IV 2013
Matching Application Requirements with Dynamic Graph Visualization Profiles
Fabian Beck, Michael Burch
VISUS, University of Stuttgart
Stuttgart, Germany
{fabian.beck,michael.burch}@visus.uni-stuttgart.de
Abstract—Mapping a dynamic graph dataset to an inappropriate visualization leads to a degradation of visualization
performance at some task. To tap the full potential of existing
dynamic graph visualization techniques, we propose a methodology for matching application requirements with dynamic
graph visualization profiles. We target at supporting experts
choosing the right visualization technique. Our methodology
describes both the application requirements and the visualization techniques as profiles covering important aesthetic
criteria for visualizing dynamic graphs. Characteristics of the
graph and task are used to derive the application profile. The
probably most appropriate visualization technique is the one
whose profile matches best the required application profile. We
compile exemplary visualization profiles for representatives of
dynamic graph visualization approaches and demonstrate the
methodology in a case study.
Keywords-dynamic graph visualization; aesthetic criteria;
requirements
I. I NTRODUCTION
The readability of node-link representations of static
graphs has been studied in detail [1]. It has been assessed
how well they meet certain requirements called aesthetic
criteria, for instance, the minimization of the number of
edge crossings or the reduction of overlap of nodes and
links. Empirical studies [2], [3] have validated and ranked
these criteria. We aim at continuing this line of research for
dynamic graphs: more and more approaches are proposed
for visualizing such graphs1 , but the metadiscourse seems
to fall behind.
Beck et al. [4] already generalized aesthetic criteria
from static to dynamic graphs. While these criteria support
assessing the general quality of a visualization approach,
they do not directly support an analyst in finding the most
appropriate visualization technique. This paper proposes a
methodology for embedding dynamic graph drawing aesthetics into a process for finding matching visualization
techniques in specific application scenarios. The matching
process is driven by profiles that summarize visualization
and application in terms of aesthetic criteria: First, profiles
of different candidate visualization techniques are created.
Second, application profiles are derived from characteristics
1 Google Scholar lists 82 articles (26 in 2011/12) for the term “dynamic
graph visualization” and even 304 (92 in 2011/12) for “dynamic network
visualization”; retrieved: 2012-09-19.
Stephan Diehl
University of Trier
Trier, Germany
[email protected]
of the dataset and task. Third, the most appropriate visualization technique is selected by matching application and
visualization profiles.
Beside specifying this process, a major contribution of
this paper is providing visualization profiles for representatives of three fundamental approaches to dynamic graph
visualization: animated node-link diagrams, timeline-based
node-link diagrams, and matrix-based approaches. A case
study illustrates how these can be matched with application
profiles for specific dynamic graph drawing applications.
II. DYNAMIC G RAPH V ISUALIZATION A ESTHETICS
A graph structure that changes over time is called a dynamic graph. A visualization of such a graph should provide
detailed information and uncover general regularities and
anomalies of the graph structure; the user should be enabled
to detect or read information like edge weights, adjacency
of vertices, paths, as well as clusters of vertices, outliers,
trends, symmetries, and patterns. A dynamic graph visualization that meets these general design goals is considered
readable. Criteria for increasing the readability of graphs are
usually referred to as aesthetic criteria interpreting the term
aesthetic more from a pragmatic point of view than from an
artistic one.
The aesthetics criteria proposed by Beck et al. [4] provide
dimensions to assess dynamic graph visualizations. They
translate the unspecific term aesthetic into a set of specific
criteria that are applicable to arbitrary dynamic graph visualizations. These criteria target at being independent and
exhaustive as far as possible.
General Aesthetic Criteria The general criteria abstract from
conventional aesthetic criteria for static node-link diagrams
like minimizing the number of edge crossings. Abstraction is
necessary because the criteria also need to be applicable to
other forms of graph visualizations such as those employing
adjacency matrices. First, visual clutter is to be reduced (GAC1).
Second, spatial aliases—objects possibly mistaken for another—
should be avoided (GAC2). Third, if entities are represented by
multiple visual objects (e.g., nodes are represented in matrixbased graph visualizations by rows and columns), these multiple
representatives have to match easily (GAC3). Fourth, space
should be used efficiently (GAC4).
