HybridVis: An adaptive hybrid-scale visualization of multivariate graphs

Existing network visualizations support hierarchical exploration, which rely on user interactions to create and modify graph hierarchies based on the patterns in the data attributes. It will take a relatively long time for users to identify the impact of different attributes on the cluster structure. To address this problem, this paper proposes a visual analytical approach, called HybridVis, creating an interactive layout to reveal clusters of obvious characteristics on one or more attributes at different scales. HybridVis can help people gain social insight and better understand the roles of attributes within a cluster. First, an approximate optimal graph hierarchy based on an energy model is created, considering both data attributes and relationships among data items. Then a layout algorithm and a level-dependent perceptual view for multi-scale graphs are proposed to show the attribute-driven graph hierarchy. Several views, which interact with each other, are designed in HybridVis, including a graphical view of the relationships among clusters; a cluster tree revealing the cluster scales and the details of attributes on parallel coordinates augmented with histograms and interactions. From the meaningful and globally approximate optimal abstraction, users can navigate a large multivariate graph with an overview+detail to explore and rapidly find the potential correlations between the graph structure and the attributes of data items. Finally, experiments using two real world data sets are performed to demonstrate the effectiveness of our methods.     

一种多变元网络可视化方法

提出一种多变元网络可视化方法MulNetVisBasc,根据节点的多变元属性,使用高级星形坐标法布局网络节点,以边融合及路由技术为基础设计算法,自动有效布局网络边,实现友好的人机交互界面辅助用户进一步对数据进行分析挖掘.实验结果表明,MulNetVisBasc的可视化结果能够在直观揭示数据集多变元分布特性的同时清晰展现其网络关联特性,有助于用户发掘多变元网络数据集中潜在的隐性知识.边布局算法能够有效减少视图中的边交叉数量,且复杂度较低,适用于较大规模数据集,人机交互界面灵活方便.     

语义增强的大规模多元图简化可视分析方法

网络图可视化可以有效展示网络节点之间的连接关系,广泛应用于诸多领域,如社交网络、知识图谱、生物基因网络等.随着网络数据规模的不断增加,如何简化表达大规模网络图结构已成为图可视化领域中的研究热点.经典的网络图简化可视化方法主要包括图采样、边绑定和图聚类等技术,在减少大量点线交叉造成的视觉紊乱的基础上,提高用户对大规模网络结构的探索和认知效率.然而,上述方法主要侧重于网络图中的拓扑结构,却较少考虑和利用多元图节点的多维属性特征,难以有效提取和表达语义信息,从而无法帮助用户理解大规模多元网络的拓扑结构与多维属性之间的内在关联,为大规模多元图的认知和理解带来困难.因此,本文提出一种语义增强的大规模多元图简化可视分析方法,首先在基于模块度的图聚类算法基础上提取出网络图的层次结构;其次通过多维属性信息熵的计算和比较分析,对网络层次结构进行自适应划分,筛选出具有最优属性聚集特征的社团;进而设计交互便捷的多个关联视图来展示社团之间的拓扑结构、层次关系和属性分布,从不同角度帮助用户分析多维属性在社团形成和网络演化中的作用.大量实验结果表明,本文方法能够有效简化大规模多元图的视觉表达,可以快速分析不同应用领域大规模多元图的关联结构与语义构成,具有较强的实用性.     

Using adjacency matrices to lay out larger small-world networks

Many networks exhibit small-world properties. The structure of a small-world network is characterized by short average path lengths and high clustering coefficients. Few graph layout methods capture this structure well which limits their effectiveness and the utility of the visualization itself. Here we present an extension to our novel graphTPP layout method for laying out small-world networks using only their topological properties rather than their node attributes. The Watts–Strogatz model is used to generate a variety of graphs with a small-world network structure. Community detection algorithms are used to generate six different clusterings of the data. These clusterings, the adjacency matrix and edgelist are loaded into graphTPP and, through user interaction combined with linear projections of the adjacency matrix, graphTPP is able to produce a layout which visually separates these clusters. These layouts are compared to the layouts of two force-based techniques. graphTPP is able to clearly separate each of the communities into a spatially distinct area and the edge relationships between the clusters show the strength of their relationship. As a secondary contribution, an edge-grouping algorithm for graphTPP is demonstrated as a means to reduce visual clutter in the layout and reinforce the display of the strength of the relationship between two communities.     

