基于领域本体的社会网络信息分析与可视化

社会网络分析与可视化是当前的热门研究领域,但是针对社会网络信息的高效理解与组织的研究成果却十分缺乏。本文提出一种针对社会网络信息的领域本体模型,它对社会网络信息领域的客观存在及其关系进行描述。该模型适合于描述各种社会网络分析与可视化方法,并能针对不同社会网络信息可视化应用进行扩展,克服了传统力导引布局算法在社会网络结构分析与可视化上的不足。其可视化结果能够清晰显示子群分布,表现行动者间的密切程度,显示行动者关键属性分布以及子群内部的角色分布等信息。最后通过恐怖活动信息实例,验证了领域本体模型在社会网络信息分析与显示方面的优越性。     

基于GPU的大尺度网络布局显示

复杂网络的可视化是复杂网络研究中的重要手段.随着Web2.0时代和大数据时代的来临,作为研究对象的复杂网络的规模越来越大,这对复杂网络可视化布局算法的布局效果和运算速度提出了新的挑战.本文针对复杂网络布局的力导引算法,从布局效果和算法效率两方面对该算法进行了改进和实现.布局效果方面,利用复杂网络中的关节点,对网络数据进行抽象合并,从而实现分层次的网络布局显示.算法效率方面,针对压缩后的网络采用具有强大浮点运算能力的GPU进行计算,对力导引算法需要斥力计算、引力计算和坐标更新三个部分均实现了基于GPU的并行计算,大大提高了计算效率.     

基于社团划分的网络聚类布局算法

复杂网络日益受到广大专家和学者们的关注,对其进行可视化展示可以帮助用户发现复杂网络表征的复杂系统中隐藏的知识信息,对计算机科学、社会学、生物学等领域具有重要的意义。力导引布局算法是复杂网络可视化领域的主流算法,它用节点连接图的形式对复杂网络进行抽象表示,布局遵循一定的美学标准如节点的均匀分布、边长尽量一致等,这在一定程度上阻碍了对复杂网络的社团结构的展示。针对以上问题,本文提出引入基于度中心性的社团斥力与引力对力导引算法进行改进,以对复杂网络进行聚类布局。实验结果表明,本文算法可有效地展示复杂网络的社团结构,同时又能保留社团之间边缘节点的信息。     

基于改进力导引图布局的层级视觉抽象方法

随着图规模的增大,传统的力导引布局算法会出现节点重叠、边交叉等视觉混乱问题,为此提出一种基于改进力导引布局的可扩展的可视化层级抽象方法.首先结合FR算法与LinLog算法的优点对力导引算法进行改进,生成一个具有明显聚类结构、能够体现图结构信息的初步布局;然后基于布局结果,采用自下而上的层次聚类方法生成图的层级结构,同时定义了体现抽象层级的参数来决定不同层级下的聚类显示,允许用户在多个层级观察数据结构特征;最后采用几何距离、拓扑结构和拓扑结构加中介中心性3种不同的度量进行视觉抽象,并对抽象结果进行比较分析.为了说明文中方法的有效性,分别对信息可视化文章间的文献引用数据、2004年美国总统竞选的政治博客数据,以及IEEE Visualization会议文章的作者合作数据这3个数据实例进行实验,结果表明,使用该方法并结合移动、缩放、选择等可视化交互技术,能有效地帮助用户分析、探索和理解数据隐藏的信息.     

面向多层网络可视化的多力导引节点自动布局算法

为展示多层网络层内社区结构和实现层间结构对比,基于传统力导引布局模型提出一种面向多层网络可视化的多力导引节点自动布局算法.首先在综合考虑节点所受层内引力、斥力和层间节点-副本间引力的基础上改进传统引力-斥力模型,构建多力导引模型;然后引入模拟退火思想为多力导引布局添加温度控制参数,控制节点移动速度;最后借鉴布局美学标准中的"总边长最短"原则,采用节点与副本的水平偏移与最优边长的平均相对长度和社区内部连边与最优边长的平均相对长度这2个指标评价文中算法的布局效果.通过对仿真数据和真实数据的实验结果表明,与基于层叠切片模型的独立布局算法相比,该算法能同时兼顾多层网络社区结构展示和跨层节点及其副本识别的效果,满足多层网络可视化对节点自动布局的要求.     

