VINCENT

Visualization of Network Centralities

Andreas Kerren, Harald K

ostinger and Bj

orn Zimmer

Linnaeus University, School of Computer Science, Physics and Mathematics (DFM), ISOVIS Group

Vejdes Plats 7, 351 95 V

axj

o, Sweden

Keywords:

Centralities, Network Analysis, Visualization, Biological Networks, Graph Drawing, Coordinated Views.

Abstract:

The use of network centralities in the ﬁeld of network analysis plays an important role when the relative impor-

tance of nodes within the network topology should be rated. A single network can easily be represented by the

use of standard graph drawing algorithms, but not only the exploration of one centrality might be important:

the comparison of two or more of them is often crucial for a better understanding. When visualizing the com-

parison of several network centralities, we are facing new problems of how to show them in a meaningful way.

For instance, we want to be able to track all the changes of centralities in the networks as well as to display

the single networks as best as possible. In the life sciences, centrality measures help scientists to understand

the underlying biological processes and have been successfully applied to different biological networks. The

aim of this paper is to present a novel system for the interactive visualization of biochemical networks and its

centralities. Researchers can focus on the exploration of the centrality values including the network structure

without dealing with visual clutter or occlusions of nodes. Simultaneously, ﬁltering based on statistical data

concerning the network elements and centrality values supports this.

1 INTRODUCTION

In Information Visualization or Graph Drawing, there

are many different approaches to visualize complex

networks that support various exploration methods.

Such networks (or graphs) can be drawn, for example,

by using standard graph drawing algorithms (Di Bat-

tista et al., 1999; Jia et al., 2008). In case of social

networks and their visual analysis based on any graph

representation, important tasks are the identiﬁcation

of communities and central actors as well as the anal-

ysis of roles and positions (Henry et al., 2007). Those

tasks allow researchers to ﬁnd the most relevant parts

and correlations in social networks.

Another example are biochemical networks,

which are representations of biological processes,

such as metabolism, the regulation of genes, or the in-

teraction of proteins. They have been of strong inter-

est in the last few years and are crucial for a compre-

hensive understanding of living beings (Jusuﬁ et al.,

2011; Albrecht et al., 2010). In this paper, we focus

on this application ﬁeld. In the life sciences, centrality

measures help scientists to understand the underlying

biological processes and have been successfully ap-

plied to different biological networks. Network cent-

rality analysis measures the relative importance of

nodes in a network based on their connectivity within

the network structure (Dwyer et al., 2006; Kosch

utzki

and Schreiber, 2004). Applications in biological net-

works can be found at the investigation of protein-

protein interaction networks (PPI) or transcriptional

regulatory networks (TR) (Kosch

utzki and Schreiber,

2004). Typical tasks for such network analysis by the

use of centrality values are: (a) ﬁnding nodes with

high centrality values, since those are more likely of

interest to the researcher; (b) ﬁnding nodes with low

centrality values to hide them, since they are of less

importance; (c) ﬁnding nodes with high values in sev-

eral centralities (comparisons of values over many

nodes). Especially the latter task is challenging, be-

cause important problems arise when visualizing it.

For example: How to visualize the data in a way that

researchers can get the most meaning out of it? How

enabling the user to keep track of centrality changes

within the network? How to minimize occlusions and

visual clutter? Or how to build a ﬂexible solution in

order to deal with a large number of centrality values

at the same time?

The aim of our work is to overcome the aforemen-

tioned problems and to develop a new solution of vi-

703

Kerren A., Köstinger H. and Zimmer B..

VINCENT - Visualization of Network Centralities.

DOI: 10.5220/0003822207030712

In Proceedings of the International Conference on Computer Graphics Theory and Applications (IVAPP-2012), pages 703-712

ISBN: 978-989-8565-02-0

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

sualizing networks together with its centralities. We

introduce a new visual representation of networks and

their centrality values in a circular view. Analyses can

then be focused on the exploration of the centrality

values including the network structure, without deal-

ing with visual clutter or occlusions of nodes. Filter-

ing based on statistical data concerning the network

elements and centrality values supports this and helps

keeping the network itself readable. Hereby, the com-

parability of the nodes is one of most important goals

to fulﬁll, followed by the minimization of visual clut-

ter and occlusions.

