基于最大熵模型的微博传播网络中的链路预测

微博是基于用户关注关系建立的具有媒体特性的实时信息分享社交平台.微博上的信息扩散具有快速性、爆发性和时效性.理解信息的传播机理,预测信息转发行为,对研究微博上舆论的形成、产品的推广等具有重要意义.本文通过解析微博转发记录来研究影响信息转发的因素或特征,把微博信息转发预测问题抽象为链路预测问题,并提出基于最大熵模型的链路预测算法.实例验证的结果表明:1)基于最大熵模型的算法在运行时间上具有明显的优势;2)在预测结果方面,最大熵模型比同类其他算法表现优异;3)当训练集大小和特征数量变化时,基于最大熵模型的预测结果表现稳定.该方法在预测链路时避免了特征之间相互独立的约束,准确率优于其他同类方法,对解决复杂网络中其他类型的预测问题具有借鉴意义.     

复杂网络链路可预测性:基于特征谱视角

近年来链路预测的理论和实证研究发展迅速,大部分工作关注于提出更精确的预测算法.事实上,链路预测的前提是网络的结构本身能够被预测,这种"可被预测的程度"可以看作是网络自身的基本属性.本文拟从特征谱的视角去解释网络的链路可预测性,并刻画网络的拓扑结构信息,通过对网络特征谱进行分析,构造了复杂网络链路可预测性评价指标.通过该指标计算和分析不同网络的链路可预测性,能够在选择算法前获取目标网络能够被预测的难易程度,解决到底是网络本身难以预测还是预测算法不合适的问题,为复杂网络与链路预测算法的选择和匹配问题提供帮助.     

基于ZigBee的低延时PDA网络系统设计与实现

传统的自助信息服务系统（PDA）终端平台设备只具有简单的读写功能,网络互动能力较差,在文件传输中出现较大的数据流时会造成明显的网络延时;设计并实现了基于ZigBee网络的终端PDA平台系统设计;在平台设计中,引入了主从CPU轮流方式,分担设备交互中产生的海量数据流,在外围电路中,设计了专用的SD存储卡,便于数据移植;在原有的平台无线传输模块中,进行了链路升级,采用分级路由机制,有效降低数据包的路由延时。并以教学PDA为例进行开发与试验,系统测试表明：文章设计的自助信息服务系统PDA网络下行发送数据在10 ms可以完成,误码率为0001%,上行发送数据成功率高达997%;证明该系统具有稳定的网络性能与实用性;具有较高的网络稳定性和很强的实用价值。     

无线传感器网络多跳路由协议研究

目的：无线传感器网络发展迅速,但传感器的高能耗问题成为制约其发展的主要瓶颈,高效节能的路由协议设计成为研究热点。方法：针对目前无线传感器网络常用的LEACH路由协议存在的簇首能耗过分集中、簇首分布不均衡问题,提出了改进的路由协议EEACRA,在总结、分析LEACH路由协议现有问题的基础上,给出了EEACRA路由协议的簇首选取门限值、簇首位置调整算法和基于能量代价最小的簇间多跳路由算法的实现方法,同时给出了具体的实现EEACRA协议的工作流程和关键算法。在MATLAB环境下对LEACH路由协议和EEACRA路由协议进行了仿真,对比了不同能耗降低措施对网络能耗降低的贡献。结果：仿真结果表明EEACRA路由协议的网络稳定期较LEACH路由协议有较大的改善。结论：证明了改进的路由协议EEACRA可以有效地提高网络的稳定期。     

能量均衡的AD Hoc网络路由协议研究

传统的AD Hoc路由协议由于节点的能量不均衡,导致网络耗能较大,减少了网络生命周期。从能量均衡角度提出了一种能量优化的Ad Hoc网络路由协议（EOARP协议）,通过引入公共经济学中洛伦茨指数法来衡量网络的能耗均衡,结合节点通信繁忙程度、节点剩余能量等因素建立代价函数,选择剩余能量相对较高,通信繁忙程度较轻的路由链路,从而达到均衡网络的目的。仿真结果表明,EOARP协议在数据传输性能、能量均衡、以及网络节点剩余能量等方面指标均有较大改善,有效地延长了网络生命周期     

一种在光网络实现流量工程的快捷的路由算法

提出了一种在光网络中实现流量工程的快捷的有带宽保证的负载均衡动态路由算法。该算法通过提出期望负载率的概念和新定义的链路关键度函数以及链路当前可用带宽确定链路动态成本，并依据该动态成本运用最短路径优先算法为到达的LSP请求建立动态成本优化路径。仿真实验表明，与其他算法相比，该算法在降低LSP建立请求服务拒绝率、均衡网络负载以及链路失效后重路由等方面有更好的性能。     