虚拟社群结构特征可视化呈现的社会网络布局算法研究

近年来,随着虚拟社区的发展,社会网络可视化软件逐渐走向普通社区成员的面前。然而现今社会网络可视化领域所采用的布局算法普遍与社会网络分析法相脱离,无法呈现社群结构特征。因此,提出凝聚子群布局算法与核心位置布局算法,它们分别以凝聚子群分析结果和成员整体中心度为布局依据,呈现社群子群和成员位置两种社群结构特征,并且依据实际数据给出布局效果。     

展示复杂网络社团结构的社团引力导引的布局算法EI北大核心CSCD

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.     

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.     

多域网络逻辑拓扑布局算法研究

网络空间态势可视化已经成为网络空间态势信息综合与分析的重要环节,在态势可视化中网络逻辑拓扑的布局生成技术也得到了越来越多的关注。本文针对网络逻辑拓扑结构复杂、多域的特点,结合层级布局、力导引布局等图布局算法,提出了一种针对网络空间态势逻辑拓扑数据的布局生成算法。经过实验验证,该算法能够完成多域网络逻辑拓扑的自动布局,并保证布局的清晰、合理。     

社会网络信息的本体论建模与可视化

本体论是共享概念模型的明确的形式化规范说明,基于本体论对社会网络信息建模能够提供客观存在的本质认识,发挥本体论在信息表示与组织上的优势.提出一种针对社会网络信息的领域本体模型,将社会网络信息领域的客观存在抽象为3个主要本体:行动者、关系网络和群组,能够方便地定义各种社会网络分析与可视化方法,为社会网络信息可视化应用提供支持.着眼于社会网络结构分析中的需求,在上述模型框架下提出了基于规则对等组的群组分析方法和基于群组、角色分析的力导引布点算法.最后结合恐怖活动信息可视化实例,阐述了文中提出的领域模型与可视化方法的应用.     

Visualizing the Evolution of Community Structures in Dynamic Social Networks

Social network analysis is the study of patterns of interaction between social entities. The field is attracting increasing attention from diverse disciplines including sociology, epidemiology, and behavioral ecology. An important sociological phenomenon that draws the attention of analysts is the emergence of communities, which tend to form, evolve, and dissolve gradually over a period of time. Understanding this evolution is crucial to sociologists and domain scientists, and often leads to a better appreciation of the social system under study. Therefore, it is imperative that social network visualization tools support this task. While graph‐based representations are well suited for investigating structural properties of networks at a single point in time, they appear to be significantly less useful when used to analyze gradual structural changes over a period of time. In this paper, we present an interactive visualization methodology for dynamic social networks. Our technique focuses on revealing the community structure implied by the evolving interaction patterns between individuals. We apply our visualization to analyze the community structure in the US House of Representatives. We also report on a user study conducted with the participation of behavioral ecologists working with social network datasets that depict interactions between wild animals. Findings from the user study confirm that the visualization was helpful in providing answers to sociological questions as well as eliciting new observations on the social organization of the population under study.     

