Dynamic graph exploration by interactively linked node-link diagrams and matrix visualizations

The visualization of dynamic graphs is a challenging task owing to the various properties of the underlying relational data and the additional time-varying property.For sparse and small graphs,the most efficient approach to such visualization is node-link diagrams,whereas for dense graphs with attached data,adjacency matrices might be the better choice.Because graphs can contain both properties,being globally sparse and locally dense,a combination of several visual metaphors as well as static and dynamic visualizations is beneficial.In this paper,a visually and algorithmically scalable approach that provides views and perspectives on graphs as interactively linked node-link and adjacency matrix visualizations is described.As the novelty of this technique,insights such as clusters or anomalies from one or several combined views can be used to influence the layout or reordering of the other views.Moreover,the importance of nodes and node groups can be detected,computed,and visualized by considering several layout and reordering properties in combination as well as different edge properties for the same set of nodes.As an additional feature set,an automatic identification of groups,clusters,and outliers is provided over time,and based on the visual outcome of the node-link and matrix visualizations,the repertoire of the supported layout and matrix reordering techniques is extended,and more interaction techniques are provided when considering the dynamics of the graph data.Finally,a small user experiment was conducted to investigate the usability of the proposed approach.The usefulness of the proposed tool is illustrated by applying it to a graph dataset,such as e co-authorships,co-citations,and a Comprehensible Perl Archive Network distribution.

Visual Graph动画在“电气控制与PLC技术”教学中的应用

Visual Graph是近年来出现的新型图形交互和动画展示技术,具有直观性好、交互性强等优点。文章从“电气控制与PLC技术”课程的实际教学效果出发,讨论了课程动画辅助教学的需求分析和规划,并展示了典型动画设计案例,供广大教学人员参考。

The Importance of Visual Identity Graphic Design in Cities

The identity system depends on tangible architecture, landscape roads and so on, but it doesn＇ t equal to the supplement of its subject but an indispensable constituent part. As the finishing touch of the whole urban space, by means of design of elements such as symbols, graphs, characters and colors as well as integration of various factors, the identity system reflects the urban specialty, conveys urban features related to history, geography, culture and emotion. The identity system provides suitable space environment to people through application of concrete symbols, graphs, characters and colors, in addition, it also mirrors that the designing culture of a city equals to its design concept with the combination of the historical culture of this city. A modem city shall be provided with such function, that is, it shall possess a whole set of well-designed and scientific-planned indication mechanism to help those wandering pedestrians to indicate direction and find their way successfully. It shall not only serve the mass, realize inter-coordination among people, identity facility and space environment to make people＇ s living environment more comfortable and convenient, but also it constitutes the vital cartier to spread local culture and model urban brand.

VSAN:A new visualization method for super-large-scale academic networks

As a carrier of knowledge,papers have been a popular choice since ancient times for documenting everything from major historical events to breakthroughs in science and technology.With the booming development of science and technology,the number of papers has been growing exponentially.Just like the fact that Internet of Things(IoT)allows the world to be connected in a flatter way,how will the network formed by massive academic papers look like?Most existing visualization methods can only handle up to hundreds of thousands of node size,which is much smaller than that of academic networks which are usually composed of millions or even more nodes.In this paper,we are thus motivated to break this scale limit and design a new visualization method particularly for super-large-scale academic networks(VSAN).Nodes can represent papers or authors while the edges means the relation(e.g.,citation,coauthorship)between them.In order to comprehensively improve the visualization effect,three levels of optimization are taken into account in the whole design of VSAN in a progressive manner,i.e.,bearing scale,loading speed,and effect of layout details.Our main contributions are two folded:(1)We design an equivalent segmentation layout method that goes beyond the limit encountered by state-of-the-arts,thus ensuring the possibility of visually revealing the correlations of larger-scale academic entities.(2)We further propose a hierarchical slice loading approach that enables users to observe the visualized graphs of the academic network at both macroscopic and microscopic levels,with the ability to quickly zoom between different levels.In addition,we propose a“jumping between nebula graphs”method that connects the static pages of many academic graphs and helps users to form a more systematic and comprehensive understanding of various academic networks.Applying our methods to three academic paper citation datasets in the AceMap database confirms the visualization scalability of VSAN in the sense that it can visualize academic networks with more than 4 million nodes.The super-large-scale visualization not only allows a galaxy-like scholarly picture unfolding that were never discovered previously,but also returns some interesting observations that may drive extra attention from scientists.

NetV.js:A web-based library for high-efficiency visualization of large-scale graphs and networks

Graph visualization plays an important role in several fields,such as social media networks,protein-protein interaction networks,and traffic networks.A number of visualization design tools and programming toolkits have been widely used in graph-related applications.However,a key challenge remains in the high-efficiency visualization of large-scale graph data.In this study,we present NetV.js,an open-source and WebGL-based JavaScript library that supports the fast visualization of large-scale graph data(up to 50 thousand nodes and 1 million edges)at an interactive frame rate with a commodity computer.Experimental results demonstrate that our library outperforms existing toolkits(Sigma.js,D3.js,Cytoscape.js,and Stardust.js)in terms of performance.

