基于三元闭包的节点相似性链路预测算法

链路预测作为复杂网络分析的基本方法被应用到很多领域,完全基于拓扑结构信息的复杂网络链路预测仍然是一个具有挑战性的问题。三元闭包作为网络中最小局部结构,具有结构平衡和稳定的特征。提出了一种基于三元闭包的节点相似性链路预测算法,通过计算出每个节点在网络中所占三元闭包的权重,并将该权重用于节点相似性指标中,提出了3个相似性指标TWCN、TWAA、TWRA和具有调节参数的3个相似性指标TWCN*、TWAA*、TWRA*。在10个不同的网络数据集上的实验结果表明,所提算法能够提高链路预测的精度。不仅如此,通过分析实验结果,发现在社交网络中拥有较多三元闭包的节点具有局部稳定性,不倾向于建立更多的新链接;相反,拥有较少三元闭包的节点具有局部不稳定性,倾向于建立更多的新链接。这种现象也符合社会学中有关弱关系产生链接的现象。     

基于低秩和稀疏矩阵分解的多源融合链接预测算法

近年来,链接预测成为社会网络和其他复杂网络链接挖掘中的热门研究领域.在链接预测问题中,经常会存在用来提高预测效果的附加数据信息源,这些数据可以用于预测网络中的链接是否存在.在所有的数据源中,最主要的数据源在链接预测中起到最重要的作用.因此,设计具备健壮性的算法用于充分利用所有数据源的信息来进行链接预测十分重要,算法还需要平衡主数据源和附加数据源的关系,使得链接预测能够获得更好的效果.同时,传统基于拓扑结构计算的无监督算法大多数通过计算网络中节点间的评分值来解决预测链接存在可能性的问题,这些方法能够获得有效的结果.在链接预测方法中,最关键的一步是构建准确的输入矩阵数据.由于许多真实世界数据集存在噪声,这导致降低了大多数链接预测模型的效果.提出了一种新的链接预测方法,通过多个数据源的融合,兼顾地利用了主数据源的信息和其他附加数据源的信息.接着,主数据源和其他附加数据源被用于构建一个低噪声且更准确的矩阵,而新的矩阵被用于作为传统无监督拓扑链接预测算法的输入.根据在多个真实世界数据上的测试结果,在多源数据集上进行对比实验,提出的基于低秩和稀疏矩阵分解的多源融合链接预测算法相对于基准算法能够获得更好的效果.     

大数据下的基于主题模型的社交网络链接预测

计算机技术和网络的发展使得数据呈爆炸式的涌现,社交媒体不断融入到人们的生活中,社会网络分析已成为研究的热点。随着大数据时代的到来,对社交网络链接算法研究产生巨大影响,原有的基于网络结构的预测方法已经渐渐不适应现状。因此,提出了一种基于主题模型的社交网络链接预测方法。首先以微博社交网络为数据源,将实验网络分为测试集和训练集;其次利用主题模型得到用户的主题特征,结合命名实体集和用户联系特征集合得到用户的兴趣特征相似性度量,加上网络结构相似性从而得到用户节点相似度,进而对社交网络链接进行预测;最终使用链接预测最常用的评价体系AUC来评价链接预测方法的效果。通过实验验证,该方法的预测准确率更高。     

基于节点相似性的链接预测

链接预测是图数据挖掘中的一个重要问题。它是通过已知的网络结构等信息预测和估计尚未链接的两个节点存在链接的可能性。目前大部分基于节点相似性的链接预测算法只考虑共同部居节点的个体特征,针对目前预测。算法对共同部居节点间相互关系的考虑不足,提出了一种新算法:节点引力指数算法。该算法在保持低时间复杂度的同时,提高了预测的准确率。通过多个现实网络实验证实了算法的预测效果。     

基于矩阵分解的属性网络嵌入和社区发现算法

属性网络不但包含节点之间复杂的拓扑结构,还包含拥有丰富属性信息的节点,其可以比传统网络更有效地建模现代信息系统,属性网络的社区划分对于分析复杂系统的层次结构、控制信息在网络中的传播和预测网络用户的群体行为等方面具有重要的研究价值.为了更好地利用拓扑结构信息和属性信息进行社区发现,提出了一种基于矩阵分解的属性网络嵌入和社区发现算法(CDEMF).首先提出基于矩阵分解的属性网络嵌入方法,基于网络局部链接信息计算相邻节点的相似性,将其与属性接近度联合建模,通过矩阵分解的分布式算法得到每个节点对应的低维嵌入向量,即把网络节点映射为低维向量表示的数据点集合.接着提出基于曲率和模块度的社区划分方法,自动确定数据点集合中蕴含的社区数量,并通过对数据点集合聚类完成属性网络社区划分.在真实网络数据集上,将CDEMF方法与其他8种知名算法进行比较,实验结果表明CDEMF具有良好的性能.     

