有限步传播范围期望指标判别节点传播影响力

在线社交网络逐渐成为人们不可或缺的重要工具,识别网络中具有高影响力的节点作为初始传播源,在社会感知与谣言控制等方面具有重要意义.本文基于独立级联模型,给出了一个描述有限步传播范围期望的指标-传播度,并设计了一种高效的递推算法.该指标在局部拓扑结构信息的基础上融合了传播概率对影响力进行刻画,能够较好地反映单个节点的传播影响力.对于多传播源影响力极大化问题,本文提出了一种基于传播度的启发式算法-传播度折扣算法,使得多个传播源的联合影响力最大.最后,将上述方法应用到三个真实网络中,与经典指标和方法相比,该方法不需要知道网络的全局结构信息,而是充分了利用网络的局部结构信息,可以较快地筛选出高传播影响力的传播源.     

花簇分形无标度网络中节点影响力的区分度

大量研究表明分形尺度特性广泛存在于真实复杂系统中, 且分形结构显著影响网络上的传播动力学行为. 虽然复杂网络的节点传播影响力吸引了越来越多学者的关注, 但依旧缺乏针对分形网络结构的节点影响力的系统研究. 鉴于此, 本文基于花簇分形网络模型, 研究了分形无标度结构上的节点传播影响力. 首先, 对比了不同分形维数下的节点影响力, 结果表明, 当分形维数很小时, 节点影响力的区分度几乎不随节点度变化, 很难区分不同节点的传播影响力, 而随着分形维数的增大, 从全局和局域角度都能很容易识别网络中的超级传播源. 其次, 通过对原分形网络进行不同程度的随机重连来分析网络噪声对节点影响力区分度的影响, 发现在低维分形网络上, 加入网络噪声之后能够容易区分不同节点的影响力, 而在无穷维超分形网络中, 加入网络噪声之后能够区分中间度节点的影响力, 但从全局和局域角度都很难识别中心节点的影响力. 所得结论进一步补充、深化了基于花簇分形网络的节点影响力研究, 研究结果对实际病毒传播的预警控制提供了一定的理论借鉴.     

识别高阶网络传播中最有影响力的节点

识别网络传播中最有影响力的节点是控制传播速度和范围的重要步骤,有助于加速有益信息扩散,抑制流行病、谣言和虚假信息的传播等.已有研究主要基于描述点对交互的低阶复杂网络.然而,现实中个体间的交互不仅发生在点对之间,也发生在3个及以上节点形成的群体中.群体交互可利用高阶网络来刻画,如单纯复形与超图.本文研究单纯复形上最有影响力的传播者识别方法.首先,提出单纯复形上易感-感染-恢复(SIR)微观马尔可夫链方程组,定量刻画单纯复形上的疾病传播动力学.接下来利用微观马尔可夫链方程组计算传播动力学中节点被感染的概率.基于网络结构与传播过程,定义节点的传播中心性,用于排序节点传播影响力.在两类合成单纯复形与4个真实单纯复形上的仿真结果表明,相比于现有高阶网络中心性和复杂网络中最优的中心性指标,本文提出的传播中心性能更准确地识别高阶网络中最有影响力的传播者.     

综合考虑局部和全局特性的电网关键节点识别

为有效识别网络中的关键节点,提出一种综合考虑网络局部和全局特性的节点重要性识别综合指标,依据此指标对加权标准测试系统IEEE39和IEEE118中的节点进行重要性排序,并将排序结果与基于介数法和点权法对节点重要性进行排序结果进行对比,基于结构的网络效能分析和基于动力学的失同步扩散时间、同步能力比较均表明,提出的基于综合指标的节点重要性排序更合理,优于基于介数和点权的节点重要性识别方法.     

云计算网络中边界节点识别方法改进研究

目前,云计算网络为人们的生产和生活提供了各种应用和服务,网络边界节点的识别问题一直较难解决。传统的网络中边界节点类型复杂,边界部署成本高,较多感知模型和静态场景难以实现。为此,提出一种改进的云计算网络中边界节点识别方法,通过制定边界部署规则确定边界节点部署数量及要求,对边界节点感知漏洞进行修补,保证边界节点对网络区域内的全覆盖识别,最后设计出云计算网络识别模型,实现了云计算网络中边界节点正确识别。仿真实验表明,提出的边界节点识别方法在稳定性、识别率和识别数量上都比传统方法有优越性,具有应用价值。     

