具有隐藏变量的贝叶斯网络结构学习

目前,学习具有隐藏变量的贝叶斯网络结构主要采用结合EM算法的打分-搜索方法,其效率和可靠性低.本文针对此问题建立一种新的具有隐藏变量贝叶斯网络结构学习方法.该方法首先依据变量之间基本依赖关系、基本结构和依赖分析思想进行不考虑隐藏变量的贝叶斯网络结构学习,然后利用贝叶斯网络道德图中的Cliques发现隐藏变量的位置,最后基于依赖结构、Gibbs sampling和MDL标准确定隐藏变量的取值、维数和局部结构.该方法能够避免标准Gibbs sampling的指数复杂性问题和现有学习方法存在的主要问题.实验结果表明,该方法能够有效进行具有隐藏变量的贝叶斯网络结构学习.     

Discovering Hidden Analogies in an Online Humanities Database

Voluminous databases contain hiddenknowledge, i.e., literatures that are logicallybut not bibliographically linked. Unlinkedliteratures containing academically interestingcommonalities cannot be retrieved via normalsearching methods. Extracting hidden knowledgefrom humanities databases is especiallyproblematic because the literature, written ineverydayrather than technical language, lacksprecision required for efficient retrieval, andbecause humanities scholars seek new analogiesrather than causes. Drawing upon an efficaciousmethod for discovering previously unknown causesof medical syndromes, and searching inHumanities Index, a periodical index included inWILS, the Wilson Database, an illuminating newhumanities analogy was found by constructing asearch statement in which proper names werecoupled with associated concepts.     

一种增量式的社区发现算法研究

传统社区发现算法基本上属于静态的分析算法,其计算复杂性使其难以适应目前网络结构的频繁变化.为了改善静态算法的这一局限性,通过对Radiechi静态算法进行扩展,提出一种增量式的社区发现算法,并将其应用于MSN Space链接结构分析上.该算法能在网络结构变化频繁时进行增量式计算并保证社区发现的实时性.实验结果表明,该增量式算法在处理网络结构变化时的效率相对传统算法有显著提高,尤其对小规模频繁变化的网络有很强的适应力.     

Community Structure and Information Cascade in Signed Networks

In this paper, we study information cascade in networks with positive and negative edges. The cascade depth is correlated with community structure of signed networks where communities are defined such that positive inter-community and negative intra-community links are minimized. The cascade is initialized from a number of nodes that are selected randomly. Finally, the number of nodes that have participated in the cascade is interpreted as cascade depth; the more the number of such nodes, the more the depth of the cascade. We investigate influence of community structure (i.e., percentage of inter-community positive and intra-community negative links) on the cascade depth. We find significant influence of community structure on cascade depth in both model and real networks. Our results show that the more the intra-community negative links (i.e., the worse the community structure), the more the cascade depth.     

复杂网络强社团结构探测

社团结构是反映复杂网络整体性质的重要特征,本文从强社团结构定义出发提出简单启发式强社团结构探测算法,受启发因素为度-度负相关性和簇-度负相关性.利用该算法对空手道俱乐部成员关系网络和美国大学橄榄球队网络进行社团结构探测,验证了该算法能正确探测出网络的强社团结构.并将划分结果与传统划分进行比较分析,该算法未引入其它量化指标或中间变量,降低了计算复杂度,在采用方法上不同于单纯的分裂或聚合,有效地提高了探测速度,更适合大规模复杂网络社团结构探测.     

Discovering Social Networks from Event Logs

Process mining techniques allow for the discovery of knowledge based on so-called “event logs”, i.e., a log recording the execution of activities in some business process. Many information systems provide such logs, e.g., most WFM, ERP, CRM, SCM, and B2B systems record transactions in a systematic way. Process mining techniques typically focus on performance and control-flow issues. However, event logs typically also log the performer, e.g., the person initiating or completing some activity. This paper focuses on mining social networks using this information. For example, it is possible to build a social network based on the hand-over of work from one performer to the next. By combining concepts from workflow management and social network analysis, it is possible to discover and analyze social networks. This paper defines metrics, presents a tool, and applies these to a real event log within the setting of a large Dutch organization.     