GO: A cluster algorithm for graph visualization

As we are in the big data age, graph data such as user networks in Facebook and Flickr becomes large. How to reduce the visual complexity of a graph layout is a challenging problem. Clustering graphs is regarded as one of effective ways to address this problem. Most of current graph visualization systems, however, directly use existing clustering algorithms that are not originally developed for the visualization purpose. For graph visualization, a clustering algorithm should meet specific requirements such as the sufficient size of clusters, and automatic determination of the number of clusters. After identifying the requirements of clustering graphs for visualization, in this paper we present a new clustering algorithm that is particularly designed for visualization so as to reduce the visual complexity of a layout, together with a strategy for improving the scalability of our algorithm. Experiments have demonstrated that our proposed algorithm is capable of detecting clusters in a way that is required in graph visualization.     

Visualizing Group Structures in Graphs: A Survey

Graph visualizations encode relationships between objects. Abstracting the objects into group structures provides an overview of the data. Groups can be disjoint or overlapping, and might be organized hierarchically. However, the underlying graph still needs to be represented for analyzing the data in more depth. This work surveys research in visualizing group structures as part of graph diagrams. A particular focus is the explicit visual encoding of groups, rather than only using graph layout to indicate groups implicitly. We introduce a taxonomy of visualization techniques structuring the field into four main categories: visual node attributes vary properties of the node representation to encode the grouping, juxtaposed approaches use two separate visualizations, superimposed techniques work with two aligned visual layers, and embedded visualizations tightly integrate group and graph representation. The derived taxonomies for group structure and visualization types are also applied to group visualizations of edges. We survey group‐only, group–node, group–edge and group–network tasks that are described in the literature as use cases of group visualizations. We discuss results from evaluations of existing visualization techniques as well as main areas of application. Finally, we report future challenges based on interviews we conducted with leading researchers of the field.     

GraphDice: A System for Exploring Multivariate Social Networks

Social networks collected by historians or sociologists typically have a large number of actors and edge attributes. Applying social network analysis (SNA) algorithms to these networks produces additional attributes such as degree, centrality, and clustering coefficients. Understanding the effects of this plethora of attributes is one of the main challenges of multivariate SNA. We present the design of GraphDice, a multivariate network visualization system for exploring the attribute space of edges and actors. GraphDice builds upon the ScatterDice system for its main multidimensional navigation paradigm, and extends it with novel mechanisms to support network exploration in general and SNA tasks in particular. Novel mechanisms include visualization of attributes of interval type and projection of numerical edge attributes to node attributes. We show how these extensions to the original ScatterDice system allow to support complex visual analysis tasks on networks with hundreds of actors and up to 30 attributes, while providing a simple and consistent interface for interacting with network data.     

Flip-Book Visualization of Dynamic Graphs

Dynamic graph visualization techniques can be based on animated or static diagrams showing the evolution over time. In this paper, we apply the concept of small multiples to visually illustrate the dynamics of a graph. Node-link, adjacency matrix, and adjacency list visualizations are used as basic visual metaphors for displaying individual graphs of the sequence. For node-link diagrams, we apply edge splatting to improve readability and reduce visual clutter caused by overlaps and link crossings. Additionally, to obtain a more scalable dynamic graph visualization in the time dimension, we integrate an interactive Rapid Serial Visual Presentation (RSVP) feature to rapidly °ip between the sequences of displayed graphs, similar to the concept of flipping a book''s pages. Our visualization tool supports the focus-and-context design principle by providing an overview of a longer time sequence as small multiples in a grid while also showing a graph in focus as a large single representation in a zoomed in and more detailed view. The usefulness of the technique is illustrated in two case studies investigating a dynamic directed call graph and an evolving social network that consists of more than 1,000 undirected graphs.     

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。     

加权社交网络敏感边的差分隐私保护研究

社交网络边权重表示节点属性相似性时,针对边权重能导致节点敏感属性泄露的问题,提出一种利用差分隐私保护模型的扰动策略进行边权重保护。首先根据社交网络构建属性相似图和非属性相似图,同时建立差分隐私保护算法;然后对属性相似图及非属性相似图边权重进行扰动时,设计扰动方案,并按扰动方案对属性相似图及非属性相似图进行扰动。实现了攻击者无法根据扰动后边权重判断节点属性相似性,从而防止节点敏感属性泄漏,而且该方法能够抵御攻击者拥有最大背景知识的攻击。从理论上证明了算法的可行性,并通过实验验证了算法的可行性及有效性。     

