Research on social network influence maximization algorithm based on time sequential relationship

For the time sequential relationship between nodes in a dynamic social network,social network influence maximization based on time sequential relationship was proved.The problem was to find k nodes on a time sequential social network to maximize the spread of information.Firstly,the propagation probability between nodes was calculated by the improved degree estimation algorithm.Secondly,in order to solve the problem that WCM models based on static social networks could not be applied to time sequential social networks,an IWCM propagation model was proposed and based on this,a two-stage time sequential social network influence maximization algorithm was proposed.The algorithm used the time sequential heuristic phase and the time sequential greedy phase to select the candidate node with the largest influence estimated value inf (u) and the most influential seeds.At last,the efficiency and accuracy of the TIM algorithm were proved by experiments.In addition,the algorithm combines the advantages of the heuristic algorithm and the greedy algorithm,reducing the calculation range of the marginal revenue from all nodes in the network to the candidate nodes,and greatly shortens the running time of the program while ensuring accuracy.     

基于树核度的社交网络影响最大化问题

社交网络中的影响最大化问题是指对于给定的k值,寻找k个在特定传播模型下能够使得传播范围达到最大的节点.此问题在常用的几种传播模型中都是NP-难的.目前虽然已经有很多近似求解的算法,但如何在较低的算法时间复杂度下,保证较大的传播范围仍然是求解该问题的一个挑战.为此,本文提出了一种新颖的基于图的树核度理论的方法来求解社交网络影响最大化问题,并相应地给出了一个多项式时间的算法.所提算法综合考虑了网络的结构特征和传播特征.另外,我们将该算法与传统的随机、度以及贪心算法进行了比较.实验结果表明,所提算法可以较快地找到能够使得传播范围较大的节点集合.     

基于全局贪心的有向传感器网络覆盖算法

针对分布式贪心算法（DGreedy）以传感器节点的剩余能量为优先级,节点处理顺序没有考虑相邻节点间的关系对网络覆盖率的影响,从而影响覆盖率的不足,在此提出了一种新的有向传感器网络覆盖算法。基于全局贪心的原则,以节点一重覆盖区域面积的大小为优先级,优先确定一重覆盖区域面积最大的传感器节点方向,从而保证传感器网络的一重覆盖区域面积更大,重叠覆盖区域较少。对比实验结果表明,该算法能有效提高覆盖率。     

一种结合模拟退火和贪心策略的社团识别算法

为了提高复杂网络社团识别的精度和速度,文中结合模拟退火和贪心策略识别社团结构的优势,提出一种新的社团识别算法。该算法利用贪心策略引导模拟退火搜索最优解过程中单个结点的无规则盲目移动,消除了大量无效移动,在搜索到全局最优解的情况下,将搜索时间大幅缩减。实验表明,SAGA具有强大的搜索能力和较快的模拟退火执行速度,可获得较高的模块度,达到较为准确的社团分割,且具有一定的应用价值。     

基于CVaR子模效益模型的传感器布局优化

针对在不确定情况下如何保证传感器布局取得最优效果问题,本文在初始部署节点时考虑节点存在的不确定性,采用基于CVaR的子模效益模型来最小化这种不确定性对传感器网络布局效果的影响,为了快速有效获得该模型下的最优传感器布局,对传统贪婪算法进行改进,根据模型中存在的参数τ对全局最优解进行有序搜索,同时引入lazy evuluation减少算法的时间复杂度.仿真实验表明,在不确定情况下对传感器进行布局时,CVaR模型可以有效提高网络布局的鲁棒性,与改进的贪婪算法相结合,可以快速获得保证较高信息增益下的布局点集.     

一种基于临近原则的虚拟网络映射算法

在两阶段虚拟网络映射算法的基础上,针对现有的贪婪策略对节点映射存在盲目性的缺点,提出了一种基于临近原则的虚拟网络映射算法。该算法对节点映射阶段做了优化和改进,在节点映射阶段充分考虑其对链路映射带来的影响,使节点映射到相对集中的区域,降低链路映射阶段的求解难度。仿真结果表明,所提出的算法与已有的算法相比,在虚拟网络请求接受率、平均时间收益和收益开销比等方面均有明显提高。     

Influence diffusion model based on affinity of dynamic social network

Recently,influence maximization model is a hot issue in the field of social network influence,while the traditional independent cascade model is generally based on static network with a fixed value of activation probability.DDIC model,which was a dynamic network influence diffusion model with attenuation factor was proposed.It calculated the activation probability between nodes via affinity propagation,and according with dynamic segmentation of social network time slice,calculation of influence on proliferation of next time slice with the current time slice of activation probability performance decay.The experimental results show that the nodes in the DDIC model have more chances to active the neighbor and the average probability of activing of the DDIC model is higher.Further experiments show that influence value via computing with affinity propagation can reflect the process of the spread model more accurately.     