Dynamic Aesthetic Criteria Additional aesthetic criteria are
required for representing dynamic aspects: When comparing
application
dynamic
graph
Table I
E XAMPLES FOR GRAPH CHARACTERISTICS .
visualization
approaches
task
applicable
application
profile
matching
visualization profiles
static
graph theoretical
numeric
directed or undirected, cyclic
or acyclic, tree, bipartite,
planar, weighted, multi-edges
#nodes, dense/sparse, average
degree of vertices, number of
connected components,
clustering coefficient, frequency
of cliques or outliers, average
centrality, small-world
characteristics
dynamic non-emptiness of change sets, #changes, dynamic variance,
set theoretical relations
other numeric relations of the
between the change sets
change sets
Figure 1. Methodology for matching application and visualization profiles.
subsequent graphs, the user’s mental map [5] of the visualization
is to be preserved (DAC1). In general, when analyzing the
dynamic information, the user’s cognitive load should be low
(DAC2). And like spatial aliases, temporal aliases (objects that
might be mistaken one for the other due to their placement in
time/on a time axis) are to be avoided (DAC3).
Aesthetic Scalability Criteria Scalability addresses the question
whether a visualization is still readable for larger datasets. Dynamic graphs can grow in different dimensions: The visualization
should be scalable for an increasing number of vertices (SC1),
for an increasing number of edges (SC2), as well as for longer
sequences of graphs (SC3).
A good dynamic graph visualization reasonably fulfills
most of these criteria. But in general, it cannot be assumed
that a visualization can meet all criteria perfectly—each
technique is a trade-off between different criteria.
III. M ETHODOLOGY FOR M ATCHING A PPLICATION AND
V ISUALIZATION
Finding the right visualization for a graph visualization
problem is similar to finding the right candidate for a job:
In such an assessment process, we start with a job profile and
search for a matching profile among the job applicants. In the
case of the visualization, data and task define an application
profile, which has to be matched instead of a job profile. And
in place of the applicants, different visualization approaches
compete for the offer.
A profile of a job applicant might be defined in terms of
work experience, skills, education, etc. In our visualization
scenario, the aesthetic criteria could stand in for these properties. Hence, ratings for all criteria represent the profiles of
the visualization and, analogously, importance estimations
for all criteria represent the profile of the application.
Figure 1 illustrates our methodology, which is explained in
detail in the following.
A. Visualization Profiles
The aesthetic criteria introduced in Section II provide the
means for describing the characteristics of dynamic graph
visualizations: The analyst has to rate the visualization for
each of the criteria as already demonstrated by Beck et
al. [4]. We define a visualization profile as the vector of
these ratings. Generating visualization profiles is independent of an application. Hence, the visualization profiles are
universal and can be reused in subsequent assessments. It is
important, however, that the ratings were made consistently,
for instance, by the same analyst applying the same set of
scales.
B. Application Profile
The performance of a visualization is determined by the
dataset that is visualized and by the task that is performed
with the help of the visualization [6]. Hence, dataset and task
describe an application and imply the requirements for an
appropriate visualization, which could be expressed in terms
of aesthetic criteria. To describe the application, we classify
the data as well as the tasks according to significant characteristics. Based on these characteristics, we estimate the
relative importance of each of the aesthetic criteria. These
values form the profile for the particular application. They
are harder to derive than the visualization profiles because
we first need a taxonomy for specifying characteristics of
the graph and task. As discussed in the following, static
and dynamic graph characteristics are distinguished as well
as theoretical and numeric characteristics; Table I provides
examples for each category.
1) Static Graph Characteristics: Static graph characteristics describe the graphs of the sequence of changing
graphs independent of each other. While graph theoretical
characteristics are assumed to hold for all graphs in the
considered application, numeric characteristics are only supposed to represent common or average characteristics among
the graphs.
• Graph theoretical characteristics (static) We usually
describe a static graph in terms of its graph theoretical
characteristics (e.g., a weighted bipartite graph). This is
essential information for visualizing the graph because
these characteristics impose certain requirements on the
visualization (e.g., the visualization is required to show
edge weights) or could be exploited to increase the
readability of the layout (e.g., in a bipartite graph,
•
we could draw the two sets of vertices in separate
areas). These aspects of the graph topology might limit
the number of applicable approaches—illustrated by
the dashed line in Figure 1. For instance, not every
approach might be capable of representing weighted
graphs.