The remainder of this paper is organized as fol-

lows. The next two sections provide a brief overview

of network analysis and network centrality concepts

and motivate their importance for visual network

analysis on the basis of biological networks. Related

work and actual challenges of the visual analysis of

network centralities are summarized too. To address

those problems, our tool ViNCent is introduced in

Section 4. Here, the system and its design are ex-

plained in detail. An explicit description of the meth-

ods and approaches used to solve the described prob-

lems is given too. Section 5 exempliﬁes the interac-

tion design of our tool based on a small use case sce-

nario. A discussion at the end of this section summa-

rizes advantages and disadvantages of our tool. The

conclusion and future work section deals with possi-

ble improvements of the tool and planed further work.

2 BACKGROUND

This section provides additional background informa-

tion to facilitate the understanding of the rest of the

paper. First, a brief introduction into graphs is given

including the most important deﬁnitions.

A graph provides information about single ele-

ments and relationships between those. A (simple)

graph G = (V, E) consists of a ﬁnite set of vertices

(or nodes) V and a set of edges E ⊆ {(u, v)|u, v ∈

V, u 6= v}. An edge e = (u, v) in the graph G con-

nects two nodes u and v. Two nodes u and v are said

to be incident with the edge e = (u, v) and adjacent to

each other. The degree d(u) of a node u is deﬁned as

the number of edges incident to this node u. Further-

more, we can deﬁne a walk on a graph as described

as follows: let (e

, ..., e

) be a sequence of edges in a

graph G = (V, E). This sequence is called a walk if

there are nodes v

, ..., v

such that e

= (v

i−1

, v

) for

i = 1, ..., k. If the edges e

and the nodes v

are pair-

wise distinct respectively, then the walk is called a

path. The length of a walk/path is given by its num-

ber of edges, i.e., k = |(e

, ..., e

)|. A shortest path

between two nodes u, v is a path with minimal length.

The distance (dist(u, v)) between two nodes u, v is the

length of a shortest path between them. (Jusuﬁ et al.,

2010; G

org et al., 2007; Di Battista et al., 1999)

2.1 Network Analysis

In the sciences, huge networks are used to model

structural relationships of various types. Therefore,

network analyses for social, biological or computer

sciences become more important as well to support a

better understanding of the underlying network struc-

tures (Newman, 2010). The following paragraphs

deal with network analysis and identify important

tasks by means of biological networks.

Analysis of Biological Networks. Junker et al.

state in their work: “structural analysis of networks

can lead to new insights into biological systems

and is a helpful method for proposing new hypothe-

ses” (Junker et al., 2006). For structural analysis,

several techniques exist: analysis of the global net-

work structure, network motifs (i.e., small subnet-

works, which occur more often within the whole net-

work), network clustering, and network centralities.

The latter technique uses far more information about

the network than just the relationships and neighbor-

hood of nodes. In fact, this technique uses centrali-

ties of nodes to rank the elements in the network ac-

cording to a given importance concept (Junker et al.,

2006). The following Subsection 2.2 discusses net-

work centralities in general and how they are calcu-

lated. A presentation of suitable visualization tech-

niques follows in Section 3.

2.2 Network Centralities

A network centrality C is a function that assigns a

value C(u) to a node u ∈ V of a given graph G =

(V, E). In order to compare network centralities ac-

cording to their importance, u is more important than

v iff C(u) > C(v) (Kosch

utzki and Schreiber, 2004;

Dwyer et al., 2006). Network centralities are used

for a better understanding of complex processes in

networks. In the life sciences, centrality measures

are useful to understand biological processes. They

are therefore applied to biological networks and then

explored. The following two sample centralities are

typically used by scientists to receive further meaning

of networks (Junker et al., 2006; Dwyer et al., 2006;

Kosch

utzki and Schreiber, 2004):

Eccentricity C

ecc

: This network centrality is cal-

culated by using the distance between nodes in the

IVAPP 2012 - International Conference on Information Visualization Theory and Applications

704

graph. The eccentricity ecc of a node u is deﬁned

as ecc(u) := max

u∈V

dist(u, v) and the corresponding

centrality as C

ecc

(u) :=

ecc(u)

. More central nodes

have therefore a higher value of C

ecc

Random Walk Betweenness C

: Betweenness cen-

tralities model communication paths in networks and

measure the extent to which a node lies on paths be-

tween other nodes. For C

, the centrality of a vertex

w is equal to the number of times that a random walk

from u to v goes through w, averaged over all u and

v (Newman, 2003).

The actual calculation of centralities is not complex.

Even for large-scale networks, this can be done very

fast and be cached to speed up a later interactive ex-

ploration. The complexities of the individual central-

ities range from O(n) to O((n + m)n

) (Kosch

utzki

and Schreiber, 2004).