Multidimensional Projections for Visual Analysis of Social Networks

Visual analysis of social networks is usually based on graph drawing algorithms and tools.However,social networks are a special kind of graph in the sense that interpretation of displayed relationships is heavily dependent on context.Context,in its turn,is given by attributes associated with graph elements,such as individual nodes,edges,and groups of edges,as well as by the nature of the connections between individuals.In most systems,attributes of individuals and communities are not taken into consideration during graph layout,except to derive weights for force-based placement strategies.This paper proposes a set of novel tools for displaying and exploring social networks based on attribute and connectivity mappings.These properties are employed to layout nodes on the plane via multidimensional projection techniques.For the attribute mapping,we show that node proximity in the layout corresponds to similarity in attribute,leading to easiness in locating similar groups of nodes.The projection based on connectivity yields an initial placement that forgoes force-based or graph analysis algorithm,reaching a meaningful layout in one pass.When a force algorithm is then applied to this initial mapping,the final layout presents better properties than conventional force-based approaches.Numerical evaluations show a number of advantages of pre-mapping points via projections.User evaluation demonstrates that these tools promote ease of manipulation as well as fast identification of concepts and associations which cannot be easily expressed by conventional graph visualization alone.In order to allow better space usage for complex networks,a graph mapping on the surface of a sphere is also implemented.     

Using graph parsing for automatic graph drawing

This paper presents a procedure for automatically drawing directed graphs. Our system, Clan-based Graph Drawing Tool (CG), uses a unique clan-based graph decomposition to determine intrinsic substructures (clans) in the graph and to produce a parse tree. The tree is given attributes that specify the node layout. CG then uses tree properties with the addition of “routing nodes” to route the edges. The objective of the system is to provide, automatically, an aesthetically pleasing visual layout for arbitrary directed graphs. The prototype has shown the strengths of this approach. The innovative strategy of clan-based graph decomposition is the first digraph drawing technique to analyze locality in the graph in two dimensions. The typical approach to drawing digraphs uses a single dimension, level, to arrange the nodes     

On the visualization of social and other scale-free networks

This paper proposes novel methods for visualizing specifically the large power-law graphs that arise in sociology and the sciences. In such cases a large portion of edges can be shown to be less important and removed while preserving component connectedness and other features (e.g. cliques) to more clearly reveal the network’s underlying connection pathways. This simplification approach deterministically filters (instead of clustering) the graph to retain important node and edge semantics, and works both automatically and interactively. The improved graph filtering and layout is combined with a novel computer graphics anisotropic shading of the dense crisscrossing array of edges to yield a full social network and scale-free graph visualization system. Both quantitative analysis and visual results demonstrate the effectiveness of this approach.     

Exploration of networks using overview+detail with constraint-based cooperative layout

A standard approach to large network visualization is to provide an overview of the network and a detailed view of a small component of the graph centred around a focal node. The user explores the network by changing the focal node in the detailed view or by changing the level of detail of a node or cluster. For scalability, fast force-based layout algorithms are used for the overview and the detailed view. However, using the same layout algorithm in both views is problematic since layout for the detailed view has different requirements to that in the overview. Here we present a model in which constrained graph layout algorithms are used for layout in the detailed view. This means the detailed view has high-quality layout including sophisticated edge routing and is customisable by the user who can add placement constraints on the layout. Scalability is still ensured since the slower layout techniques are only applied to the small subgraph shown in the detailed view. The main technical innovations are techniques to ensure that the overview and detailed view remain synchronized, and modifying constrained graph layout algorithms to support smooth, stable layout. The key innovation supporting stability are new dynamic graph layout algorithms that preserve the topology or structure of the network when the user changes the focus node or the level of detail by in situ semantic zooming. We have built a prototype tool and demonstrate its use in two application domains, UML class diagrams and biological networks.      

Hyperbolic Dependency Tree Visualization for Parser Evaluation

We present a novel tool to visualize dependency trees in a hyperbolic layout, and to provide visual support for comparative evaluation of parsing errors. Compared with traditional flat tree visualization, our hyperbolic tree visualization tool can be more convenient for showing long-range dependencies. Our tool integrates the hyperbolic view with a flat view, and support corpus-level error analysis. It offers several features, including statistical analysis of error distributions, visual analysis of individual dependency trees, and an integrated online interface.     