Numeric characteristics (static) Besides graph theoretical characteristics, other properties could have a
significant impact on visualizing the graphs: For example, the size of the graph in number of nodes is
relevant and it is important whether a graph is sparse or
dense. Moreover, a graph may usually form a connected
component or fall into many of these components.
Those kinds of properties can be described as numeric
characteristics.
2) Dynamic Graph Characteristics: While it is common
practice to look at the static characteristics of a graph,
investigating dynamic graph characteristics is not as mature.
Of course, a dynamic graph can be described as a sequence
of static graphs and their characteristics, but this does not
necessarily hint at the characteristics of the differences
between the subsequent graphs. These differences are very
important for dynamic graph visualization because proximity
in sequence usually implies temporal or spatial proximity in
visualization. And optimizing the dynamic aesthetic criteria
is largely based on exploiting this proximity.
To analyze the difference, we consider the transition from
one graph to the other as consisting of four steps: First, we
remove the edges that are no longer needed, second, we
also delete the superfluous vertices, third, we add the newly
needed vertices, and fourth, we finally add the new edges.
Each of the steps can be described by removing or adding a
set of vertices or edges. These sets need to be minimal in the
sense that no vertices or edges are unnecessarily removed
and immediately added again in one transition. In addition
to these sets, we have to consider changes of attributes
like edge weights. Analogous to the static characteristics,
we describe these change sets from a graph theoretical
perspective as well as by numeric properties.
•
•
Graph theoretical characteristics (dynamic) An essential question is which of the four change sets are
always empty. For instance, some applications only take
growing graphs into account where the set of removed
vertices and the set of removed edges is empty. Beyond
that, it is interesting to look at the relationships between
the sets: For instance, we may only remove edges when
we also remove the source or the target vertex of the
edge. Or the set of added vertices and edges may always
build a clique as, for example, an author and his coauthors in a co-author graph.
Numeric characteristics (dynamic) Beside the number or frequency of changes, an important property
is dynamic variance—the size of the change sets in
relation to the size of the graph. For example, a graph
could have a high dynamic variance in terms of edges,
which means that a high percentage of edges changes
in each step, while the dynamic variance with respect
of vertices is low, which means that only few vertices
are removed or added in each step.
3) Task Characteristics: Users employ graph visualizations to answer certain questions. Different questions lead
to different tasks the user wants to perform, and hence, to
different requirements for a graph visualization technique.
The tasks usually do not restrict the number of applicable
approaches, but could heavily influence the usefulness of
a visualization approach for a particular application. For
instance, if we are interested in following paths, matrix
visualizations might be less useful than for other tasks [7].
Lee et al. [8] developed a taxonomy for static graph
visualization tasks, which groups the tasks into four groups:
topology-based tasks like finding adjacent vertices, attributebased tasks like comparing the weights of edges, browsing
tasks like following paths, and overview tasks like roughly
estimating the number of vertices. We can directly apply
this taxonomy in our methodology to describe the static
aspects of a task. Extending this framework by dynamic
tasks, new tasks need to be added to the proposed categories:
For instance, the topology-based tasks could extended by a
time dimension so that finding adjacent vertices is bound
to a certain point in time or period. Among the attributebased tasks, especially tasks that focus on numeric attributes
get more important: By adding the time dimension these
attributes now form time series. We may want to observe
trends and according counter trends, periodic progress, or
temporal shifts.
An alternative is employing the taxonomy of Ahn et
al. [9], which even directly addresses the use case of
dynamic graph visualization. By discerning individual temporal tasks from aggregated temporal event tasks and rate
of change, they put the time dimension into the main focus
of the categorization and provide a detailed list of possible
task in each category. Numeric changes like growth and
contraction are already integrated into their framework. As
well, the birth and death of network structures is considered.
4) Generating the Application Profile: The static and
dynamic graph characteristics together with the task characteristics form the foundation for compiling the application profile. The characteristics need to be translated into
requirements described as ratings for the aesthetic criteria.
The two profiles, hence, complement each other: While the
visualization profile represents acquirements, the application
profile formulates requirements.