动态复杂网络中节点影响力的研究进展

节点影响力的识别和预测具有重要的理论意义和应用价值,是复杂网络的热点研究领域.目前大多数研究方法都是针对静态网络或动态网络某一时刻的快照进行的,然而在实际应用场景中,社会、生物、信息、技术等复杂网络都是动态演化的.因此在动态复杂网络中评估节点影响力以及预测节点未来影响力,特别是在网络结构变化之前的预测更具意义.本文系统地总结了动态复杂网络中节点影响力算法面临的三类挑战,即在增长网络中,节点影响力算法的计算复杂性和时间偏见;网络实时动态演化时,节点影响力算法的适应性;网络结构微扰或突变时,节点影响力算法的鲁棒性,以及利用网络结构演变阐释经济复杂性涌现的问题.最后总结了这一研究方向几个待解决的问题并指出未来可能的发展方向.     

一种有效的基于三角结构的复杂网络节点影响力度量模型

度量复杂网络中的节点影响力对理解网络的结构和功能起着至关重要的作用.度、介数、紧密度等经典指标能够一定程度上度量节点影响力,k-shell和H-index等指标也可以应用于评价节点影响力.然而这些模型都存在着各自的局限性.本文基于节点与邻居节点之间的三角结构提出了一种有效的节点影响力度量指标模型(local triangle centrality,LTC),该模型不仅考虑节点间的三角结构,同时考虑了周边邻居节点的规模.我们在多个真实复杂网络上进行了大量实验,通过SIR模型进行节点影响力仿真实验,证明LTC指标相比于其他指标能够更加准确地度量节点的传播影响力.节点删除后网络鲁棒性的实验结果也表明LTC指标具有更好效果.     

基于层间相似性的时序网络节点重要性研究

时序网络可以更加准确地描述节点之间的交互顺序和交互关系.结合多层耦合网络分析法,本文提出了基于节点层间相似性的超邻接矩阵时序网络节点重要性识别方法,与经典的认为所有层间关系为常数不同,层间关系用节点的邻居拓扑重叠系数进行度量.Workspace和Enrons数据集上的结果显示:相比经典的方法,使用该方法得到的Kendall’sτ值在各时间层上的平均提高,最高为17.72%和12.44%,结果表明层间相似性的度量对于时序网络的节点重要性度量具有十分重要的意义.     

基于子网划分的电网关键节点识别

结合电网拓扑结构和潮流追踪技术,提出一种基于子网划分的电网关键节点识别方法。首先,根据发电机节点的邻域信息和功率将发电机节点划分为不同的子集,然后根据电网的系数分配矩阵将负荷节点划分到为其提供最大功率的发电机节点子集中,完成子网划分。接着采用多属性决策法对每个子网的节点进行排序,进一步改进并计算每个子网的结构系数,作为衡量子网重要性的指标。根据子网重要性,从每个子网中提取特定比例的候选关键节点,对这些候选节点依据多属性决策法重新排序,得到关键节点的最终排序。以IEEE14、IEEE57和IEEE118三种节点系统为例进行分析,得到各个系统的子网划分结果和各个标准网络的重要节点排序结果。采用本文方法、PageRank法和多属性决策法分别进行关键节点排序,并对排序靠前的关键节点进行级联故障性能实验和网络效能实验。实验表明,本文算法选择的关键节点对整个网络的传播性能影响最大,优于其他两种关键节点识别方法。     

基于复杂网络动力学模型的无向加权网络节点重要性评估

定量分析识别复杂网络中的重要节点对于研究复杂网络鲁棒性和脆弱性意义重大,当前基于网络结构的节点重要性评估方法成果丰富,而基于复杂网络动力学模型的节点重要性评估方法较少.针对无向加权网络,本文首先提出了构建其对应的复杂网络动力学模型的方法,并证明了该类复杂网络动力学模型是大范围内一致渐近稳定的;然后建立了复杂网络动力学模型的偏离均值和基于偏离均值的方差两级节点重要性评估标准;最后给出了扰动测试和破坏测试两种基于复杂网络动力学模型的节点重要性评估方法.基于复杂网络动力学模型的节点重要性评估方法不仅结合了网络拓扑结构信息,同时又结合了节点自身的特性,所以评价结果更为全面.将这两种方法用于ARPA(advanced research project agency)网络、对称无向加权网络、社交网络、Dobbs-Watts-Sabel网络和Barrat-Barthelemy-Vespignani网络的重要节点评估,并与已有的复杂网络节点重要性分析方法进行比较,证明了所提出方法的有效性.     