城市道路交通数据可视分析综述

目的 交通是困扰现代大都市的世界性难题.近年来,可视分析技术在分析和利用交通大数据中扮演了越来越重要的角色,成为一项重要的智能交通技术.本文将全面回顾自信息可视化和可视分析兴起以来城市交通数据可视分析领域的研究现状.方法 从道路交通流量分析和其他交通问题分析两个方面,按照数据的类型及问题的分类探讨交通领域的可视化技术和可视分析系统,简单回顾近年来出现的研究新趋势.结果 早期研究注重对道路流量的可视化展示方案,主要方法有箭头图、马赛克图和轨迹墙等.随着可视分析手段的丰富,对城市道路交通流量的分析层次上升到交通事件层面,但是交通事件的定义仅局限于交通拥堵.应用可视分析的其他交通问题领域包括公共交通、交通事故和人群出行行为等.近年出现了挖掘和利用交通轨迹或交通事件的社会属性或称环境上下文信息的研究新趋势.结论 从对交通流量的可视化到交通事件的可视分析,从面向道路交通状况到与交通相关的其他社会性问题,从单纯反映路况的交通数据到富含社会性语义的多源数据,从传统的PC端可视化和交互范式到新型的可视化展示介质,交通数据可视化领域的研究在深度和广度上都得到大大拓展,未来该领域的研究趋势也体现于其中.     

群组协作社交网络关系的分析及可视化

社交网络的研究应用领域广泛,大多数的研究主要关注于社交网络结构中节点和链接的变化,研究角度较为单一.而群组协作关系在社交网络中较为普遍,为更好地探索群组协作过程中群组级别网络结构的演变,首先将组级任务添加到用于图可视化的任务分类中,针对协作关系网络的特性,设置一个适用于群组协作关系分析的任务分类法,根据此任务分类法设置探究群组协作需完成的任务;然后设计相关可视化视图,以更直观的方式展示群组之间以及群组内部协作关系的特征以及随时间推进的演变模式;最后在学术协作文献数据集上进行具体案例分析,通过用户评估实验,采用李克特量表对调查问卷进行分析,验证了设计可视化视图的有效性和实用性,可为社交网络关系分析人员提供群组协作的可视化分析方法,更便于对群组协作网络关系的进一步探究和应用.     

Social Mirrors as Social Signals: Transforming Audio into Graphics

A social mirror is a specific type of visualization for group interaction. Three examples of social mirrors emphasize different motivations for visualizing vocal conversation: the power of visualization to influence conversation in real time, the addition of anonymous input into group visualization, and idea formation over time. Social visualizations are visualizations about people, for people. A social mirror is a type of social visualization with three particular qualities. First, it's a third-person visualization in which people can see information about themselves within information about other people. In a sense, we can perceive "us and them." Every participant sees the same visualization. Second, subtle perceivable changes of any participant appear in the visualization as they occur in near real time. This can include capturing and visualizing subtle social behaviors such as laughing and coughing. Because of this near real-time feature, people can quickly alter their behavior-and hence the visualization-so that others perceive them as they'd like to be perceived in this public mirror. Third, social mirrors allow for exploring group patterns and behavior through real-time experimentation, replay, annotation, and reconfiguration.     

Correlation in mobile call networks from structure perspective

Researchers have done considerable work on the structure of social network recently, but mostly neglected the correlation between two connected nodes. In this paper, our primary goal is to acquire users’ structural properties in mobile call networks. We take a novel perspective—structure correlation between two connected users perspective to study the structural properties. To investigate the structural properties in static and dynamic mobile call networks, we define some metrics which are based on the clique size vectors of mobile call users. By exploring several realworldmobile call networks, which contain hundreds of thousands of mobile call users respectively, we find that people tend to communicate with the one who has a similar structure in static mobile call networks. Moreover, It is found that the connected people have similar structural changes on the whole in dynamicmobile call networks, and the structures of some two connected persons both have growing or shrinking trends. We use a visualization toolkit to give a view of the growing or shrinking scenarios temporally.     