Some characteristics are easy to translate; for instance,
it is obvious that a large number of nodes in the graph
requires good scalability with respect to number of nodes
(SC1). In other cases, the analyst needs some visualization
expertise for translation; for example, it is important for path
finding tasks that multiple representatives (of nodes) can be
easily matched (GAC3). Several characteristics might also
interact with each other; for instance, scalability may not
be important even for large graphs when the tasks do not
require showing the whole graph at once.
C. Profile Matching
After deriving a set of visualization profiles and an
application profile, the final step of matching these profiles
is straightforward and simple: The visualization profile that
is most similar to the application profile promises to be the
most appropriate visualization technique for the application
at hand. However, it cannot be assumed that there always
will be a single close match: it is likely that several techniques are matching only partly having different advantages
and drawbacks. Then, the profiles need to be refined or other
criteria have to be taken into consideration until, finally, one
of the techniques forms the best match.
D. Discussion
Our methodology defines a process that supports evaluating dynamic graph visualizations. In the following, we discuss the limitations as well as important issues for practical
application.
1) Profiles and Ratings: The aesthetic criteria were designed to be orthogonal so that rating one criterion is
independent of rating another. But perfect orthogonality
cannot be reached in practice. Moreover, the aesthetic criteria may not exhaustively cover all aspects of dynamic
graph visualization, such as interaction. Considering a specific application, the criteria might need to get adapted or
extended. However, the proposed methodology is open to
these changes, even orthogonal to them because the criteria
just span the dimensions the analyst uses for describing the
visualizations and applications.
Each profile—application or visualization profile—
consists of a rating for each of the aesthetic criteria. One
way of assessing a criterion is to define a metric. Although
such a metric may provide precise values, it is only a
heuristic to estimate a criterion; it is hard to reduce an
aesthetic criterion to a simple metric. Hence, we prefer using
more subjective ratings on a primitive ordinal scale (e.g.,
“good, medium, bad” as also proposed for the Cognitive
Dimensions Framework [10]). To increase the validity of
these ratings, we may employ different experts to assess the
aesthetic criteria and merge their results.
In the assessment process, we need to derive visualization
profiles for all applicable visualization approaches. However,
there might be too many available approaches to thoroughly
formulate profiles for. But the assessment process could also
be applied iteratively. In the first iteration, we just compare
a roughly estimated application profile to the profiles of
general dynamic graph visualization paradigms. Then, we
could focus on the most promising combinations and step
by step refine the application as well as the visualization
profiles until we come to a clear conclusion.
2) Application: Our methodology is designed for finding
matching visualization approaches for a dynamic graph
visualization problem. Applying the methodology, however,
requires a sound knowledge of visualization techniques in
this domain and significant experience in designing visualizations in general. Hence, the target group of the presented
approach are visualization experts. Due to this specific
scope, performing a user study with non-experts or without
a realistically complex task for evaluating the methodology
would probably not provide valuable insights.
We also consider the methodology as a framework for
discussion, reasoning, and reflecting about dynamic graph
visualization techniques in a systematic way, similar to a
taxonomy. As already discussed, the methodology does not
rely on specific aesthetic criteria; they can be adapted to the
needs of the experts. But having a set of criteria embedded in
the matching process helps analyzing the complex visualization problem step by step and consistently reviewing existing
approaches. In contrast to an unsystematic discussion, the
guided methodology might better prevent from missing
important aspects, might reveal problematic issues more
clearly, and might additionally point to directions for future
research.
By omitting or generalizing parts of the assessment process, the methodology can be also applied in other scenarios:
For example, visualization profiles can be used for formulating design goals of novel visualization approaches. The comparison of visualization profiles is equivalent to a qualitative
evaluation of existing visualizations. Furthermore, the lack
of a visualization profile that matches a given application
profile might hint at an unanswered research question.
The complexity of the methodology, its capabilities as a
basis for discussion, and its flexible applicability are reasons why we manually classify visualization techniques and
perform a case study in the following instead of conducting
a user study. The ratings and assessment of these analyses,
however, only reflect the subjective opinion of the authors.
