抽象为静态图,如社交网络及路网中节点间只在某些特定时间存在联系,即节点间的联系是具有时序性的.因此,本文研究了时序图影响力最大化问题,即在时序图上寻找k个顶点使得信息在特定的时间段内最大化传播.传播模型的选择和节点间传播概率的计算是影响力最大化问题的基础,由于基于静态图的IC(Independent Cascade model)传播模型无法应用于时序图,因此本文首先对IC模型进行改进,并提出了ICT(Independent Cascade model on Temporal graph)传播模型,使信息可以通过ICT传播模型在时序图上进行传播.而后通过改进PageRank算法来进行计算节点间的传播概率.然后在此基础上将时序图影响力最大化问题分为两步来进行实现.第一步首先研究时序图节点影响力的计算,并提出了用来计算节点影响力的SIC(Single Node Influence Computation)算法,然后通过对时序图中节点联系时序性这一特性的研究提出了一种改进算法ISIC(Improved SIC).第二步是在第一步结果的基础上来寻找k个种子节点,首先提出了一种基本的时序图影响力最大化算法BIMT(Basic Method for IMTG).但BIMT难以高效解决大规模时序图影响力最大化问题,因此通过优化节点边际效应的计算时间,提出了高效的AIMT(Advanced Method for IMTG)算法,然后通过避免某些节点边际效应的重复计算,对AIMT算法进行改进,从而提出了IMIT(Improved Method for IMTG)算法.最后通过大量实验验证了AIMT和IMIT两种算法高效性和扩展性,相比于BIMT算法,AIMT和IMIT可以更加快速地解决大规模时序图影响力最大化问题.
基金Projects(61201302,61372023,61671197)supported by the National Natural Science Foundation of ChinaProject(201308330297)supported by the State Scholarship Fund of ChinaProject(LY15F010009)supported by Zhejiang Provincial Natural Science Foundation,China
文摘Noise-assisted multivariate empirical mode decomposition(NA-MEMD) is suitable to analyze multichannel electroencephalography(EEG) signals of non-stationarity and non-linearity natures due to the fact that it can provide a highly localized time-frequency representation.For a finite set of multivariate intrinsic mode functions(IMFs) decomposed by NA-MEMD,it still raises the question on how to identify IMFs that contain the information of inertest in an efficient way,and conventional approaches address it by use of prior knowledge.In this work,a novel identification method of relevant IMFs without prior information was proposed based on NA-MEMD and Jensen-Shannon distance(JSD) measure.A criterion of effective factor based on JSD was applied to select significant IMF scales.At each decomposition scale,three kinds of JSDs associated with the effective factor were evaluated:between IMF components from data and themselves,between IMF components from noise and themselves,and between IMF components from data and noise.The efficacy of the proposed method has been demonstrated by both computer simulations and motor imagery EEG data from BCI competition IV datasets.