Correlation in mobile call networks from structure perspective

Researchers have done considerable work on the structure of social network recently, but mostly neglected the correlation between two connected nodes. In this paper, our primary goal is to acquire users’ structural properties in mobile call networks. We take a novel perspective-structure correlation between two connected users perspective to study the structural properties. To investigate the structural properties in static and dynamic mobile call networks, we define some metrics which are based on the clique size vectors of mobile call users. By exploring several real-world mobile call networks, which contain hundreds of thousands of mobile call users respectively, we find that people tend to communicate with the one who has a similar structure in static mobile call networks. Moreover, It is found that the connected people have similar structural changes on the whole in dynamicmobile call networks, and the structures of some two connected persons both have growing or shrinking trends. We use a visualization toolkit to give a view of the growing or shrinking scenarios temporally.     

地理社会网络数据可视化分析研究综述

随着移动互联网与社会网络的深度融合,基于位置服务(Location Based Service,LBS)的社交媒体应用更加流行,成为地理社会网络(Geo-Social Networks,GSN)的研究重点。基于位置信息的社会网络(Location Based Social Network,LBSN)由于具有时空特性,其海量数据可视化不同于传统信息可视化,必须结合其地理信息特征进行表达。该文以GSN中抽取出的海量时空数据为分析对象,从LBSN时空数据抽取、海量时空数据可视化等方面进行综述,对地理社会网络时空数据交互可视化分析技术开展研究,以期能够实现比较方便、快速、直接地从地理社会网络的海量数据中提取出有用、可靠、可知识化的综合信息,并通过信息可视化方式进行直观表达、展示与分析。     

In situ exploration of large dynamic networks

The analysis of large dynamic networks poses a challenge in many fields, ranging from large bot-nets to social networks. As dynamic networks exhibit different characteristics, e.g., being of sparse or dense structure, or having a continuous or discrete time line, a variety of visualization techniques have been specifically designed to handle these different aspects of network structure and time. This wide range of existing techniques is well justified, as rarely a single visualization is suitable to cover the entire visual analysis. Instead, visual representations are often switched in the course of the exploration of dynamic graphs as the focus of analysis shifts between the temporal and the structural aspects of the data. To support such a switching in a seamless and intuitive manner, we introduce the concept of in situ visualization--a novel strategy that tightly integrates existing visualization techniques for dynamic networks. It does so by allowing the user to interactively select in a base visualization a region for which a different visualization technique is then applied and embedded in the selection made. This permits to change the way a locally selected group of data items, such as nodes or time points, are shown--right in the place where they are positioned, thus supporting the user's overall mental map. Using this approach, a user can switch seamlessly between different visual representations to adapt a region of a base visualization to the specifics of the data within it or to the current analysis focus. This paper presents and discusses the in situ visualization strategy and its implications for dynamic graph visualization. Furthermore, it illustrates its usefulness by employing it for the visual exploration of dynamic networks from two different fields: model versioning and wireless mesh networks.     

Multi‐scale navigation of large trace data: A survey

Dynamic analysis through execution traces is frequently used to analyze the runtime behavior of software systems. However, tracing long running executions generates voluminous data, which are complicated to analyze and manage. Extracting interesting performance or correctness characteristics out of large traces of data from several processes and threads is a challenging task. Trace abstraction and visualization are potential solutions to alleviate this challenge. Several efforts have been made over the years in many subfields of computer science for trace data collection, maintenance, analysis, and visualization. Many analyses start with an inspection of an overview of the trace, before digging deeper and studying more focused and detailed data. These techniques are common and well supported in geographical information systems, automatically adjusting the level of details depending on the scale. However, most trace visualization tools operate at a single level of representation, which are not adequate to support multilevel analysis. Sophisticated techniques and heuristics are needed to address this problem. Multi‐scale (multilevel) visualization with support for zoom and focus operations is an effective way to enable this kind of analysis. Considerable research and several surveys are proposed in the literature in the field of trace visualization. However, multi‐scale visualization has yet received little attention. In this paper, we provide a survey and methodological structure for categorizing tools and techniques aiming at multi‐scale abstraction and visualization of execution trace data and discuss the requirements and challenges faced to be able to meet evolving user demands.