IV. E XEMPLARY V ISUALIZATION P ROFILES
Though the visualization of static graphs has been researched a lot, the additional time dimension of dynamic
graphs still forms a significant visualization challenge. In
general, two main approaches exist for representing time:
animation, which is a time-to-time mapping, and timelines,
which encode time in space. These two approaches have also
been explored for dynamic graph visualization:
• Time-to-Time Mapping A time-to-time mapping represents the dynamic graph data as an animated sequence of graphs. Although there exist two general
graph visualization paradigms—node-link diagrams and
adjacency matrices—only node-link diagrams are usually combined with animation. Animated node-link
time-to-time mapping
time-to-space mapping
animation
a
time
b
c
a
b
c
Foresighted Layout with Tolerance (FLT)
Figure 2.
•
Parallel Edge Splatting (PES)
Pixel-Oriented Matrix (POM)
Three representatives of dynamic graph visualization techniques.
diagram approaches either consider past and future
changes for laying out a single diagram of the sequence
(offline approach [11]) or only past changes (online
approach [12]). Specialized graph layouts preserve a
viewer’s mental map [5] of the graph and reduce
cognitive efforts.
Time-to-Space Mapping A time-to-space mapping
based on node-link diagrams usually shows the dynamic
graph data as a static diagram and arranges subsequent
graphs side-by-side on a timeline [13], [14]. This
concept allows visually comparing several subsequent
graphs at once, but requires the same simple nodelayout in all graphs. But time-to-space mappings can
also be applied to matrix representations: Each cell of
the matrix contains a small timeline representing the
evolution of a particular edge—various concepts have
been proposed for these miniature timelines [15], [16].
These two categories only describe the main approaches to
dynamic graph drawing; other variants exist [17] and hybrid
approaches are possible [18]. For deriving exemplary visualization profiles, however, we selected representatives that are
intended to reflect the two main categories. In particular, we
chose the three visualization techniques depicted in Figure 2:
Foresighted Layout with Tolerance (FLT) [11] An animated
(time-to-time mapping) node-link representation. FLT is an offline approach to compute animated node-link diagrams. It tries
to minimize the changes of the layouts of subsequent graphs
without sacrificing the quality of each individual layout.
Parallel Edge Splatting (PES) [13] A static (time-to-space mapping) node-link representation. PES shows subsequent graphs in
vertical stripes each consisting of two axes of aligned nodes; all
links connect nodes from left to right. Visual clutter produced
by link crossings is counterbalanced by showing a color-coded
edge density field instead of plain links.
Pixel-Oriented Matrix (POM) [16] A static (time-to-space
mapping) matrix representation. In an adjacency matrix, each
cell represents an edge as a combination of two nodes. To
represent the dynamic change of edges, POM splits each cell
into sequences of shaded “pixels” (small boxes) encoding the
changing edge weight—the wrapping strategy for the timeline as
illustrated in Figure 2 is one out of several proposed solutions.
Next, we compile profiles for these three visualization
techniques based on the aesthetic criteria. Table II (columns
FLT, PES, and POM) summarizes the resulting profiles. The
symbols indicate the rating for each criterion: “+” for good,
“o” for moderate, and “-” for bad. The following assessment
is a significantly revised version of [4].
First, the general aesthetic criteria describe features for
representing static graphs: FLT produces visual clutter
through edge crossings (GAC1), which, however, can be
reduced by the layout algorithm. In PES the 1D layout of the
nodes constrain such optimizations. In contrast, the matrixbased approach does not have to deal with such problems
because edge representations do not overlap. In FLT there
are no multiple representations of vertices or edges (GAC3),
and spatial aliases (GAC2) might only appear for a few
edges (e.g., if they are drawn nearly parallel). In PES,
spatial aliases (GAC2) are more likely due to parallel edges
and edges crossing in small angles, but are reduced by the
density field technique. Moreover, multiple representatives
of vertices (GAC3) exist but can be matched by following a
horizontal line. Both criteria are worse for POM: Matrices
suffer from multiple representations for the vertices shown
in the rows and the columns (GAC3); hence, spatial aliases
are likely (GAC2). However, matrix visualizations are more
compact (GAC4) because edges are represented as small
boxes. Representing edges as links requires more space—
showing density fields as applied in PES eases the problem.