复杂网络中最小K-核节点的传播能力分析

K-核分解方法对于识别复杂网络传播动力学中最重要节点具有重要的价值, 然而该方法无法对复杂网络中大量最小K-核节点的传播能力进行准确度量. 本文主要考察最小K-核节点的传播行为, 利用其邻居的K-核信息, 提出一种度量这类节点传播能力的方法. 实证网络数据集的传播行为仿真结果表明, 该方法与度、介数等指标相比更能准确度量最小K-核节点的传播能力. 关键词： 复杂网络 传播能力 K-核分解 最小K-核节点     

Identifying influential nodes in complex networks

Identifying influential nodes that lead to faster and wider spreading in complex networks is of theoretical and practical significance. The degree centrality method is very simple but of little relevance. Global metrics such as betweenness centrality and closeness centrality can better identify influential nodes, but are incapable to be applied in large-scale networks due to the computational complexity. In order to design an effective ranking method, we proposed a semi-local centrality measure as a tradeoff between the low-relevant degree centrality and other time-consuming measures. We use the Susceptible-Infected-Recovered (SIR) model to evaluate the performance by using the spreading rate and the number of infected nodes. Simulations on four real networks show that our method can well identify influential nodes.     

Detection of influential nodes with multi-scale information

The identification of influential nodes in complex networks is one of the most exciting topics in network science. The latest work successfully compares each node using local connectivity and weak tie theory from a new perspective. We study the structural properties of networks in depth and extend this successful node evaluation from single-scale to multi-scale. In particular, one novel position parameter based on node transmission efficiency is proposed, which mainly depends on the shortest distances from target nodes to high-degree nodes. In this regard, the novel multi-scale information importance (MSII) method is proposed to better identify the crucial nodes by combining the network's local connectivity and global position information. In simulation comparisons, five state-of-the-art algorithms, i.e. the neighbor nodes degree algorithm (NND), betweenness centrality, closeness centrality, Katz centrality and the k-shell decomposition method, are selected to compare with our MSII. The results demonstrate that our method obtains superior performance in terms of robustness and spreading propagation for both real-world and artificial networks.     

LCH: A local clustering H-index centrality measure for identifying and ranking influential nodes in complex networks

Identifying influential nodes in complex networks is one of the most significant and challenging issues, which may contribute to optimizing the network structure, controlling the process of epidemic spreading and accelerating information diffusion. The node importance ranking measures based on global information are not suitable for large-scale networks due to their high computational complexity. Moreover, they do not take into account the impact of network topology evolution over time, resulting in limitations in some applications. Based on local information of networks, a local clustering H-index (LCH) centrality measure is proposed, which considers neighborhood topology, the quantity and quality of neighbor nodes simultaneously. The proposed measure only needs the information of first-order and second-order neighbor nodes of networks, thus it has nearly linear time complexity and can be applicable to large-scale networks. In order to test the proposed measure, we adopt the susceptible-infected-recovered (SIR) and susceptible-infected (SI) models to simulate the spreading process. A series of experimental results on eight real-world networks illustrate that the proposed LCH can identify and rank influential nodes more accurately than several classical and state-of-the-art measures.     

利用邻域“结构洞”寻找社会网络中最具影响力节点

识别复杂网络中的关键节点对网络结构优化和鲁棒性增强具有十分重要的意义. 经典的关键节点测量方法在一定程度上能够辨识网络中影响力节点, 但存在一定局限性: 局部中心性测量方法仅考虑节点邻居的数目, 忽略了邻居间的拓扑关系, 不能在计算中反映邻居节点间的相互作用; 全局测量方法则由于算法本身的复杂性而不能应用于大规模社会网络的分析, 另外, 经典的关键节点测量方法也没有考虑社会网络特有的社区特征. 为高效、准确地辨识具有社区结构的社会网络中最具影响力节点, 提出了一种基于节点及其邻域结构洞的局部中心性测量方法, 该方法综合考虑了节点的邻居数量及其与邻居间的拓扑结构, 在节点约束系数的计算中同时体现了节点的度属性和“桥接”属性. 利用SIR(易感-感染-免疫)模型在真实社会网络数据上对节点传播能力进行评价后发现, 所提方法可以准确地评价节点的传播能力且具有强的鲁棒性.     