Towards a (Max,+) Control Theory for Public Transportation Networks

We consider the modelling and analysisof public transportation networks, such as railway or subwaynetworks, governed by a timetable. Specifically, we study a (max,+)-linearmodel of a generic transportation network and thereby give aself-contained introduction to the key ideas underlying the (max,+)algebra. We elaborate on the algebraic structure implied by the(max,+)-model to formulate (and solve) the control problem inthe deterministic as well as in the stochastic case. The controlproblem is here whether a train should wait on a connecting trainwhich is delayed. Our objective is then to minimise the propagationof the delay through the network while maintaining as many connectionsas possible. With respect to the deterministic control problem,we present some recent ideas concerning the use of (max,+)-techniquesfor analysing the propagation of delays. Moreover, we show howone can use the (max,+)-algebra to drastically reduce the searchspace for the deterministic control problem. For the stochasticcontrol problem, we consider a parameterised version of the controlproblem, that is, we describe the control policy by means ofa real-valued parameter, say . Finding theoptimal control is then turned into an optimisation problem withrespect to . We address the problem by incorporatingan estimator of the derivative of the expected performance withrespect to into a stochastic approximationalgorithm.     

局部因果关系分析的隐变量发现算法

结构分析的隐变量发现方法难以有效地发现隐变量且可解释性较差。基于因果关系和局部结构的不确定性,提出了一种基于局部因果关系分析的隐变量发现算法（hidden variable discovering algorithm based on local causality analysis,LCAHD）。LCAHD算法给出了因果结构熵的定义,将因果知识和不确定性知识相融合,以因果关系的不确定性程度作为隐变量存在的判定依据,并对这一依据进行了理论上的论证。LCAHD算法首先通过寻找目标变量的马尔科夫毯来提取局部依赖结构,并基于扰动学习获得扰动数据,联合扰动数据和观测数据学习局部依赖结构中的因果关系;然后利用因果结构熵对局部因果结构中因果关系的不确定性进行度量,并利用隐变量和因果关系不确定性之间的相关性判定条件,确定隐变量的存在性。分别针对标准网络和股票网络进行了实验,结果表明,该算法能准确地确定隐变量的位置,具有较好的解释性。     

基于结构分析的信息网络社团趋势预测

社团结构在现实世界各种信息网络中广泛存在。传统信息网络中社团演化的研究均基于单一层次的观察与分析,存在算法不稳定,无法处理社团结构剧烈变化等问题。为解决该问题,提出了基于结构分析的信息网络社团趋势预测方法。该方法基于层次聚类来发现社团层次结构,对相邻网络快照的社团进行跨层次匹配,以解决社团发现算法带来的随机性问题,且使基于结构的社团演化研究成为可能。在两个真实数据集上进行了多层次社团演化挖掘实验,实验结果表明,与最优划分方法相比,新方法在效率和稳定性方面有较大优势。     

展示复杂网络社团结构的社团引力导引的布局算法

由于传统的力导引布局方法大都无法展示复杂网络的社团结构,提出一种可有效展示复杂网络社团结构的布局算法——社团引力导引的布局算法.该算法在力导引布局算法的基础上对每个节点加入社团引力,并引入k-means算法,使同一社团的节点能够向社团的中心位置聚拢.不同于先网络聚类再可视化布局的传统做法,该算法不需要预先对节点分类,可以在布局的同时完成节点聚类.实验中使用模块度指标评估社团结构的强弱程度,结果表明,文中算法可以呈现明显的聚类效果,简单、易于实现,且收敛速度快.     

Discovering Typed Communities in Mobile Social Networks

Mobile social networks,which consist of mobile users who communicate with each other using cell phones,are reflections of people’s interactions in social lives.Discovering typed communities(e.g.,family communities or corporate communities) in mobile social networks is a very promising problem.For example,it can help mobile operators to determine the target users for precision marketing.In this paper we propose discovering typed communities in mobile social networks by utilizing the labels of relationships between users.We use the user logs stored by mobile operators,including communication and user movement records,to collectively label all the relationships in a network,by employing an undirected probabilistic graphical model,i.e.,conditional random fields.Then we use two methods to discover typed communities based on the results of relationship labeling:one is simply retaining or cutting relationships according to their labels,and the other is using sophisticated weighted community detection algorithms.The experimental results show that our proposed framework performs well in terms of the accuracy of typed community detection in mobile social networks.     