非对称信息在链接预测中的应用

针对基于节点相似性的链接关系预测中因未考虑非对称信息导致预测准确度降低的问题,提出一种新的增加非对称信息的节点相似性度量方法。首先,分析了基于共同邻居（CN）的相似性度量算法的缺陷在于仅考虑CN的数量而未考虑各自节点的所有邻居的数量;然后,将节点之间的相似性度量定义为共同节点与所有邻居节点的比值,融合节点间对称相似信息和非对称相似信息,对节点间的相似程度进行深入细致的刻画;最后,将该方法应用到复杂网络中进行链接关系的预测。在真实数据集上的实验结果表明,与目前多种基于共邻的相似性度量方法——CN、AA、资源分配（RA）相比,所提方法提升了节点相似性度量的准确性,并且可以提高复杂网络中链接关系预测的准确度。     

多维社会网络中链路预测方法研究

多维社会网络中链路预测是指通过已知的网络节点以及多维社会网络结构等信息预测多维社会网络中尚未产生连边的两个节点之间产生链接的可能性.在研究有关链路预测的相关技术后,基于节点之间的相似性,提出了一种多维社会网络中的链路预测算法.算法中考虑时间因素和权重因素对于链路预测的影响,同时将多维网络中的相关技术应用到链路预测的过程...     

基于复杂网络节点重要性的链路预测算法

提升链路预测精度是复杂网络研究的基础问题之一,现有的基于节点相似的链路预测指标没有充分利用网络节点的重要性,即节点在网络中的影响力。针对以上问题提出基于节点重要性的链路预测算法。该算法在基于局部相似性链路预测算法的共同邻居（CN）、Adamic-Adar（AA）、Resource Allocation（RA）相似性指标的基础上,充分利用了节点度中心性、接近中心性及介数中心性的信息,提出考虑节点重要性的CN、AA、RA链路预测相似性指标。在4个真实数据集上进行仿真实验,以AUC值作为链路预测精度评价指标,实验结果表明,改进的算法在4个数据集上的链路预测精度均高于共同邻居等对比算法,能够对复杂网络结构产生更精确的分析预测。     

利用矩阵补全优化模型进行动态网络链接预测

动态链接预测是网络数据挖掘领域的一个重要课题,主要原理是根据以往的网络结构预测未来的网络结构状态。目前,静态链接预测已得到充分研究,但对动态链接预测的研究却比较稀少。根据网络链接的结构特点,将矩阵补全方法引入动态链接预测问题中,进一步受核矩阵分解的启发,建立了核矩阵补全模型,将数据映射到高维空间中,使得链接中的非线性关系转化为线性关系,从而使得模型能够处理更复杂的网络结构。通过在三个公开网络数据集上进行实验,验证了矩阵补全优化方法和核方法在动态链接预测中的有效性和准确性。     

加权好友推荐模型链路预测算法

链路预测是复杂网络的一个重要研究方向。基于节点结构相似性进行链路预测是目前常用的方法。真实网络中存在大量的局部群落结构,针对不同的网络结构构建算法是链路预测的核心问题。利用社交网络好友推荐策略,中介人倾向于将自己更熟悉的人介绍给目标用户,提出了一种节点相似性度量指标。该指标结合局部特征描述并有效区分了用户节点之间影响力的不同,更适用于一类特定的局部群落结构。依据该指标提出的加权好友推荐模型链路预测算法在12个数据集上的实验结果表明,该算法在AUC和Precision两个评价标准上具有明显优势。     

Only connect: teaching, technology and telesis

Abstract This paper describes an approach to the design of interactive multimedia materials being developed in a European Community project. The developmental process is seen as a dialogue between technologists and teachers. This dialogue is often problematic because of the differences in training, experience and culture between them. Conditions needed for fruitful dialogue are described and the generic model for learning design used in the project is explained.     

European Community policy and the market

Abstract This paper starts with some reflections on the policy considerations and priorities which are shaping European Commission (EC) research programmes. Then it attempts to position the current projects which seek to capitalise on information and communications technologies for learning in relation to these priorities and the apparent realities of the marketplace. It concludes that while there are grounds to be optimistic about the contribution EC programmes can make to the efficiency and standard of education and training, they are still too technology driven.     