基于信息损失量估计的匿名图构造方法

首先分析了在进化的社会网络序列中,攻击者利用节点度信息,通过识别目标节点的方法对局部社会网络进行攻击过程,分析了利用k匿名方法对该类攻击进行隐私保护时存在的信息损失问题,针对该问题,提出了一种基于信息损失量估计的k匿名图流构造方法,通过子图节点属性泛化、子图内部结构的泛化控制图重构的信息损失,通过禁止子图内部扰动阻止网络攻击。定义匿名过程中由于图重构造成的节点和结构信息损失的估算方法,建立了基于贪婪聚类算法的网络节点的k匿名聚类算法,根据信息损失估计实现匿名分组,在进化的社会网络中以最小信息损失量构造匿名社会网络,在医疗诊断数据集上的实验表明所提方法能够较理想地控制信息损失量。     

Finding Best Matching Community for Common Nodes in Mobile Social Networks

The increase of mobile data users has created traffic congestion in current cellular networks. Due to this, mobile network providers have been facing difficulty in delivering the best services for customers. Since, detecting community in mobile social network is a valuable technique to leverage the downlink traffic congestion by enhancing local communications within the community, it attracts the attention of many researchers. Therefore, developing an algorithm, which detects community, plays a key role in mobile social network. In this paper, first, we proposed external density metrics to detect mobile social network. External density is defined as the ratio of outgoing links to total links of the community. Second, method to find the best group for common node is proposed. Therefore, an external density algorithm, makes a fair partition by grouping common nodes to a community with relatively higher external density. As a result, the overall modularity value of the network has increased. Third, the proposed algorithm is evaluated. Hence, the evaluation results confirm that our proposed approach has demonstrated good performance improvements than traditional methods.

     

基于多维特征分析的社交网络意见领袖挖掘

在社交网络中进行意见领袖的挖掘对信息传播与演化的深度分析、舆情监控和引导具有重要意义,本文综合结构特征、行为特征和用户的情感特征对意见领袖节点挖掘问题进行研究.本文首先对微博真实文本数据进行话题识别得到主题社区,在主题社区中基于用户节点之间的关注关系构建交互网络拓扑.然后分别从结构、行为和情感三个维度对用户的影响力进行度量.最后,分析用户在主题社区中的影响力分布与传播规律,提出意见领袖识别算法MFP(Multi-Feature PageRank).实验表明,该算法可有效地挖掘潜在的意见领袖节点,能够获得较高的支持率.     

一种基于移动用户行为的回路融合社区发现算法

针对现有基于派系的重叠社区发现算法难以对移动社会化网络实施的问题,该文给出一种基于移动用户行为的回路融合社区发现算法。该算法首先通过分析移动用户行为构建移动社会化网络,利用k-EC(k-Elementary Circuit)简单回路发现算法寻找移动社会化网络的k阶回路作为社区核,并按照给定的规则对社区核进行融合,得到初步社区;然后通过计算移动用户行为的相关度将余下的离散节点加入到相应的初步社区,得到最终的社区;最后通过公开数据集和仿真数据集验证了该算法在移动社会化网络社区发现方面的可行性和有效性。     

短句语义向量计算方法

提出了一种基于人工神经网络的短文语义向量放缩算法,结合社交节点自身信息和短文语义,给出社交网络短文语义计算方法和突发话题发现算法。通过文本数值化实现语义距离的计算、比较、节点的分类及社区发现等。通过自行开发的微博采集工具Argus采集的大量新浪微博内容对所提模型和算法进行了验证,最后对未来工作进行了展望。     

无线自组网中最大化网络寿命的速率调整问题

该文针对发送速率可以调整的无线自组网,研究了最大化网络寿命的速率调整问题。将该问题模型化为混合整数非线性规划问题,通过分支限界法可以求出最优解。该文还提出了一个基于贪心策略的分布式最大化网络寿命速率调整算法MNLRAA,其基本思想是根据网络中节点的流量和剩余能量,尽可能为每条链路选择较低传输速率发送分组来节能。模拟实验结果表明,同所有节点使用最高传输速率的经典技术相比,MNLRAA可延长网络寿命20%以上。     

有向传感器网络覆盖增强问题的贪婪迭代算法

 在有向传感器网络中,可以通过调整节点的感知方向来增强目标区域的覆盖率.提出了有向传感器网络覆盖增强问题的一种贪婪迭代算法,在每次迭代中,调整那些使得覆盖率增加最大的节点的感知方向,重复此迭代过程直至通过调整任一节点的感知方向已不能再增加覆盖率.此外,还提出了一种通过计算几何求解该算法中区域计算问题的方法,以提高计算精度和减少计算时间.大量的仿真实验结果表明,该算法的执行时间较短,收敛速度较快,能够获得比现有算法更高的目标区域覆盖率.     