The problem of choosing the right centralities dif-

fers from network to network. For the computation of

suitable centralities, data about the functional prop-

erties of networks is often missing. This data would

allow to choose the “right” centrality measures, which

show the important parts of the network. Therefore,

this analysis is usually done by visually comparing

the centrality values on the networks (Dwyer et al.,

2006).

3 RELATED WORK

In this section, we provide a short overview of related

work in context of the visualization of network cen-

tralities. Additionally, we outline the most important

challenges. Because of space limitations, we restrict

ourselves to a brief presentation of tools in biochem-

ical network visualization and refer to the survey pa-

per (Albrecht et al., 2010) and the book (Junker and

Schreiber, 2008). For the ﬁeld of social networks, we

refer to the work (Correa and Ma, 2011).

The visualization of network centralities was not

much discussed in the literature so far. Typical meth-

ods, as stated by Dwyer et al. (Dwyer et al., 2006),

are the use of correlations, scatter plots, and paral-

lel coordinates. The problem with these solutions is,

that they have disadvantages when used for biologi-

cal networks, since correlations of centralities might

occur anyway. The most important issue is not only

to show that there are correlations, but to show where

those correlations occur within the network. In their

work, Dwyer et al. present three new techniques to

visualize network centralities as described in the fol-

lowing (Dwyer et al., 2006).

3D Parallel Coordinates-based Comparison.

This method is based on parallel coordinates to

visualize multivariate data. Standard approaches

typically deal with two dimensions. This one uses

3D to stack visual representations of a network

according to one centrality into the third dimension.

Thus, each 2D plane contains the information for a

particular centrality. 3D Parallel Coordinates-based

Comparison gives a good overview of the centrality

values within the network and about how many nodes

fall into a certain value range (Dwyer et al., 2006).

However, this approach does not reveal the actual

network structure.

Orbit-based Comparison. Arranging nodes in an

orbit-based visualization has some advantages over

the previous approach: the network topology is shown

and thus the relationships between the nodes can be

identiﬁed. In more detail, for each centrality a new

2D orbit-based plane is added to the 3D drawing. The

ordering of the planes takes the edge crossing min-

imization and the minimization of inter-plane edge

lengths into account (Dwyer et al., 2006). As a single

orbit provides information about the centrality val-

ues and as the network structure can be seen, this ap-

proach outperforms the previous one when revealing

both structure and centrality values. Drawbacks are

occlusions in the middle of the orbits, and it is hard

to keep track of changes within the single centrality

measures.

Hierarchy-based Comparison. This approach is

conceptually similar compared to the 3D parallel co-

ordinates approach, but it divides the nodes according

to their centrality values into layers. Those layers are

then drawn as horizontal lines, having an ordering on

the line as well. This could be, for example, a de-

creasing centrality value from the left to the right. The

top layers in the visualization are considered to show

larger centrality values. There might be even connect-

ing edges between nodes on the same layer. Filtering

and thresholds are used to reduce visual clutter be-

tween two planes (Dwyer et al., 2006).

CentiBiN. Junker et al. present a different approach

with the CentiBiN tool (Junker et al., 2006). Cen-

tiBiN uses standard node-link diagrams based on a

set of graph drawing algorithms. In addition to the

displayed graph, single centrality values are displayed

next to the visualization in a table. Interactions with

the table, like selecting certain centrality values, are

coordinated with the network visualization as well.

So, it allows the user to locate certain values within

the network. Simple histograms are used to compare

data. CentiBiN has advantages with respect to the

VINCENT - Visualization of Network Centralities

705

amount of available centralities, as it can calculate up

to 17 centrality measures for networks.

CentiScaPe. This tool is able to compute several

network centralities and provides analyses of exist-

ing relationships between user data (based on experi-

ments) and centrality values computed by CentiScaPe

itself (Scardoni et al., 2009). It was implemented as

Cytoscape plugin and supports even large input net-

works. However, the supported interactive visualiza-

tions are restricted.

Challenges. All aforementioned tools and ap-

proaches solve the problem of network analysis ac-

cording to centrality values. But there is still space for

improvements, such as a better arrangement of nodes

and planes, avoiding occlusions and visual clutter, vi-

sualizing structure and centralities simultaneously, or

introducing new interaction and ﬁlter techniques.

4 VINCENT

ViNCent —short for Visualization of Network

Centralities—solves most of the problems addressed

in the previous Subsection 3 by using a radial graph

drawing approach (Kerren and K

ostinger, 2011).