一种自适应子融合集成多分类器方法

融合集成方法已经广泛应用在模式识别领域,然而一些基分类器实时性能稳定性较差,导致多分类器融合性能差,针对上述问题本文提出了一种新的基于多分类器的子融合集成分类器系统。该方法考虑在度量层融合层次之上通过对各类基多分类器进行动态选择,票数最多的类别作为融合系统中对特征向量识别的类别,构成一种新的自适应子融合集成分类器方法。实验表明,该方法比传统的分类器以及分类融合方法识别准确率明显更高,具有更好的鲁棒性。     

Avoiding semantic and temporal gaps in developing software intensive systems

Development of software intensive systems (systems) in practice involves a series of self-contained phases for the lifecycle of a system. Semantic and temporal gaps, which occur among phases and among developer disciplines within and across phases, hinder the ongoing development of a system because of the interdependencies among phases and among disciplines. Such gaps are magnified among systems that are developed at different times by different development teams, which may limit reuse of artifacts of systems development and interoperability among the systems. This article discusses such gaps and a systems development process for avoiding them.     

Designing economic np control charts: A programmed simulation approach

This paper presents control charts models and the necessary simulation software for the location of economic values of the control parameters. The simulation program is written in FORTRAN, requires only 10K of main storage, and can run on most mini and micro computers. Two models are presented - one describes the process when it is operating at full capacity and the other when the process is operating under capacity. The models allow the product quality to deteriorate to a further level before an existing out-of-control state is detected, and they can also be used in situations where no prior knowledge exists of the out-of-control causes and the resulting proportion defectives.     

The development of robot art

Going through a few examples of robot artists who are recognized worldwide, we try to analyze the deepest meaning of what is called “robot art” and the related art field definition. We also try to highlight its well-marked borders, such as kinetic sculptures, kinetic art, cyber art, and cyberpunk. A brief excursion into the importance of the context, the message, and its semiotics is also provided, case by case, together with a few hints on the history of this discipline in the light of an artistic perspective. Therefore, the aim of this article is to try to summarize the main characteristics that might classify robot art as a unique and innovative discipline, and to track down some of the principles by which a robotic artifact can or cannot be considered an art piece in terms of social, cultural, and strictly artistic interest. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Explanation and prediction: an architecture for default and abductive reasoning

Although there are many arguments that logic is an appropriate tool for artificial intelligence, there has been a perceived problem with the monotonicity of classical logic. This paper elaborates on the idea that reasoning should be viewed as theory formation where logic tells us the consequences of our assumptions. The two activities of predicting what is expected to be true and explaining observations are considered in a simple theory formation framework. Properties of each activity are discussed, along with a number of proposals as to what should be predicted or accepted as reasonable explanations. An architecture is proposed to combine explanation and prediction into one coherent framework. Algorithms used to implement the system as well as examples from a running implementation are given.     

Three Process Perspectives: Organizations, Teams, and People

This paper provides the author's personal views and perspectives on software process improvement. Starting with his first work on technology assessment in IBM over 20 years ago, Watts Humphrey describes the process improvement work he has been directly involved in. This includes the development of the early process assessment methods, the original design of the CMM, and the introduction of the Personal Software Process (PSP)^SM and Team Software Process (TSP){^SM}. In addition to describing the original motivation for this work, the author also reviews many of the problems he and his associates encountered and why they solved them the way they did. He also comments on the outstanding issues and likely directions for future work. Finally, this work has built on the experiences and contributions of many people. Mr. Humphrey only describes work that he was personally involved in and he names many of the key contributors. However, so many people have been involved in this work that a full list of the important participants would be impractical.     

基于复小波噪声方差显著修正的SAR图像去噪

提出了一种基于复小波域统计建模与噪声方差估计显著性修正相结合的合成孔径雷达(Synthetic Aperture Radar,SAR)图像斑点噪声滤波方法。该方法首先通过对数变换将乘性噪声模型转化为加性噪声模型,然后对变换后的图像进行双树复小波变换(Dualtree Complex Wavelet Transform,DCWT),并对复数小波系数的统计分布进行建模。在此先验分布的基础上,通过运用贝叶斯估计方法从含噪系数中恢复原始系数,达到滤除噪声的目的。实验结果表明该方法在去除噪声的同时保留了图像的细节信息,取得了很好的降噪效果。     

How do children do mathematics with LOGO?

Abstract  This paper considers some results of a study designed to investigate the kinds of mathematical activity undertaken by children (aged between 8 and 11) as they learned to program in LOGO. A model of learning modes is proposed, which attempts to describe the ways in which children used and acquired understanding of the programming/mathematical concepts involved. The remainder of the paper is concerned with discussing the validity and limitations of the model, and its implications for further research and curriculum development.