Second, features for perceiving dynamic changes have to
be considered: While it is hard for the user of an animated
node-link representation to preserve his or her mental map
(DAC1), the whole graph is concurrently visible in PES and
POM, that is to say, the mental map is always refreshable
by looking at the image. Since in PES and POM the time
information is aligned on a timeline, the user’s cognitive
load (DAC2) is quite low and the user usually is able to
avoid or compensate for temporal aliases (DAC3)—in POM,
however, some cognitive efforts are required for comparing
edges belonging to the same time step and in PES tracing
edges across several time step still could sometimes be
misled by similar edges. In contrast, the animated represen-
Table II
E XEMPLARY VISUALIZATION PROFILES AND TWO APPLICATION
PROFILES FROM THE CASE STUDY.
FLT PES POM A1 A2
GAC1:
GAC2:
GAC3:
GAC4:
Reduce visual clutter
Reduce spatial aliases
Spatial matching of mult. representatives
Maximize compactness
o
+
+
-
o
o
o
+
+
o + o
+ o
o -
DAC1: Preserving the mental map
DAC2: Reducing the cognitive load
DAC3: Reducing temporal aliases
o
-
+
+
o
+
o
+
o +
- +
o +
SC1: Scalability in number of vertices
SC2: Scalability in number of edges
SC3: Scalability in number of graphs
+
o
-
+
o
o
o
+
o
+ +
+ o
- +
tation of FLT challenges the user much more with respect to
these two criteria. FLT, like some other animated node-link
approaches, however, uses a special layout algorithm that
strives to preserve the mental map (DAC1).
Third, scalability needs to be discussed: Since in FLT
the nodes can be scattered all around the drawing area, it
performs good with respect to the number of vertices (SC1).
Though the nodes are constrained to vertical axes in PES,
nodes only need little space. Usually, in particular matrix
visualization can represent many vertices in a readable
way; this readability, however, is limited in POM because
split cells need to be larger than cells in other matrix
representations. But due to its compact edge representation,
POM has the best scalability in number of edges (SC2), but
also reasonable edge densities are possible for FLT because
of layout optimization and PES because of showing edge
density fields. Using the time-to-space mapping, PES and
POM provide an overview on a reasonable number of graphs
(SC3) limited, however, by the width of the diagram in PES
or the size of a cell in POM. Although FLT is theoretically
unrestricted in number of graphs, we ranked it last with
respect to this criterion because the user is only able to
remember a few of the previously shown graphs.
The assessment shows unique profiles for the analyzed
visualization techniques. For the general aesthetic criteria,
the FLT approach performs well, but the representation of
time seems to be its main disadvantage. In contrast, PES and
POM seem to be more suitable for applications focusing
on the time dimension, but they have drawbacks on other
dimensions. With respect to the general criteria, FLT and
POM appear to be counterparts while PES can be considered
a compromise of both.
V. C ASE S TUDY: M ATCHING A PPLICATION P ROFILES
We demonstrate how to derive application profiles and
how to match them with visualization profiles. We analyze
a realistic dynamic graph visualization problem from the
area of bibliography.
Example: In a co-author graph, researchers are connected to each
other if they publish together. We look at ego-centered co-author
graphs; they contain a central author and all of his or her co-authors.
When we study the evolution of the publishing activity, we get a
dynamic co-author graph where each static graph may model the
publications of a year. We could extend the graph year by year by
just adding new publications (A1) or look at each year independently
forgetting previous publications (A2).
These two similar scenarios provide two applications
having surprisingly different profiles as discussed below.
•
•
Graph A co-author graph is an undirected, but
weighted graph. The weight of the edge counts the
number of co-publications. The central author has a
high degree because it is connected to all other vertices.
The number of vertices varies from few vertices for
junior researchers to more than 100 vertices for successful senior researchers. From the dynamic perspective,
A1 leads to a graph that is growing over time—neither
vertices nor edges are removed at any time (the sets of
removed vertices and edges are empty). In contrast to
A2, where vertices and edges are added and removed
(none of the sets is empty in general)—the dynamic
variance is much higher in A2. A research career lasts
up to 40 years, which produces sequences consisting of
up to 40 static graphs.
Task A typical use case of these co-author graphs is
to get to know a particular researcher. Looking at the
author’s most frequent collaborators could be interesting. Detecting clusters in the graph helps identifying
working groups the author is or has been a member
of. Newly arising clusters may indicate that the author
has changed his or her working environment (e.g.,
a new job) or area of research. Years where many
edges increase their weight (aggregated weights in A1)
or have high weights (non-aggregated weights in A2)
represent particularly productive publishing periods.