Each network node is represented by a small quad-

rangle that is positioned on a circle. Its connections

to the other nodes (i.e., the edges) are laid out inside

of this circle. Figure 1 shows an example of such a

radial layout in its center: it is easy to see how nodes

are interconnected and how many connections a node

has. Features like edge bundling and degree marking,

as described later in Section 4.2, support the user

in ﬁnding important relationships between nodes as

well as highly or lowly connected nodes.

Before we describe single features of our tool,

we give an overview of the overall design and ar-

chitecture. ViNCent provides multiple, coordinated

views on the input data (mainly by using the Prefuse

toolkit (Heer et al., 2005)), see Figure 1. They are

brieﬂy discussed in the following.

Settings Panel. The user can change the visual ap-

pearance of the tool, generate random network and

centrality data for testing purposes, etc. via the con-

trols in the settings panel.

Circle View. The circular network drawing in the

center displays the nodes, their centrality values, and

the graph structure itself. Therefore, this view pro-

vides an overview of the complexity of the entire net-

work and supports the user to get the main actors at

a ﬁrst glance. Our tool offers two possible layouts

of a node’s centrality representation (called centrality

bar in this paper): traditional stacked bars and maxi-

mum value stacking. Whereas for traditional stacking

the single bars corresponding to centrality values are

immediately stacked onto each other, the maximum

value stacking starts all bars from the level of the max-

imum value of the current centrality, thus providing a

better comparability of relative centrality values. Fig-

ure 1 shows the differences between the two modes.

Histogram View. One individual histogram pro-

vides a statistical overview of a centrality’s values.

Thus, the histograms help the user to better under-

stand the distributions of centrality values over all

nodes.

Centrality Hover View. The single centrality bars

are not only displayed in the circle view; a selected

bar is redundantly visualized together with detailed

information about the corresponding node’s central-

ity values, i.e., centrality name, relative percentage,

and absolute value. Figure 1 shows this view display-

ing data of the currently hovered node 44 in the lower

right area of the main window.

4.1 Interaction-concepts in ViNCent

The aforementioned views provide the user with the

possibility of doing further interactions, such as hov-

ering bars and ﬁltering out data based on the distribu-

tion of centrality values as well as the network topol-

ogy.

4.1.1 Linking and Brushing

ViNCent makes extensive use of linking and brushing

features (Keim, 2002; Roberts, 2007) to connect cer-

tain data objects in the visualization. Hovering fea-

tures are introduced to highlight elements and show

cross-connections:

Hovering in the Circle View. The main focus of

the user is usually on the circle view in the center,

cf. Figure 1. Hovering nodes in this window leads to

an activation of the connected nodes (their neighbor-

hood), the connecting edges, and the corresponding

labels. By the use of the settings panel, the user can

control how many hierarchy levels in the network the

highlighting spreads out in the view. This feature of

highlight spreading highlights nodes and correspond-

ing edges weaker and weaker depending on the hier-

archy level.

Hovering in the Histograms View. To show the

user which nodes fall into which certain range of data

IVAPP 2012 - International Conference on Information Visualization Theory and Applications

706

Figure 1: Overview of the ViNCent tool. The center view shows the radial drawing of a network. The two possible drawing

modes (normal stacked bars (left half) and maximum value stacking (right half)) are shown as overlay (split by the blue line).

To the right, the corresponding histograms of the network centralities are shown (top) as well as more detailed information

about the currently selected node 44 (bottom). Histograms can be used to ﬁlter the views. To the left, the settings panel allows

the user to change tool parameters and to generate data sets for testing purposes.

values according to one centrality measure, hover-

ing an individual histogram bar highlights the corre-

sponding nodes in the circle view as well. Thus, the

user can check in more detail, which nodes are related

to a histogram bin and would be affected by ﬁltering

them out.

4.1.2 Filtering and Dynamic Queries

The exploration of big datasets is mostly not possible

without ﬁltering of data and dynamic queries (Shnei-

derman, 1994). ViNCent uses several approaches to

allow the user to ﬁlter out data and therefore to re-

duce the dataset to a smaller amount of nodes. In this

way, ﬁlters support the user in fulﬁlling his/her tasks

more efﬁciently. Since the centrality data correlated

to the network is multidimensional, we decided to use

an approach that was originally realized by Attribute

Explorer (Tweedie et al., 1994). It maps each node

attribute (i.e., its centrality values) to a histogram.

