HERMAN ET AL.: GRAPH VISUALIZATION AND NAVIGATION IN INFORMATION VISUALIZATION 1
Graph Visualization and Navigation in Information
Visualization: a Survey
Ivan Herman, Member, IEEE CS Society, Guy Melançon, and M. Scott Marshall
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.
Index Terms—Information visualization, graph visualization, graph drawing, navigation, focus+context, fish–eye, clustering.

1 Introduction
lthough the visualization of graphs is the subject of this
survey, it is not about graph drawing in general.
Excellent bibliographic surveys[4],[34], books[5], or even
on–line tutorials[26] exist for graph drawing. Instead, the
handling of graphs is considered with respect to information
visualization.
Information visualization has become a large field and
“sub–fields” are beginning to emerge (see for example Card
et al.[16] for a recent collection of papers from the last
decade). A simple way to determine the applicability of graph
visualization is to consider the following question: is there an
inherent relation among the data elements to be visualized?
If the answer to the question is “no”, than data elements are
“unstructured” and the goal of the information visualization
system might be to help discover relations among data
through visual means. If, however, the answer to the question
is “yes”, then the data can be represented by the nodes of a
graph, with the edges representing the relations.
Information visualization research dealing with
unstructured data has a distinct flavour. However, such
research is not the subject of this survey. Instead, our
discussion focuses on representations of structured data, i.e.,
where graphs are the fundamental structural representation
of the data. Information visualization has specific
requirements, which means that we will approach the results
of traditional graph drawing from a different perspective than
the other surveys.
1.1 Typical Application Areas
Graph visualization has many areas of application. Most
people have encountered a file hierarchy on a computer
system. A file hierarchy can be represented as a tree (a special
type of graph). It is often necessary to navigate through the
file hierarchy in order to find a particular file. Anyone who
has done this has probably experienced a few of the problems
involved in graph visualization: “Where am I?” “Where is the
file that I'm looking for?” Other familiar types of graphs
include the hierarchy illustrated in an organisational chart and
taxonomies that portray the relations between species. Web
site maps are another application of graphs as well as
browsing history. In biology and chemistry, graphs are
applied to evolutionary trees, phylogenetic trees, molecular
maps, genetic maps, biochemical pathways, and protein
functions. Other areas of application include object–oriented
systems (class browsers), data structures (compiler data
structures in particular), real–time systems (state–transition
diagrams, Petri nets), data flow diagrams, subroutine–call
graphs, entity relationship diagrams (e.g. UML and database
structures), semantic networks and knowledge–representation
diagrams, project management (PERT diagrams), logic
programming (SLD–trees), VLSI (circuit schematics), virtual
reality (scene graphs), and document management systems.
Note that the information isn’t always guaranteed to be in a
purely hierarchical format — this necessitates techniques
which can deal with more general graphs than trees.
1.2 Key Issues in Graph Visualisation
The size of the graph to view is a key issue in graph
visualization. Large graphs pose several difficult problems. If
the number of elements is large it can compromise
performance or even reach the limits of the viewing platform.
Even if it is possible to layout and display all the elements,
the issue of viewability or usability arises, because it will
become impossible to discern between nodes and edges (see
Figure 1, although this tree is by no means a very complex
one). In fact, usability becomes an issue even before the
problem of discernability is reached. It is well known that
comprehension and detailed analysis of data in graph
structures is easiest when the size of the displayed graph is
small. In general, displaying an entire large graph may give an
indication of the overall structure or a location within it but
makes it difficult to comprehend. These issues form the
context for most of this survey.
A

• The authors are with the Centre for Mathematics and Computer
Sciences (CWI), Amsterdam, The Netherlands
E–mail: I.Herman@cwi.nl, G.Melancon@cwi.nl,
M.S.Marshall@cwi.nl