This basic analysis of the application enables creating
application profiles. The two profiles for A1 and A2 are
summarized in Table II together with the visualization
profiles. We use “+” to indicate high importance, “o” for
moderate importance, and “-” for low importance.
In the static graphs, the task requires finding clusters
of authors, which would especially benefit from reducing
spatial aliases (GAC2) and the spatial matching of multiple
representatives (GAC3). Reducing visual clutter (GAC1) and
maximizing compactness (GAC4) is not as important. The
difference between A1 and A2 is that the static graphs of
A1 consist of more entities, which makes all general criteria
a little more important. Since the temporal information is
getting aggregated in A1, the dynamic aesthetic criteria are
only of secondary interest. Nevertheless, the mental map
should be preserved if possible (DAC1) and temporal aliases
should be reduced (DAC2). For A2, all dynamic criteria are
much more important—the graph could heavily change from
year to year. Both applications require a good scalability in
number of vertices (SC1) because they potentially have to
deal with more than 100 vertices. Due to adding cliques,
the edge density (SC2) could be quite high, especially for
A1. Finally, we do not need a good scalability in number of
graphs in A1 again because the information is aggregated
over time, but all the more for the non-aggregated graphs of
A2.
Finally, matching application and visualization profiles,
the overview in Table II clearly suggests to use the nodelink approach FLT for A1, where publications are aggregated over time. For A2, where each year is considered
independently, the visualization profile of PES matches
best the application profile. Nevertheless, the profiles do
not match perfectly—this might hint at room for possible
improvements. For example, A2 requires a high scalability
in number of graphs (SC3), which is not fulfilled by any of
the approaches; integrating animation into PES promises to
increase this kind of scalability [18].
VI. R ELATED M ETHODOLOGIES
While we study the specific application of visualizing
dynamic graphs in this work, the underlying problem is
a very basic one: finding the right visualization technique
for a given visualization scenario. Other researchers already
approached this question from different directions and for
other types of visualizations as discussed in the following.
The idea of matching visualization problems and visualization techniques can already be found in the classification
framework for general visualizations introduced by Wehrend
and Lewis [19]: Both, visualization problem and technique,
are classified in a two dimensional matrix; problems and
techniques falling into the same cell can be considered as
matching. The dimensions of the matrix are objects (the
dataset) and operations (the tasks); the authors propose fixed
categories for each. But having only those two categorical
dimensions, the classification is only rough.
Applying a related approach to knowledge visualization,
Zeiller and Edlinger [20] set up a decision matrix for finding
which visualization is suitable for a specific knowledge
management task. While task and visualization form two
dimensions of the matrix, a third dimension is added by also
considering different knowledge management approaches.
The cells of the matrix contain manually derived ratings that
express how well a particular visualization matches knowledge management task and approach. Again the categories
are coarse, but the authors propose fine-grained, generic
criteria for assessing possible combinations.
Instead of providing a framework to let the user make an
informed decision, Mackinlay [21] proposes an automated
approach for creating suitable basic visualization techniques:
First, the expressiveness of a visualization technique reveals
whether a particular technique is capable of expressing a
certain kind of dataset. Second, the effectiveness of the
technique for representing the data has to be judged; an
evaluation scheme based on the accuracy of perception for a
set of encodings is proposed. The process works as follows:
candidate designs are automatically generated for different
aspects of the visualization, filtered by the expressiveness
criterion, then ranked by the effectiveness criterion, and
finally combined to a visualization technique. This work
is extended towards scientific visualization by Senay and
Ignatius [22].
VII. C ONCLUSIONS
We presented a methodology for finding appropriate visualization techniques for dynamic graph visualization scenarios applicable by visualization experts. Both the visualization techniques and the application are described as profiles
reflecting different aesthetic criteria. Visualization profiles
are application-independent; we compiled exemplary profiles for three representatives of dynamic graph visualization
approaches. Since the results showed quite unique profiles
for the different approaches, it is unlikely that one approach
performs best across different applications. Deriving profiles
for applications includes analyzing characteristics of the
dynamic graph and the task at hand. We not only discussed
in detail specific properties that can be used to describe those
characteristics, we also demonstrated in a case study how to
compile application profiles—indeed, different visualization
approaches matched best the two studied scenarios.
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