车载自组织网络中基于蚁群算法的簇路由协议

由于车辆的高速移动及拓扑动态变化,构建稳定的传输路径是车载自组织网络VANETs(Vehicular ad hoc Networks)应用的关键。而簇技术建立稳定传输路径的有效技术之一。为此,提出基于蚁群算法的簇路由ACCR(Ant Colony algorithm based cluster routing)协议。蚁群系统是典型的启发性算法,能够解决簇划分问题。据此,ACCR协议利用蚁群算法选择簇头,提高簇的稳定性和数据传输性能。仿真结果表明,与ACO-CR协议相比,提出的ACCR协议的簇头寿命提高了近20%,数据传输率提高了近45%。     

基于最大权重值纠缠分发的量子无线多跳网络路由协议研究

针对量子节点间建立链路所消耗纠缠资源的差异,使用权重数值来量化现实环境对各个量子节点的影响,并根据权值确定纠缠粒子的分发。在此基础上提出一种针对量子无线多跳网络的路由协议,该协议以纠缠利用率作为路由度量条件,通过基于最大权重值的纠缠粒子分发方式和并行纠缠交换建立量子信道,实现量子态的传输。仿真分析表明,使用该路由协议可以精确找出网络中符合度量条件的量子链路,同时该路由协议中使用并行纠缠交换的交换方式降低了量子态传输的时延,在节点数为15时,并行交换比串行交换的平均时延降低了50%,而且随着链路中节点数的增加,两者的时延差异将会越来越大。为方便分析各仿真节点的权重值均设置为1至15的随机整数,通过对5节点的链路进行多次仿真发现使用基于最大权重值纠缠粒子分发方式能够传送的量子态的平均个数为14,基于串行的分发方式能够传送的量子态的平均个数为7。     

基于OPNET的星地光网络性能测试仿真平台研究

为了测试星地光网络的性能,设计了一种基于OPNET的星地光网络性能测试仿真平台。介绍了平台的总体技术架构,探讨了星间链路建立的条件。采用最大接入仰角与最长服务时间加权的方式,完成星地链路的卫星接入服务;按照切换呼叫优先的策略完成业务传输中的卫星切换服务,确保星地链路不间断的通信能力。根据最小链路代价和首次命中原则,实现星地光网络的路由与波长的动态分配。最后,利用平台的OPNET软件测试了三种星座下的星地光网络性能,测试结果表明：在LEO、MEO和GEO星座下,星地光网络的网络阻塞率分别为10%、40%和54%,平均网络时延分别为0.1s、0.07s和0.054s。测试结果对星地光网络的工程应用具有一定的指导意义。     

一种有效提高无标度网络负载容量的管理策略

现有研究表明明显的社团结构会显著降低网络的传输性能. 本文基于网络邻接矩阵的特征谱定义了链路对网络社团特性的贡献度, 提出一种通过逻辑关闭或删除对网络社团特性贡献度大的链路以提高网络传输性能的拓扑管理策略, 即社团弱化控制策略(CWCS 策略). 在具有社团结构的无标度网络上分别进行了基于全局最短路径路由和局部路由的仿真实验, 并与关闭连接度大的节点之间链路的HDF 策略进行了比较. 仿真实验结果显示, 在全局最短路径路由策略下, CWCS策略能更有效地提高网络负载容量, 并且网络的平均传输时间增加的幅度变小. 在局部路由策略下, 当调控参数0<α<2, 对网络负载容量的提升优于HDF策略. 关键词： 复杂网络 社团特性 负载容量 拓扑管理     

Link prediction based on a semi-local similarity index

Missing link prediction provides significant instruction for both analysis of network structure and mining of unknown links in incomplete networks. Recently, many algorithms have been proposed based on various node-similarity measures. Among these measures, the common neighbour index, the resource allocation index, and the local path index, stemming from different source, have been proved to have relatively high accuracy and low computational effort. In this paper, we propose a similarity index by combining the resource allocation index and the local path index. Simulation results on six unweighted networks show that the accuracy of the proposed index is higher than that of the local path one. Based on the same idea of the present index, we develop its corresponding weighted version and test it on several weighted networks. It is found that, except for the USAir network, the weighted variant also performs better than both the weighted resource allocation index and the weighted local path index. Due to the improved accuracy and the still low computational complexity, the indices may be useful for link prediction.     

Entropy-based link prediction in weighted networks

Information entropy has been proved to be an effective tool to quantify the structural importance of complex networks.In a previous work [Xu et al. Physica A, 456 294(2016)], we measure the contribution of a path in link prediction with information entropy. In this paper, we further quantify the contribution of a path with both path entropy and path weight,and propose a weighted prediction index based on the contributions of paths, namely weighted path entropy(WPE), to improve the prediction accuracy in weighted networks. Empirical experiments on six weighted real-world networks show that WPE achieves higher prediction accuracy than three other typical weighted indices.     