基于社会属性的PSN消息路由算法

在PSN（pocket switched network）中实现数据传输成功率与传输延迟的有效平衡是当前有待解决的问题。提出一种基于社会属性的PSN路由算法——BridgingCom算法,该算法使用带有衰退机制的局部社区识别方法,引入桥接中心度（bridging centrality）作为中继节点的选择依据,将社会网络中节点重要性与移动社会网络的特征相结合,考虑了节点社区关系和节点活跃度对路由算法的影响。实验表明,该算法与现有经典算法相比能够获得较高的传输效率和较低的传输延迟。     

基于拓扑势的重叠社区及社区间结构洞识别——兼论结构洞理论视角下网络的脆弱性

社会网络和复杂网络上的社区识别已经成为当前研究的热点和前沿课题.针对目前社区识别方法不能兼具较低时间复杂度、无须专家知识或先验知识和允许存在重叠节点的不足,提出了基于拓扑势理论的重叠社区识别方法.通过提出的重叠节点社区归属不确定性测度,该方法同时实现了社区间结构洞的识别.实验验证了该方法的有效性.另外,文章在理论证明的基础上提出了影响因子优化算法;论证了结构洞理论视角下网络的脆弱性.     

节点相似度感知的社会化间断连接无线网络结构检测机制

间断连接无线网络中的节点采用"存储、携带、转发"的方式传输消息,网络中的节点具有较强的社会属性。该文提出一种基于相似度和标签传播的网络结构检测机制,节点以运动过程所经历的社区状态为依据,描述自身的中心度,进而,采用带有约束条件的标签传播方法,更新网络结构检测状态,最终完成社区结构划分。仿真结果表明,所提出的检测算法具有较高的准确性,适用于多种复杂网络结构。     

一种面向物联网的无线传感器网络优化部署策略(英文)

This paper proposes an open hierarchical network architecture for the Internet of Things (IoT), which can provide a unified network topology by using heterogeneous Wireless Sensor Networks (WSNs). With this proposed architecture, our research focuses on the optimal deployment strategy of the nodes on the convergence level. We aim at the maximization of the sub-network's lifetime while minimizing the deployment cost. Meanwhile, a novel metric named as the Ratio of Lifetime to Cost (RLC) is proposed to estima...     

Overlapping community detection in complex networks based on multi kernel label propagation

In view of the strong randomness and pre-setting the related threshold of traditional overlapping community detection method based on label propagation,overlapping community detection in complex networks based on multi kernel label propagation (OMKLP) was proposed.Evaluation model of kernel nodes was proposed after analyzing the node's degree and local covering density of nodes and their neighbor nodes.And on this basis,the detection method of local kernel nodes was also presented.Based on local kernel nodes,a new asynchronous label propagation strategy ori-ented to overlapping community was proposed,which can rapidly distinguish inner nodes and outer nodes of communi-ties so as to obtain overlapping community structure.The analysis method of overlapping nodes was proposed to increase the accuracy of detecting overlapping nodes.Without any prior knowledge,only on the basis of the basic network infor-mation (nodes and links),the algorithm can detect the structure of overlapping communities accurately.Therefore,it ef-fectively solved the defect of the traditional label propagation algorithm.The algorithm was tested over benchmark net-works and real-world networks and also compared with some classic algorithms.The experiment results verify the valid-ity and feasibility of OMKLP.     

面向船舶AIS自组织网络的地理位置路由算法

针对船舶自动识别系统（Automatic Identification System,AIS）的自组织网络路由算法设计问题,提出了一种基于优化的贪婪边界无状态路由（Greedy Perimeter Stateless Routing,GPSR）的技术方案。该方案利用AIS时分多址接入协议,为消息报文提供信道接入方法。首先,设计了位置报文的自适应转发策略,使船舶节点根据网络环境自主决策位置报文的转发行为;其次,通过缩小GPSR贪婪转发的选择范围,减少了因节点运动导致的边缘路由失败情况。仿真结果表明,所提方案实现了AIS船舶节点的有效组网。与现有转发策略相比,自适应转发策略降低92.7%的位置报文转发量,并降低了79.6%的时隙冲突率,可有效提高网络资源的利用率。 