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. Index Terms---Information visualization, graph visualization, graph drawing, navigation, focus+context, fish--eye, clustering. 1

Never before in history has data been generated at such high volumes as it is today. Exploring and analyzing the vast volumes of data is becoming increasingly difficult. Information visualization and visual data mining can help to deal with the flood of information. The advantage of visual data exploration is that the user is directly involved in the data mining process. There are a large number of information visualization techniques which have been developed over the last decade to support the exploration of large data sets. In this paper, we propose a classification of information visualization and visual data mining techniques which is based on the data type to be visualized, the visualization technique, and the interaction and distortion technique. We exemplify the classification using a few examples, most of them referring to techniques and systems presented in this special section.

AbstractÐVisualization techniques are ofincreasing importance in exploring and analyzing large amounts ofmultidimensional information. One important class of visualization techniques which is particularly interesting for visualizing very large multidimensional data sets is the class ofthe pixel-oriented techniques. The basic idea ofpixel-oriented visualization techniques is to represent as many data objects as possible on the screen at the same time by mapping each data value to a pixel ofthe screen and arranging the pixels adequately. A number of different pixel-oriented visualization techniques have been proposed in recent years and it has been shown that the techniques are useful for visual data exploration in a number of different application contexts. In this paper, we discuss a number ofissues which are ofhigh importance in developing pixel-oriented visualization techniques. The major goal ofthis article is to provide a formal basis of pixel-oriented visualization techniques and show that the design decisions in developing them can be seen as solutions ofwell-defined optimization problems. This is true for the mapping ofthe data values to colors, the arrangement ofpixels inside the subwindows, the shape ofthe subwindows, and the ordering ofthe dimension subwindows. The paper also discusses the design issues of special variants of pixel-oriented techniques for visualizing large spatial data sets. The optimization functions for the mentioned design decisions are important for the effectiveness of the resulting visualizations. We show this by evaluating the optimization functions and comparing it the results to the visualization obtained in a number of different application. Index TermsÐInformation visualization, visualizing large data sets, visualizing multidimensional and multivariate data, visual data exploration, visual data mining. 1

Techniques for improving the imageability of an existing data visualisation are described. The aim is to make the visualisation more easily explored, navigated and remembered. Starting from a relatively sparse landscape– like representation of a set of objects, we selectively add to the visualisation static features such as clusters, and dynamic features such as view–specific sampling of object detail. Information on past usage is used in this process, making manifest an aspect of interaction which is often neglected. Issues arising from the use of such features in a shared virtual environment are discussed.

An important goal of visualization technology is to support the exploration and analysis of very large amounts of data. In this paper, we describe a set of pixeloriented visualization techniques which use each pixel of the display to visualize one data value and therefore allow the visualization of the largest amount of data possible. Most of the techniques have been specifically designed for visualizing and querying large databases. The techniques may be divided into query-independent techniques which directly visualize the data (or a certain portion of it) and query-dependent techniques which visualize the data in the context of a specific query. Examples for the class of query-independent techniques are the screen-filling curve and recursive pattern techniques. The screen-filling curve techniques are based on the well-known Morton and Peano-Hilbert curve algorithms, and the recursive pattern technique is based on a generic recursive scheme which generalizes a wide range of pixel-ori...

Focus+context visualization techniques aim to give users integrated visual access to both details and overview of a data set. This paper gives a systematic account of such visualization techniques. We introduce the notion that there are different levels of information visualization, with focus+context being a second-level visualization, and illustrate this with examples. We then provide a formal framework for describing and constructing focus+context visualization and relate this to the examples. A description of a software framework based on the principles of the theoretical framework follows, and we give some examples of how different focus+context visualization applications have been constructed using this framework. Finally, we discuss the implications of the formal framework and outline some future work in this area.

Social network analysis is an active area of study beyond sociology. It uncovers the invisible relationships between actors in a network and provides understanding of social processes and behaviors. It has become an important technique in a variety of application areas such as the Web, organizational studies, and homeland security. This paper presents a visual analytics tool, OntoVis, for understanding large, heterogeneous social networks, in which nodes and links could represent different concepts and relations, respectively. These concepts and relations are related through an ontology (also known as a schema). OntoVis is named such because it uses information in the ontology associated with a social network to semantically prune a large, heterogeneous network. In addition to semantic abstraction, OntoVis also allows users to do structural abstraction and importance filtering to make large networks manageable and to facilitate analytic reasoning. All these unique capabilities of OntoVis are illustrated with several case studies.

A new method for the visualization of state transition systems is presented. Visual information is reduced by clustering nodes, forming a tree structure of related clusters. This structure is visualized in three dimensions with concepts from cone trees and emphasis on symmetry. A number of interactive options are provided as well, allowing the user to superimpose detail information on this tree structure. The resulting visualization enables the user to relate features in the visualization of the state transition graph to semantic concepts in the corresponding process and vice versa.

Simple presentation graphics are intuitive and easy-to-use, but show only highly aggregated data and present only a very limited number of data values (as in the case of bar charts), and may have a high degree of overlap which may occlude a significant portion of the data values (as in the case of the x-y plots). In this paper, we therefore propose a generalization of traditional bar charts and x-y-plots which allows the visualization of large amounts of data. The basic idea is to use the pixels within the bars to present the detailed information of the data records. Our so-called pixel bar charts retain the intuitiveness of traditional bar charts while allowing very large data sets to be visualized in an effective way. We show that, for an effective pixel placement, we have to solve complex optimization problems, and present an algorithm which efficiently solves the problem. Our application using real-world e-commerce data shows the wide applicability and usefulness of our new idea.

Abstract. Insight in the global structure of a state space is of great help in the analysis of the underlying process. We advocate the use of visualization for this purpose and present a new method to visualize the structure of very large state spaces. The method uses a clustering method to obtain a simplified representation, which is used as a backbone for the display of the entire state space. With this visualization we are able to answer questions about the global structure of a state space that cannot easily be answered by conventional methods. We show this by presenting a number of visualizations of real-world protocols. 1