G6:A web-based library for graph visualization

Authoring graph visualization poses great challenges to developers due to its high requirements on both domain knowledge and development skills.Although existing libraries and tools reduce the difficulty of generating graph visualization,there are still many challenges.We work closely with developers and formulate several design goals,then design and implement G6,a web-based library for graph visualization.It combines template-based configuration for high usability and flexible customization for high expressiveness.To enhance development efficiency,G6 proposes a range of optimizations,including state management and interaction modes.We demonstrate its capabilities through an extensive gallery,a quantitative performance evaluation,and an expert interview.G6 was first released in 2017 and has been iterated for 317 versions.It has served as a web-based library for thousands of applications and received 8312 stars on GitHub.

Edge Bundling in Information Visualization

The edge, which can encode relational data in graphs and multidimensional data in parallel coordinates plots, is an important visual primitive for encoding data in information visualization research. However, when data become very large, visualizations often suffer from visual clutter as thousands of edges can easily overwhelm the display and obscure underlying patterns. Many edge-bundling techniques have been proposed to reduce visual clutter in visualizations. In this survey, we briefly introduce the visual-clutter problem in visualizations. Thereafter, we review the cost-based, geometry-based, and image-based edge-bundling methods for graphs, parallel coordinates, and flow maps. We then describe the various visualization applications that use edge-bundling techniques and discuss the evaluation studies concerning the effectiveness of edge-bundling methods. An edge-bundling taxonomy is proposed at the end of this survey.

Simple Algorithms for Network Visualization:A Tutorial

The graph drawing and information visualization communities have developed many sophisticated techniques for visualizing network data, often involving complicated algorithms that are difficult for the uninitiated to learn. This article is intended for beginners who are interested in programming their own network visualizations, or for those curious about some of the basic mechanics of graph visualization. Four easy-to-program network layout techniques are discussed, with details given for implementing each one： force-directed node-link diagrams, arc diagrams, adjacency matrices, and circular layouts. A Java applet demonstrating these layouts, with open source code, is available at http：//www.michaelmcguffin.com/research/simpleNetVis/. The end of this article also briefly surveys research topics in graph visualization, pointing readers to references for further reading.

Visualizing large-scale high-dimensional data via hierarchical embedding of KNN graphs

Visualizing intrinsic structures of high-dimensional data is an essential task in data analysis.Over the past decades,a large number of methods have been proposed.Among all solutions,one promising way for enabling effective visual exploration is to construct a k-nearest neighbor(KNN)graph and visualize the graph in a low-dimensional space.Yet,state-of-the-art methods such as the LargeVis still suffer from two main problems when applied to large-scale data:(1)they may produce unappealing visualizations due to the non-convexity of the cost function;(2)visualizing the KNN graph is still time-consuming.In this work,we propose a novel visualization algorithm that leverages a multilevel representation to achieve a high-quality graph layout and employs a cluster-based approximation scheme to accelerate the KNN graph layout.Experiments on various large-scale datasets indicate that our approach achieves a speedup by a factor of five for KNN graph visualization compared to LargeVis and yields aesthetically pleasing visualization results.

SideKnot:Edge Bundling for Uncovering Relation Patterns in Graphs

The node-link diagram is an intuitive way to depict a graph and present relationships between entities. Addressing the visual clutter induced by edge crossing and node-edge overlapping is a challenging task as the size of graph outgrows the visualization space. Many edge bundling methods are proposed to disclose high-level edge patterns. Though previous methods can successfully reveal the skeleton graph structure, the relation patterns at the individual node level can be overlooked. In addition, most edge bundling algorithms are computationally complex, which prevents them from scaling up for extremely large graphs. In this article, we extend SideKnot, an efficient edge bundling method to cluster and knot edges at the node side. Our proposed method is light, runs faster than most existing algorithms, and can reveal the relation patterns at the individual node level. Our results show that SideKnot can disclose a node＇s standing in the graph as well as the directional connection patterns to its peers.

Graph modelling for tracking the COVID-19 pandemic spread

The modelling is widely used in determining the best strategies for the mitigation of the impact of infectious diseases.Currently,the modelling of a complex system such as the spread of COVID-19 infection is among the topical issues.The aim of this article is graphbased modelling of the COVID-19 infection spread.The article investigates the studies related to the modelling of COVID-19 pandemic and analyses the factors affecting the spread of the disease and its main characteristics.We propose a conceptual model of COVID-19 epidemic by considering the social distance,the duration of contact with an infected person and their location-based demographic characteristics.Based on the hypothetical scenario of the spread of the virus,a graph model of the process are developed starting from the first confirmed infection case to human-to-human transmission of the virus and visualized by considering the epidemiological characteristics of COVID-19.The application of graph for the pandemic modelling allows for considering multiple factors affecting the epidemiological process and conducting numerical experiments.The advantage of this approach is justified with the fact that it enables the reverse analysis the spread as a result of the dynamic record of detected cases of the infection in the model.This approach allows for to determining undetected cases of infection based on the social distance and duration of contact and eliminating the uncertainty significantly.Note that social,economic,demographic factors,the population density,mental values and etc.affect the increase in number of cases of infection and hence,the research was not able to consider all factors.In future research will analyze multiple factors impacting the number of infections and their use in the models will be considered.