一种基于用户相对权重的在线社交网络信息传播模型

根据在线社交网络信息传播特点和目前社交网络传播模型研究中存在的问题, 本文定义了网络用户之间的相互影响力函数, 在此基础上提出了一种基于用户相对权重的社交网络信息传播模型, 并对网络中的传播路径及传播过程进行了分析, 讨论了不同路径的信息传播影响力.为验证模型的有效性, 将传统的SIR模型和本文模型在六类不同网络拓扑下进行了仿真实验.仿真结果表明, 两类模型在均匀网络中没有明显差异, 但在非均匀网络中本文模型更能体现真实网络特点, 实验同时验证了节点的地位影响着信息的传播, 并且发现英文社交平台Twitter和中文社交平台新浪微博在拓扑结构上具备一定相似性.     

基于在线社交网络的信息传播模型

本文构造了一个基于在线社交网络的信息传播模型.该模型考虑了节点度和传播机理的影响,结合复杂网络和传染病动力学理论,进一步建立了动力学演化方程组.该方程组刻画了不同类型节点随着时间的演化关系,反映了传播动力学过程受到网络拓扑结构和传播机理的影响.本文模拟了在线社交网络中的信息传播过程,并分析了不同类型节点在网络中的行为规律.仿真结果表明:由于在线社交网络的高度连通性,信息在网络中传播的门槛几乎为零;初始传播节点的度越大,信息越容易在网络中迅速传播;中心节点具有较大的社会影响力;具有不同度数的节点在网络中的变 关键词： 在线社交网络 信息传播 微分方程 传染病动力学     

一种改进的基于信息传播率的复杂网络影响力评估算法

评价网络中节点的信息传播影响力对于理解网络结构与网络功能具有重要意义.目前,许多基于最短路径的指标,如接近中心性、介数中心性以及半局部(SP)指标等相继用于评价节点传播影响力.最短路径表示节点间信息传播途径始终选择最优方式,然而实际上网络间的信息传播过程更类似于随机游走,信息的传播途径可以是节点间的任一可达路径,在集聚系数高的网络中,节点的局部高聚簇性有利于信息的有效扩散,若只考虑信息按最优传播方式即最短路径传播,则会低估节点信息传播的能力,从而降低节点影响力的排序精度.综合考虑节点与三步内邻居间的有效可达路径以及信息传播率,提出了一种SP指标的改进算法,即ASP算法.在多个经典的实际网络和人工网络上利用SIR模型对传播过程进行仿真,结果表明ASP指标与度指标、核数指标、接近中心性指标、介数中心性指标以及SP指标相比,可以更精确地对节点传播影响力进行排序.     

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.     

一种基于社交影响力和平均场理论的信息传播动力学模型

在线社会网络中,信息传播蕴含着复杂的动力学成因.本文将传染病模型与社交影响力要素相结合,并针对影响力度量中主要研究静态网络拓扑结构、忽略个体行为特征的问题,提出一种基于动态节点行为和用户影响力的信息传播动力学模型,旨在量化影响力强度,为研究信息扩散过程中不同用户群体状态转变提供理论依据.首先,在网络拓扑结构和用户行为两方面,提取个人记忆和用户交互两个表征,分析影响力形成的内因和外因两个动力学成因,并基于多元线性回归模型,提出一种度量用户社会影响力的方法.其次,在传统传染病SIR(susceptible-infected-recovered)模型基础上,结合信息扩散与传染病蔓延相似的传播机理,综合考虑信息传播的多源并发性和双向性,引入影响力因子,利用平均场理论改进得到一种基于用户影响力的信息传播模型.实验表明,该模型能有效地解释在线社会网络中信息传播的动力学原因,感知社会网络中信息传播演化态势.     

基于区域密度曲线识别网络上的多影响力节点

复杂网络多影响力节点的识别可以帮助理解网络的结构和功能，具有重要的理论意义和应用价值.本文提出一种基于网络区域密度曲线的多影响力节点的识别方法.应用两种不同的传播模型，在不同网络上与其他中心性指标进行了比较.结果表明，基于区域密度曲线的识别方法能够更好地识别网络中的多影响力节点，选中的影响力节点之间的分布较为分散，自身也比较重要.本文所提方法是基于网络的局部信息，计算的时间复杂度较低.     