Filtering processes in ViNCent are based on the

distribution of centrality values over the nodes. In our

case, ﬁltering out nodes means ﬁltering out the corre-

sponding edges in the circular view as well in order to

clear the center of the visualization up and to reduce

the visual clutter. Hereby, ViNCent supports four dif-

ferent ﬁlter options:

Filtering based on Histogram Bars. The ﬁrst pos-

sibility is to ﬁlter out elements belonging to a speciﬁc

bar of a histogram. By clicking the bar, the corre-

sponding elements in the circle view are hidden. The

bar in the histogram is therefore marked in light gray

color, which symbolizes that this bar has been ﬁltered

out, see Figure 2(c).

Filtering based on Histogram Sliders. Another

way of hiding elements is to apply the range sliders

below each histogram. Figure 1 (right) shows ex-

amples of range sliders for several centralities. By

sliding from the left or right, the amount of dis-

played nodes decreases corresponding to the elimi-

nated elements in the dataset. This method is useful

to quickly ﬁlter out minimum and maximum values

of histograms.

Filtering based on Single Nodes. As the ﬁlters pre-

sented before act on the whole set, they affect more

nodes at once. But to ﬁlter out single nodes from

the view, our penultimate ﬁlter option can be use-

ful, which is based on single nodes. A right-click

VINCENT - Visualization of Network Centralities

707

(a) ’Centroid’ C

cen

. (b) ’Degree’ C

deg

ecc

. (d) ’Radiality’ C

rad

Figure 2: Histograms for four different centralities. Fig-

ure 2(c) has an active ﬁlter on the ﬁrst bar, shown in light

gray. This active ﬁlter spreads to the other histograms, dis-

played in dark gray.

on nodes in the circle view causes that they disappear

from the view. Renewed right-clicking brings them

up again.

Hiding Centralities from the Circle View. Some-

times, certain centrality values—and therefore the

whole centrality—have no further meaning for the ex-

ploration of the dataset. For instance, if all nodes have

the same centrality value. In this case, the central-

ity can be hidden by deselection of the corresponding

visibility check box, see Figure 1 (right).

Filter Propagation. Filtering out elements affects

directly the circle view; Figure 3 shows the results of

ﬁltering processes performed by the user. Thus, di-

rect feedback based on such actions is provided to the

user. But to better keep track of already eliminated

elements, they are marked in the histograms as light

gray bars, when the ﬁlter is applied directly on this

histogram. The bars are marked in dark gray, when

the ﬁlter spreads from another active ﬁlter in a differ-

ent histogram. This ﬁlter propagation gives the user a

more precise feedback of how certain centrality val-

ues are related to each other, cp. Figure 2.

4.2 Implementation Aspects

The advantages of our tool are its capabilities con-

cerning the interactive exploration of the nodes and

the display of a number of different centrality values

at the same time. These features are mainly achieved

by the circular arrangement of nodes and the addi-

tional edge bundling approaches done in the middle

of the circle view. The latter support the user in ﬁnd-

ing connections between nodes and showing if nodes

are highly or lowly connected. In the following, we

discuss the most important technical aspects of these

features.

For the circular arrangement of the nodes, the en-

tire available space in the view is taken for drawing

Figure 3: Active ﬁlter on the network. Nodes have been ﬁl-

tered out by using different ﬁlter options of the histograms.

The active eccentricity histogram bar ﬁlter eliminated all

nodes having a low value in this centrality. As Figure 2(c)

shows, about 55 nodes are affected by this active ﬁlter.

the inner circular disk and the outer circular ring. Be-

tween both, the nodes are added and represented as

small squares. Their degree is represented by a color

gradient from light-red to dark-red (connection degree

marker) and their centrality values as bar drawings

on the outer ring. The arrangement is done by sim-

ply dividing the whole 360

◦

circle into single-angle

steps, used to deﬁne the positions of the nodes. In

order to distinguish between single centrality values,

a speciﬁc color schema is employed. ViNCent uses

a color schema provided by ColorBrewer (Brewer, 3

22). Thus, a centrality gets assigned a speciﬁc color

from this schema which is consistently used in all

views.

Graph drawing techniques usually have to deal

with occlusions and visual clutter when it comes to

more dense graphs. This is prevalently the case in

the area of network analysis, especially for biolog-

ical networks. In order to reduce visual complex-

ity in our views, the circular arrangement of nodes

avoids occlusions of them. However, there is still the

problem of visual clutter in the center of the circle

view where the connecting edges are located. One

technique to solve this problem is hierarchical edge

bundling (Holten, 2006). It basically follows a simple

principle: visually bundling adjacency edges together

analogous to the way electrical wires and/or network

cables are merged into bundles along their joint path.