Deep Link-Prediction Based on the Local Structure of Bipartite Networks

Link prediction based on bipartite networks can not only mine hidden relationships between different types of nodes, but also reveal the inherent law of network evolution. Existing bipartite network link prediction is mainly based on the global structure that cannot analyze the role of the local structure in link prediction. To tackle this problem, this paper proposes a deep link-prediction (DLP) method by leveraging the local structure of bipartite networks. The method first extracts the local structure between target nodes and observes structural information between nodes from a local perspective. Then, representation learning of the local structure is performed on the basis of the graph neural network to extract latent features between target nodes. Lastly, a deep-link prediction model is trained on the basis of latent features between target nodes to achieve link prediction. Experimental results on five datasets showed that DLP achieved significant improvement over existing state-of-the-art link prediction methods. In addition, this paper analyzes the relationship between local structure and link prediction, confirming the effectiveness of a local structure in link prediction.     

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.     

Complex Embedding with Type Constraints for Link Prediction

Large-scale knowledge graphs not only store entities and relations but also provide ontology-based information about them. Type constraints that exist in this information are of great importance for link prediction. In this paper, we proposed a novel complex embedding method, CHolE, in which complex circular correlation was introduced to extend the classic real-valued compositional representation HolE to complex domains, and type constraints were integrated into complex representational embeddings for improving link prediction. The proposed model consisted of two functional components, the type constraint model and the relation learning model, to form type constraints such as modulus constraints and acquire the relatedness between entities accurately by capturing rich interactions in the modulus and phase angles of complex embeddings. Experimental results on benchmark datasets showed that CHolE outperformed previous state-of-the-art methods, and the impartment of type constraints improved its performance on link prediction effectively.     

Prediction of sgRNA Off-Target Activity in CRISPR/Cas9 Gene Editing Using Graph Convolution Network

CRISPR/Cas9 is a powerful genome-editing technology that has been widely applied in targeted gene repair and gene expression regulation. One of the main challenges for the CRISPR/Cas9 system is the occurrence of unexpected cleavage at some sites (off-targets) and predicting them is necessary due to its relevance in gene editing research. Very few deep learning models have been developed so far to predict the off-target propensity of single guide RNA (sgRNA) at specific DNA fragments by using artificial feature extract operations and machine learning techniques; however, this is a convoluted process that is difficult to understand and implement for researchers. In this research work, we introduce a novel graph-based approach to predict off-target efficacy of sgRNA in the CRISPR/Cas9 system that is easy to understand and replicate for researchers. This is achieved by creating a graph with sequences as nodes and by using a link prediction method to predict the presence of links between sgRNA and off-target inducing target DNA sequences. Features for the sequences are extracted from within the sequences. We used HEK293 and K562 t datasets in our experiments. GCN predicted the off-target gene knockouts (using link prediction) by predicting the links between sgRNA and off-target sequences with an auROC value of 0.987.     

星-地量子密钥分发链路持续时间分析与仿真

链路持续时间是影响卫星量子密钥分发量子比特率的重要参数.文章分析了影响星-地单光子和纠缠光子量子密钥分发链路持续时间的若干因素,并且进行了数值仿真研究.结果表明,随着轨道高度的增加或者地面最小通信仰角的减小,单光子和纠缠光子链路的持续时间有较大的改善;单光子链路时地面站同卫星星下点轨迹间的距离和纠缠光子链路时两地面站之间的距离也均是影响链路持续时间的重要参数;纠缠光子链路持续时间的影响因素较单光子链路更为复杂,其最终决定于卫星星下点轨迹同两地面站之间的位置关系.     

TLP-CCC: Temporal Link Prediction Based on Collective Community and Centrality Feature Fusion

In the domain of network science, the future link between nodes is a significant problem in social network analysis. Recently, temporal network link prediction has attracted many researchers due to its valuable real-world applications. However, the methods based on network structure similarity are generally limited to static networks, and the methods based on deep neural networks often have high computational costs. This paper fully mines the network structure information and time-domain attenuation information, and proposes a novel temporal link prediction method. Firstly, the network collective influence (CI) method is used to calculate the weights of nodes and edges. Then, the graph is divided into several community subgraphs by removing the weak link. Moreover, the biased random walk method is proposed, and the embedded representation vector is obtained by the modified Skip-gram model. Finally, this paper proposes a novel temporal link prediction method named TLP-CCC, which integrates collective influence, the community walk features, and the centrality features. Experimental results on nine real dynamic network data sets show that the proposed method performs better for area under curve (AUC) evaluation compared with the classical link prediction methods.