This is a survey on graph visualization and navigation techniques, as used in information visualization. Graphs appear in numerous applications such as Web browsing, state-transition diagrams, and data structures. The ability to visualize and to navigate in these potentially large, abstract graphs is often a crucial part of an application. Information visualization has specific requirements, which means that this survey approaches the results of traditional graph drawing from a different perspective.

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Graph visualization and navigation in information visualization: A survey

Graphs, such as social networks, word co-occurrence networks, and communication networks, occur naturally in various real-world applications. Analyzing them yields insight into the structure of society, language, and different patterns of communication. Many approaches have been proposed to perform the analysis. Recently, methods which use the representation of graph nodes in vector space have gained traction from the research community. In this survey, we provide a comprehensive and structured analysis of various graph embedding techniques proposed in the literature. We first introduce the embedding task and its challenges such as scalability, choice of dimensionality, and features to be preserved, and their possible solutions. We then present three categories of approaches based on factorization methods, random walks, and deep learning, with examples of representative algorithms in each category and analysis of their performance on various tasks. We evaluate these state-of-the-art methods on a few common datasets and compare their performance against one another. Our analysis concludes by suggesting some potential applications and future directions. We finally present the open-source Python library we developed, named GEM (Graph Embedding Methods, available at https://github.com/palash1992/GEM ), which provides all presented algorithms within a unified interface to foster and facilitate research on the topic.

Graph embedding techniques, applications, and performance: A survey

L'A. passe en revue les techniques de visualisation utilisees pour representer de facon cartographique la structure de domaine des disciplines scientifiques, et pour soutenir la recherche d'information et la classification. Un bref historique montre que la visualisation des domaines de connaissances s'enracine dans des disciplines telles que la scientometrie, la bibliometrie et l'analyse de citations, ainsi que la visualisation scientifique. L'A. analyse les principales etapes du processus de visualisation des domaines de connaissances : unites d'analyse, mesures, similarites entre unites. Differentes techniques couramment utilisees pour l'analyse et la visualisation des connaissances sont passees en revue : techniques de reduction de la dimensionnalite, analyse par clusters, configuration spatiale, visualisation et conception d'interaction. Differentes approches sont appliquees pour engendrer et comparer diverses representations cartographiques de la recherche sur la visualisation des domaines de connaissances. Ces cartes mettent en valeur les relations entre l'analyse de citations, la bibliometrie, la semantique et la visualisation de l'information. Augmenter l'accessibilite de la visualisation des domaines aupres des non-experts, appliquer la visualisation des domaines de connaissances pour mieux repondre a des questions pragmatiques, favoriser la collaboration et la diffusion des resultats entre chercheurs, developper des algorithmes plus robustes, comptent parmi les directions de recherche les plus prometteuses.

https://cns.iu.edu/docs/publications/2003-borner-arist.pdf

Visualizing knowledge domains

A compound graph is a frequently encountered type of data set. Relations are given between items, and a hierarchy is defined on the items as well. We present a new method for visualizing such compound graphs. Our approach is based on visually bundling the adjacency edges, i.e., non-hierarchical edges, together. We realize this as follows. We assume that the hierarchy is shown via a standard tree visualization method. Next, we bend each adjacency edge, modeled as a B-spline curve, toward the polyline defined by the path via the inclusion edges from one node to another. This hierarchical bundling reduces visual clutter and also visualizes implicit adjacency edges between parent nodes that are the result of explicit adjacency edges between their respective child nodes. Furthermore, hierarchical edge bundling is a generic method which can be used in conjunction with existing tree visualization techniques. We illustrate our technique by providing example visualizations and discuss the results based on an informal evaluation provided by potential users of such visualizations

Hierarchical Edge Bundles: Visualization of Adjacency Relations in Hierarchical Data

Although information visualization (infovis) technologies have proven indispensable tools for making sense of complex data, wide-spread deployment has yet to take hold, as successful infovis applications are often difficult to author and require domain-specific customization. To address these issues, we have created prefuse, a software framework for creating dynamic visualizations of both structured and unstructured data. prefuse provides theoretically-motivated abstractions for the design of a wide range of visualization applications, enabling programmers to string together desired components quickly to create and customize working visualizations. To evaluate prefuse we have built both existing and novel visualizations testing the toolkit's flexibility and performance, and have run usability studies and usage surveys finding that programmers find the toolkit usable and effective.

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prefuse: a toolkit for interactive information visualization

Many applications, from everyday file system browsers to visual programming tools, require the display of network and graph structures. The Graph Visualization Framework (GVF) is an architecture that supports the tasks common to most graph browsers and editors. This article gives a brief overview of the design of the GVF and focuses on the core classes that are used to represent and manipulate graphs. The design of the core classes is justified by the requirements for navigation and visualization.

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An object-oriented design for graph visualization

Two tasks in graph visualization require partitioning: the assignment of visual attributes and divisive clustering. Often, we would like to assign a color or other visual attributes to a node or edge that indicates an associated value. In an application involving divisive clustering, we would like to partition the graph into subsets of graph elements based on metric values in such a way that all subsets are evenly populated. Assuming a uniform distribution of metric values during either partitioning or coloring can have undesired effects such as empty clusters or only one level of emphasis for the entire graph. Probability density functions derived from statistics about a metric can help systems succeed at these tasks.

Density functions for visual attributes and effective partitioning in graph visualization

The problem of graph layout and drawing is fundamental to many approaches to the visualization of relational information structures. As the data set grows, the visualization problem is compounded by the need to reconcile the user’s need for orientation cues with the danger of information overload. Put simply: How can we limit the number of visual elements on the screen so as not to overwhelm the user yet retain enough information that the user is able to navigate and explore the data set confidently? How can we provide orientational cues so that a user can understand the location of the current viewpoint in a large data set? These are problems inherent not only to graph drawing but information visualization in general. We propose a method which extracts the significant features of a directed acyclic graph as the basis for navigation 1.

/pdf/skeletal-images-as-visual-cues-in-graph-visualization-2w5aseebn8.pdf

Skeletal Images as Visual Cues in Graph Visualization

GraphXML is a graph description language in XML that can be used as an interchange format for graph drawing and visualization packages. The generality and rich features of XML make it possible to define an interchange format that not only supports the pure, mathematical description of a graph, but also the needs of information visualization applications that use graph-based data structures.

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M. S Marshall

Papers

Graph visualization and navigation in information visualization: A survey

An object-oriented design for graph visualization

Density functions for visual attributes and effective partitioning in graph visualization

Skeletal Images as Visual Cues in Graph Visualization

GraphXML -- an XML based graph interchange format