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.     

基于社交网络的观点传播动力学研究

社交网络和微博是重要的Web2.0应用模式, 其观点传播模式与其他网络媒体以及传统媒体相比有很大差异. 本文提出一种基于在线社交网络的观点传播模型, 研究社交网络中舆论观点扩散的形式与特征. 仿真结果表明: 模型中信息传播的速度与六度分割理论的结论十分符合; 一个带强烈倾向性的观点在固有观点均匀分布的网络中传播的情况下, 稳定时网络中不会出现相反的观点; 稳定时的观点分布与源节点的度和回溯深度有关, 并不受信任界限的限制, 这与Deffuant模型和Hegselmann-Krause模型不同. 同时, 本文还分析了传播意愿、观点变更率和信任界限对弛豫时间的影响.     

一种新的网络传播中最有影响力的节点发现方法

在复杂网络的传播模型研究中, 如何发现最具影响力的传播节点在理论和现实应用中都有重大的意义. 目前的研究一般使用节点的度数、紧密度、介数和K-shell等中心化指标来评价影响力, 这种方法虽然简单, 但是由于它们仅利用了节点自身的内部属性, 因而在评价影响力时精确度并不高, 普遍性适用性较弱.为了解决这个问题, 本文提出了KSC (K-shell and community centrality)指标模型. 此模型不但考虑了节点的内部属性, 而且还综合考虑了节点的外部属性, 例如节点所属的社区等. 然后利用SIR (susceptible-infected-recovered)模型对传播过程进行仿真, 实验证明所提出的方法可以更好地发现最具有影响力的节点, 且可适用于各种复杂网络. 本文为这项具有挑战性研究提供了新的思想和方法. 关键词： 复杂网络 最具影响力的节点 社区划分 中性化测量     

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.     

基于传播免疫的复杂网络可控性研究

复杂网络控制反映了人类对复杂系统的认识深度和改造能力. 最新研究成果基于线性系统控制理论建立了复杂网络可控性的理论架构, 能够发现任意拓扑结构的线性时不变复杂网络中控制全部节点状态的最小驱动节点集, 但是该模型未考虑免疫节点或失效节点对控制信号传播的阻断.在继承该模型优点的前提下, 重新构建了基于传播免疫的复杂网络控制模型.在采用分属于随机免疫和目标免疫两种策略的 4个方法确定免疫节点的情况下,分析14个真实网络的可控性.结果表明:如果将网络中度数、 介数和紧密度指标较高的节点作为免疫节点,将极大地提高控制复杂网络的难度. 从而在一定程度上丰富了以往模型的结论.     

Link prediction in complex networks via modularity-based belief propagation

Link prediction aims at detecting missing, spurious or evolving links in a network, based on the topological information and/or nodes' attributes of the network. Under the assumption that the likelihood of the existence of a link between two nodes can be captured by nodes' similarity, several methods have been proposed to compute similarity directly or indirectly, with information on node degree. However, correctly predicting links is also crucial in revealing the link formation mechanisms and thus in providing more accurate modeling for networks. We here propose a novel method to predict links by incorporating stochastic-block-model link generating mechanisms with node degree. The proposed method first recovers the underlying block structure of a network by modularity-based belief propagation, and based on the recovered block structural information it models the link likelihood between two nodes to match the degree sequence of the network. Experiments on a set of real-world networks and synthetic networks generated by stochastic block model show that our proposed method is effective in detecting missing, spurious or evolving links of networks that can be well modeled by a stochastic block model. This approach efficiently complements the toolbox for complex network analysis, offering a novel tool to model links in stochastic block model networks that are fundamental in the modeling of real world complex networks.     