SmallWorlds: Visualizing Social Recommendations

We present SmallWorlds, a visual interactive graph‐based interface that allows users to specify, refine and build item‐preference profiles in a variety of domains. The interface facilitates expressions of taste through simple graph interactions and these preferences are used to compute personalized, fully transparent item recommendations for a target user. Predictions are based on a collaborative analysis of preference data from a user's direct peer group on a social network. We find that in addition to receiving transparent and accurate item recommendations, users also learn a wealth of information about the preferences of their peers through interaction with our visualization. Such information is not easily discoverable in traditional text based interfaces. A detailed analysis of our design choices for visual layout, interaction and prediction techniques is presented. Our evaluations discuss results from a user study in which SmallWorlds was deployed as an interactive recommender system on Facebook.     

Techniques for Edge Stratification of Complex Graph Drawings

We propose an approach that allows a user (e.g., an analyst) to explore a layout produced by any graph drawing algorithm, in order to reduce the visual complexity and clarify its presentation. Our approach is based on stratifying the drawing into layers with desired properties; to this aim, heuristics are presented. The produced layers can be explored and combined by the user to gradually acquire details. We present a user study to test the effectiveness of our approach. Furthermore, we performed an experimental analysis on popular force-directed graph drawing algorithms, in order to evaluate what is the algorithm that produces the smallest number of layers and if there is any correlation between the number of crossings and the number of layers of a graph layout. The proposed approach is useful to explore graph layouts, as confirmed by the presented user study. Furthermore, interesting considerations arise from the experimental evaluation, in particular, our results suggest that the number of layers of a graph layout may represent a reliable measure of its visual complexity. The algorithms presented in this paper can be effectively applied to graph layouts with a few hundreds of edges and vertices. For larger drawings that contain lots of crossings, the time complexity of our algorithms grows quadratically in the number of edges and more efficient techniques need to be devised. The proposed approach takes as input a layout produced by any graph drawing algorithm, therefore it can be applied in a variety of application domains. Several research directions can be explored to extend our framework and to devise new visualization paradigms to effectively present stratified drawings.     

A regularized graph layout framework for dynamic network visualization

Many real-world networks, including social and information networks, are dynamic structures that evolve over time. Such dynamic networks are typically visualized using a sequence of static graph layouts. In addition to providing a visual representation of the network structure at each time step, the sequence should preserve the mental map between layouts of consecutive time steps to allow a human to interpret the temporal evolution of the network. In this paper, we propose a framework for dynamic network visualization in the on-line setting where only present and past graph snapshots are available to create the present layout. The proposed framework creates regularized graph layouts by augmenting the cost function of a static graph layout algorithm with a grouping penalty, which discourages nodes from deviating too far from other nodes belonging to the same group, and a temporal penalty, which discourages large node movements between consecutive time steps. The penalties increase the stability of the layout sequence, thus preserving the mental map. We introduce two dynamic layout algorithms within the proposed framework, namely dynamic multidimensional scaling and dynamic graph Laplacian layout. We apply these algorithms on several data sets to illustrate the importance of both grouping and temporal regularization for producing interpretable visualizations of dynamic networks.     

Visual Inspection of Multivariate Graphs

Most graph visualization techniques focus on the structure of graphs and do not offer support for dealing with node attributes and edge labels. To enable users to detect relations and patterns in terms of data associated with nodes and edges, we present a technique where this data plays a more central role. Nodes and edges are clustered based on associated data. Via direct manipulation users can interactively inspect and query the graph. Questions that can be answered include, “which edge types are activated by specific node attributes?” and, “how and from where can I reach specific types of nodes?” To validate our approach we contrast it with current practice. We also provide several examples where our method was used to study transition graphs that model real‐world systems.     

基于社区探测的层次自适应并行布局算法

针对大规模网络高效布局和递进式结构分析的需求,提出基于社区发现的多层级力导向布局算法.首先,该算法采用Louvain算法对网络进行多层级社团结构划分,根据划分结果压缩网络并进行骨架布局,确定网络整体架构;然后,采用自适应的力导向变体算法对各个社团内部的原始节点并行布局,细化社区内部网络结构,并引入补偿力减少社区划分带来的网络结构信息缺失;最后,设计了初始布局算法、改良了振颤模型来减少布局所需的迭代次数.实验结果表明,与现有网络布局算法相比,该算法能够更清晰、高效地展示大规模社交网络数据,满足大规模复杂网络可视化的需要.