Graph visualization and navigation in information visualization: A survey
01 Jan 2000-IEEE Transactions on Visualization and Computer Graphics (IEEE Educational Activities Department)-Vol. 6, Iss: 1, pp 24-43
TL;DR: This is a survey on graph visualization and navigation techniques, as used in information visualization, which approaches the results of traditional graph drawing from a different perspective.
Abstract: 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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TL;DR: A comprehensive and structured analysis of various graph embedding techniques proposed in the literature, and the open-source Python library, 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.
Abstract: 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.
1,553 citations
Cites background from "Graph visualization and navigation ..."
...[47] generalize this and view it from an information visualization perspective....
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TL;DR: L'A.
Abstract: 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.
1,304 citations
TL;DR: This paper presents a new method for visualizing compound graphs based on visually bundling the adjacency edges, i.e., non-hierarchical edges, together and discusses the results based on an informal evaluation provided by potential users of such visualizations.
Abstract: 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
1,057 citations
Cites background or methods from "Graph visualization and navigation ..."
...Current methods for aggregating edges are mentioned as well....
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...F...
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...…software system, e.g., source code divided into directories, files, and classes, and the relations between these elements, for instance, dependency relations; • Social networks comprised of individuals at the lowest level of the hierarchy and groups of individuals at higher levels of the hierarchy....
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...There is a large class of data sets that contain both hierarchical components, i.e., parent-child relations between data items, as well as non-hierarchical components representing additional relations between data items....
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TL;DR: Network embedding assigns nodes in a network to low-dimensional representations and effectively preserves the network structure as discussed by the authors, and a significant amount of progress has been made toward this emerging network analysis paradigm.
Abstract: Network embedding assigns nodes in a network to low-dimensional representations and effectively preserves the network structure. Recently, a significant amount of progresses have been made toward this emerging network analysis paradigm. In this survey, we focus on categorizing and then reviewing the current development on network embedding methods, and point out its future research directions. We first summarize the motivation of network embedding. We discuss the classical graph embedding algorithms and their relationship with network embedding. Afterwards and primarily, we provide a comprehensive overview of a large number of network embedding methods in a systematic manner, covering the structure- and property-preserving network embedding methods, the network embedding methods with side information, and the advanced information preserving network embedding methods. Moreover, several evaluation approaches for network embedding and some useful online resources, including the network data sets and softwares, are reviewed, too. Finally, we discuss the framework of exploiting these network embedding methods to build an effective system and point out some potential future directions.
929 citations
TL;DR: A method to visualize comorbidity networks is proposed and it is argued that this approach generates realistic hypotheses about pathways to comor bidity, overlapping symptoms, and diagnostic boundaries, that are not naturally accommodated by latent variable models.
Abstract: The pivotal problem of comorbidity research lies in the psychometric foundation it rests on, that is, latent variable theory, in which a mental disorder is viewed as a latent variable that causes a constellation of symptoms. From this perspective, comorbidity is a (bi)directional relationship between multiple latent variables. We argue that such a latent variable perspective encounters serious problems in the study of comorbidity, and offer a radically different conceptualization in terms of a network approach, where comorbidity is hypothesized to arise from direct relations between symptoms of multiple disorders. We propose a method to visualize comorbidity networks and, based on an empirical network for major depression and generalized anxiety, we argue that this approach generates realistic hypotheses about pathways to comorbidity, overlapping symptoms, and diagnostic boundaries, that are not naturally accommodated by latent variable models: Some pathways to comorbidity through the symptom space are more likely than others; those pathways generally have the same direction (i.e., from symptoms of one disorder to symptoms of the other); overlapping symptoms play an important role in comorbidity; and boundaries between diagnostic categories are necessarily fuzzy.
918 citations
Cites methods from "Graph visualization and navigation ..."
...Many of the efforts in complex systems theory have been aimed at providing adequate visual representations of networks, and this has yielded a number of algorithms to optimally represent networks (De Berg et al. 2008; DiBattista et al. 1994; Herman 2000), as well as freely available software to visualize them; most notable, in this respect, are the programs Cytoscape (Shannon et al....
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...…of networks, and this has yielded a number of algorithms to optimally represent networks (De Berg et al. 2008; DiBattista et al. 1994; Herman 2000), as well as freely available software to visualize them; most notable, in this respect, are the programs Cytoscape (Shannon et al. 2003…...
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References
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Book•
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TL;DR: This fourth edition of the highly successful Cluster Analysis represents a thorough revision of the third edition and covers new and developing areas such as classification likelihood and neural networks for clustering.