Evolution mechanism of Weibo top news competition

In a certain period, some news will compete for the top news to gain the most attention and influence, and more news will be submerged in the ocean of news and become mediocre. This article deeply studies the evolution process and competition mechanism of the dissemination of Weibo news. In this paper, we innovatively propose a pre-processing scheme for traditional small-world networks and scale-free networks and divide nodes into three roles:fans, passersby, and anti-fans. The competition mechanism of Weibo top news is defined from the aspects of node role and node aggregation degree. A network evolution model is established based on the competition mechanism. The propagation characteristics of the network evolution model are deeply analyzed, and simulation experiments are performed on the small-world network and the scale-free network. Finally, the validity and rationality of the new model are verified through comparative experiments, and a feasible scheme for the propagation of top news on Weibo is given.     

Adaptive bridge control strategy for opinion evolution on social networks

In this paper, we present an efficient opinion control strategy for complex networks, in particular, for social networks. The proposed adaptive bridge control (ABC) strategy calls for controlling a special kind of nodes named bridge and requires no knowledge of the node degrees or any other global or local knowledge, which are necessary for some other immunization strategies including targeted immunization and acquaintance immunization. We study the efficiency of the proposed ABC strategy on random networks, small-world networks, scale-free networks, and the random networks adjusted by the edge exchanging method. Our results show that the proposed ABC strategy is efficient for all of these four kinds of networks. Through an adjusting clustering coefficient by the edge exchanging method, it is found out that the efficiency of our ABC strategy is closely related with the clustering coefficient. The main contributions of this paper can be listed as follows: (1) A new high-order social network is proposed to describe opinion dynamic. (2) An algorithm, which does not require the knowledge of the nodes' degree and other global∕local network structure information, is proposed to control the "bridges" more accurately and further control the opinion dynamics of the social networks. The efficiency of our ABC strategy is illustrated by numerical examples. (3) The numerical results indicate that our ABC strategy is more efficient for networks with higher clustering coefficient.     

一类高聚类系数的加权无标度网络及其同步能力分析

针对真实世界中大规模网络都具有明显聚类效应的特点, 提出一类具有高聚类系数的加权无标度网络演化模型, 该模型同时考虑了优先连接、三角结构、随机连接和社团结构等四种演化机制. 在模型演化规则中, 以概率p增加单个节点, 以概率1–p增加一个社团. 与以往研究的不同在于新边的建立, 以概率φ在旧节点之间进行三角连接, 以概率1–φ进行随机连接. 仿真分析表明, 所提出的网络度、强度和权值分布都是服从幂律分布的形式, 且具有高聚类系数的特性, 聚类系数的提高与社团结构和随机连接机制有直接的关系. 最后通过数值仿真分析了网络演化机制对同步动态特性的影响, 数值仿真结果表明, 网络的平均聚类系数越小, 网络的同步能力越强. 关键词： 无标度网络 加权网络 聚类系数 同步能力     

Change of network load due to node removal

The interplay between topology changes and the redistribution of traffic plays a significant role in many real-world networks. In this paper we study how the load of the remaining network changes when nodes are removed. This removal operation can model attacks and errors in networks, or the planned control of network topology. We consider a scenario similar to the data communication networks, and measure the load of a node by its betweenness centrality. By analysis and simulations, we show that when a single node is removed, the change of the remaining network’s load is positively correlated with the degree of the removed node. In multiple-node removal, by comparing several node removal schemes, we show in detail how significantly different the change of the remaining network’s load will be between starting the removal from small degree/betweenness nodes and from large degree/betweenness nodes. Moreover, when starting the removal from small degree/betweenness nodes, we not only observe that the remaining network’s load decreases, which is consistent with previous studies, but also find that the load of hubs keeps decreasing. These results help us to make a deeper understanding about the dynamics after topology changes, and are useful in planned control of network topology.     

Detecting local community structures in complex networks based on local degree central nodes

Detecting local communities in real-world graphs such as large social networks, web graphs, and biological networks has received a great deal of attention because obtaining complete information from a large network is still difficult and unrealistic nowadays. In this paper, we define the term local degree central node whose degree is greater than or equal to the degree of its neighbor nodes. A new method based on the local degree central node to detect the local community is proposed. In our method, the local community is not discovered from the given starting node, but from the local degree central node that is associated with the given starting node. Experiments show that the local central nodes are key nodes of communities in complex networks and the local communities detected by our method have high accuracy. Our algorithm can discover local communities accurately for more nodes and is an effective method to explore community structures of large networks.