Identifying influential nodes based on graph signal processing in complex networks

Identifying influential nodes in complex networks is of both theoretical and practical importance. Existing methods identify influential nodes based on their positions in the network and assume that the nodes are homogeneous. However,node heterogeneity(i.e., different attributes such as interest, energy, age, and so on) ubiquitously exists and needs to be taken into consideration. In this paper, we conduct an investigation into node attributes and propose a graph signal processing based centrality(GSPC) method to identify influential nodes considering both the node attributes and the network topology. We first evaluate our GSPC method using two real-world datasets. The results show that our GSPC method effectively identifies influential nodes, which correspond well with the underlying ground truth. This is compatible to the previous eigenvector centrality and principal component centrality methods under circumstances where the nodes are homogeneous. In addition, spreading analysis shows that the GSPC method has a positive effect on the spreading dynamics.     

微博双向“关注”网络节点中心性及传播 影响力的分析

根据新浪微博的实际数据, 建立了两个基于双向“关注”的用户关系网络, 通过分析网络拓扑统计特征, 发现二者均具有小世界、无标度特征. 通过对节点度、紧密度、介数和k-core 四个网络中心性指标进行实证分析, 发现节点度服从分段幂率分布; 介数相比其他中心性指标差异性最为显著; 两个网络均具有明显的层次性, 但不是所有度值大的节点核数也大; 全局范围内各中心性指标之间存在着较强的相关性, 但在度值较大的节点群这种相关性明显减弱. 此外, 借助基于传染病动力学的SIR信息传播模型来分析四种指标在刻画节点传播能力方面的差异性, 仿真结果表明, 选择具有不同中心性指标的初始传播节点, 对信息传播速度和范围均具有不同影响; 紧密度和k-core较其他指标可以更加准确地描述节点在信息传播中所处的网络核心位置, 这有助于识别信息传播拓扑网络中的关键节点.     

A novel method for identifying influential nodes in complex networks based on gravity model

How to identify influential nodes in complex networks is an essential issue in the study of network characteristics. A number of methods have been proposed to address this problem, but most of them focus on only one aspect. Based on the gravity model, a novel method is proposed for identifying influential nodes in terms of the local topology and the global location. This method comprehensively examines the structural hole characteristics and K-shell centrality of nodes, replaces the shortest distance with a probabilistically motivated effective distance, and fully considers the influence of nodes and their neighbors from the aspect of gravity. On eight real-world networks from different fields, the monotonicity index, susceptible-infected-recovered (SIR) model, and Kendall's tau coefficient are used as evaluation criteria to evaluate the performance of the proposed method compared with several existing methods. The experimental results show that the proposed method is more efficient and accurate in identifying the influence of nodes and can significantly discriminate the influence of different nodes.     

The relative importance of structure and dynamics on node influence in reversible spreading processes

The reversible spreading processes with repeated infection widely exist in nature and human society, such as gonorrhea propagation and meme spreading. Identifying influential spreaders is an important issue in the reversible spreading dynamics on complex networks, which has been given much attention. Except for structural centrality, the nodes’ dynamical states play a significant role in their spreading influence in the reversible spreading processes. By integrating the number of outgoing edges and infection risks of node’s neighbors into structural centrality, a new measure for identifying influential spreaders is articulated which considers the relative importance of structure and dynamics on node influence. The number of outgoing edges and infection risks of neighbors represent the positive effect of the local structural characteristic and the negative effect of the dynamical states of nodes in identifying influential spreaders, respectively. We find that an appropriate combination of these two characteristics can greatly improve the accuracy of the proposed measure in identifying the most influential spreaders. Notably, compared with the positive effect of the local structural characteristic, slightly weakening the negative effect of dynamical states of nodes can make the proposed measure play the best performance. Quantitatively understanding the relative importance of structure and dynamics on node influence provides a significant insight into identifying influential nodes in the reversible spreading processes.     

加权网络中基于冗余边过滤的k-核分解排序算法

k-核分解排序法对于度量复杂网络上重要节点的传播影响力具有重要的理论意义和应用价值,但其排序粗粒化的缺陷也不容忽视.最新研究发现,一些真实网络中存在局域连接稠密的特殊构型是导致上述问题的根本原因之一.当前的解决方法是利用边两端节点的外部连边数度量边的扩散性,采取过滤网络边来减少这种稠密结构给k-核分解过程造成的干扰,但这种方法并没有考虑现实网络上存在权重的普遍性.本文利用节点权重和权重分布重新定义边的扩散性,提出适用于加权网络结构的基于冗余边过滤的k-核分解排序算法:filter-core.通过世界贸易网、线虫脑细胞网和科学家合著网等真实网络的SIR(susceptible-infectedrecovered)传播模型的仿真结果表明,该算法相比其他加权k-核分解法,能够更准确地度量加权网络上具有重要传播影响力的核心节点及核心层.