Abstract: Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organising multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques are applicable in a wide range of areas such as medicine, psychology and market research. This fourth edition of the highly successful Cluster Analysis represents a thorough revision of the third edition and covers new and developing areas such as classification likelihood and neural networks for clustering. Real life examples are used throughout to demonstrate the application of the theory, and figures are used extensively to illustrate graphical techniques. The book is comprehensive yet relatively non-mathematical, focusing on the practical aspects of cluster analysis.
9,857 citations
TL;DR: A modification of the spring‐embedder model of Eades for drawing undirected graphs with straight edges is presented, developed in analogy to forces in natural systems, for a simple, elegant, conceptually‐intuitive, and efficient algorithm.
Abstract: SUMMARY We present a modification of the spring-embedder model of Eades [ Congresses Numerantium, 42, 149–160, (1984)] for drawing undirected graphs with straight edges. Our heuristic strives for uniform edge lengths, and we develop it in analogy to forces in natural systems, for a simple, elegant, conceptuallyintuitive, and efficient algorithm.
5,242 citations
"Graph visualization and navigation ..." refers methods in this paper
...Spring layouts have been used successfully to produce wellbalanced layout for graphs....
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Book•
04 Feb 2000TL;DR: The art and science of why the authors see objects the way they do are explored, and the author presents the key principles at work for a wide range of applications--resulting in visualization of improved clarity, utility, and persuasiveness.
Abstract: Most designers know that yellow text presented against a blue background reads clearly and easily, but how many can explain why, and what really are the best ways to help others and ourselves clearly see key patterns in a bunch of data? When we use software, access a website, or view business or scientific graphics, our understanding is greatly enhanced or impeded by the way the information is presented.
This book explores the art and science of why we see objects the way we do. Based on the science of perception and vision, the author presents the key principles at work for a wide range of applications--resulting in visualization of improved clarity, utility, and persuasiveness. The book offers practical guidelines that can be applied by anyone: interaction designers, graphic designers of all kinds (including web designers), data miners, and financial analysts.
Complete update of the recognized source in industry, research, and academic for applicable guidance on information visualizing.
Includes the latest research and state of the art information on multimedia presentation.
More than 160 explicit design guidelines based on vision science.
A new final chapter that explains the process of visual thinking and how visualizations help us to think about problems.
Packed with over 400 informative full color illustrations, which are key to understanding of the subject.
Table of Contents
Chapter 1. Foundations for an Applied Science of Data Visualization
Chapter 2. The Environment, Optics, Resolution, and the Display
Chapter 3. Lightness, Brightness, Contrast and Constancy
Chapter 4. Color
Chapter 5. Visual Salience and Finding Information
Chapter 6. Static and Moving Patterns
Chapter 7. Space Perception
Chapter 8. Visual Objects and Data Objects
Chapter 9. Images, Narrative, and Gestures for Explanation
Chapter 10. Interacting with Visualizations
Chapter 11. Visual Thinking Processes
3,837 citations
"Graph visualization and navigation ..." refers background in this paper
...Ware's book [123] is also an interesting source of information for this topic....
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...Ware's new book [123] may become an important source of information in this area....
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...The reader may find further examples in the overview by Young [128] or in the new book by Ware [123]....
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...Perceptual and navigational conflicts are caused by the discrepancy of using 2D screens and 2D input devices to interact with a 3D world, combined with missing motion and stereo cues (see the overview of Ware and Franck [122] for how important these cues are)....
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2,703 citations
"Graph visualization and navigation ..." refers methods in this paper
...Since then, his method was revisited and improved[28],[47],[49],[75]....
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01 Apr 1986
TL;DR: This paper explores fisheye views presenting, in turn, naturalistic studies, a general formalism, a specific instantiation, a resulting computer program, example displays and an evaluation.
Abstract: In many contexts, humans often represent their own “neighborhood” in great detail, yet only major landmarks further away. This suggests that such views (“fisheye views”) might be useful for the computer display of large information structures like programs, data bases, online text, etc. This paper explores fisheye views presenting, in turn, naturalistic studies, a general formalism, a specific instantiation, a resulting computer program, example displays and an evaluation.
2,164 citations
"Graph visualization and navigation ..." refers background in this paper
...logical focus+context view described in an often–cited paper of Furnas[50]....
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...A simple approach is to allow the user to add an application–specific “weight” to the nodes, which is then combined with the structural metric[50],[61],[62]....
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...The Degree of Interest (DOI) function of Furnas[50] is also an example of a metric that is composed of two other metrics (in this case, a metric based on distance and a level of detail)....
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...We have identified the concept of node metrics in several places in the literature[11],[50],[61],[78]....
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