具有社团结构的加权网络的病毒传播研究

文中根据Barrat等提出的BBV网络的建模思想,考虑网络社团结构特性,构建一种具有社团结构的加权无标度网络模型,利用SI传染病模型,研究该网络模型中权值增长系数和网络社团强度对病毒传播行为的影响。结果表明,权值增长系数增大时,病毒由感染源社团扩散到其他社团的时间变长,进而抑制了网络中的病毒传播。另外,研究还表明,在加权无标度网络中,较弱的社团强度能抑制病毒的传播,这与无权网络中的结论正好相反。     

基于随机重连的复杂网络社团结构特性分析

复杂网络的社团结构特性本质上是由网络的几阶度分布决定一直是网络科学领域悬而未决的问题之一。在保持网络一阶、二阶和三阶度相关特性不变的情形下,利用随机重连方法和社团检测算法研究了复杂网络的社团结构。通过对四种现实网络进行多次不同阶数的随机重连,发现一阶、二阶重连后,社团结构特性均随着重连次数的增加急剧下降,并在重连次数充分大后趋于稳定值。而保持网络3阶特性不变的随机重连所构造的网络,则可以很高的精度呈现原有网络的社团特性,从而表明网络的社团结构,可以由三阶度相关特性有效地刻画（不需要更高阶）。提供了一种网络构造方法,即利用3阶重连可构造体现现实网络社团结构等拓扑特性的随机网络。     

Discovering Trends in Large Datasets Using Neural Networks

A novel knowledge discovery technique using neural networks is presented. A neural network is trained to learn the correlations and relationships that exist in a dataset. The neural network is then pruned and modified to generalize the correlations and relationships. Finally, the neural network is used as a tool to discover all existing hidden trends in four different types of crimes (murder, rape, robbery, and auto theft) in US cities as well as to predict trends based on existing knowledge inherent in the network.     

Community Similarity Networks

Sensor-enabled smartphones are opening a new frontier in the development of mobile sensing applications. The recognition of human activities and context from sensor data using classification models underpins these emerging applications. However, conventional approaches to training classifiers struggle to cope with the diverse user populations routinely found in large-scale popular mobile applications. Differences between users (e.g., age, sex, behavioral patterns, lifestyle) confuse classifiers, which assume everyone is the same. To address this, we propose Community Similarity Networks (CSN), which incorporates inter-person similarity measurements into the classifier training process. Under CSN, every user has a unique classifier that is tuned to their own characteristics. CSN exploits crowd-sourced sensor data to personalize classifiers with data contributed from other similar users. This process is guided by similarity networks that measure different dimensions of inter-person similarity. Our experiments show CSN outperforms existing approaches to classifier training under the presence of population diversity.     

复杂网络的一种快速局部社团划分算法

为了快速准确地寻找大规模复杂网络的社团结构,文中基于节点度优先的思想,提出了一种新的寻找复杂网络中的局部社团结构的启发式算法.该算法的基本思想是从待求节点出发,基于节点的度有选择性的进行广度优先搜索,从而得到该节点所在的局部社团结构.由于该算法仅需要利用到节点的局部信息,因此时间复杂度很低,达到了线性的时间复杂度.将该算法应用于社会学中经典的Zachary网络,获得了满意的结果.最后,还分析了如何对该算法加以改进以进一步提高准确度.     

The Hidden Difficulties of Watching and Rebuilding Networks

The subtleties of network protection can bedevil even experienced IT staff and security researchers. In this installment of secure systems, we focus on two areas of network defense that are particularly troublesome to manage: network intrusion recovery and ubiquitous network monitoring.     

一个基于多维特征向量的复杂网络社团结构发现算法

作为复杂网络重要特性之一的社团结构在大量现实的大规模复杂系统研究中占有重要的一席地位.论文在研究现有的社团发现算法基础上,提出了一种基于多维特征向量的复杂网络社团结构发现算法,实验证明,该算法能够有效的发现复杂网络中的社团结构,对于进一步进行复杂网络上的信息挖掘具有重要的意义.