ViNCent deals with graphs as well, but our tool draws

all nodes to the outside of the circle. Therefore, no hi-

IVAPP 2012 - International Conference on Information Visualization Theory and Applications

708

erarchy can be used in its center to perform standard

edge bundling based on the inner hierarchy. Instead,

ViNCent supports four different edge bundling modes

that are explicitly described in the thesis (K

ostinger,

2011). Similar approaches exist in the literature, such

as those described in (Correa et al., 2008).

4.2.1 Plug-in: CentiBiN

ViNCent is actually not limited to a certain ﬁeld of

application. It can handle every network, if it is rep-

resented in GraphML container format. Our tool also

accepts precomputed (numeric) centrality values that

are stored in the input ﬁle as additional attributes.

Then, ViNCent can directly visualize the multivariate

network without any preprocessing steps. An excerpt

from such an extended GraphML ﬁle is given in the

following:

...

<key id="Centroid" for="node"

attr.name="Centroid" attr.type="double">

</key>

...

To support more application domains—especially

biological network analysis—ViNCent uses the Cen-

tiBiN plug-in (Junker et al., 2006) that calculates up to

17 network centralities for biological networks. After

loading the input graph in GraphML format, the Cen-

tiBiN plug-in can calculate a user-speciﬁed number

of centralities on directed and undirected graphs, e.g.,

degree, eccentricity, etc. Then, ViNCent visualizes

the input graph together with the computed centrality

values. Additionally, the user can export all data into

an extended GraphML ﬁle. Thus, users are able to

reload the network together with its centrality values

without recalculation. Note that not every centrality

measure can be applied to every graph, since there

are preconditions a graph has to fulﬁll in order to cal-

culate the values. These can be simplicity, connect-

edness and loop-freeness (Junker et al., 2006). One

example is the Eigenvector-centrality whose imple-

mentation in CentiBiN requires that the input graph

has to be loop-free.

5 USE-CASE SCENARIO AND

DISCUSSION

Before we discuss the pros and cons of ViNCent, a

short use-case scenario shows how the tool can be

used for biological network analysis. It is described

in the following section.

5.1 Use-case Scenario

We use the release 2005-01-26 of the Mus muscu-

lus dataset from the Database of Interacting Proteins

(DIP) (Salwinski et al., 2004) as an example for in-

vestigating biological networks. It describes protein-

protein interactions (PPI) of the house mouse and

consists of 49 nodes and 54 edges. The aim is to ﬁnd

the most important proteins by visualizing their net-

work centralities with our tool.

Figure 4(a) shows the selected dataset for this use-

case. Protein 320N, which has the highest overall cen-

trality values, is selected and its adjacent proteins are

highlighted. Detailed centrality numbers for the se-

lected protein are displayed in the hover panel on the

bottom right part of the ﬁgure. However in this case,

most of the adjacent proteins of 320N have low cen-

trality values and might not be of interest.

As already discussed before, centrality values are

usually used to identify the importance of proteins in

a biological network. To ﬁnd other important hubs

in the network, the user may modify the range slider

ﬁlters to hide all nodes with a small centrality value,

by sliding a bar from the left to the right until only a

small number of proteins remain for the selected cen-

trality value. For our use-case, the betweenness cen-

trality has been chosen as it relies on the overall net-

work topology. High values of this centrality indicate

central proteins in a PPI-network.

Figure 4(b) shows an example, where the purple

colored Current Flow Betweenness (equivalent to the

Random Walk Betweenness discussed in Section 2.2)

has been adjusted. Protein 369N, which is the cellular

tumor antigen p53, is selected here, as it has one of the

highest betweenness values and most of the remain-

ing visible nodes are adjacent to this protein and have

high centrality values as well. Therefore, they could

be important actors in the Mus musculus dataset. For

instance, the adjacent protein 24169N, a tumor sup-

pressor protein, might represent an important hub in

the PPI-network and could be an interesting candidate

for further investigation.

This small example shows an application of cen-

trality measures in biological networks and how ViN-

Cent can solve analysis problems by visualizing the

networks interactively.

5.2 Discussion

Our visualization tool combines different approaches

to overcome the difﬁculties when visualizing network

centralities. It provides a new way of visualizing cen-

trality values within a network by the use of a circular

arrangement, and therefore, it minimizes visual clut-

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709

(a) Protein 320N was selected for further consideration.

(b) Filtered view using histogram sliders of the ViNCent tool, showing the top proteins found by adjusting the

CurrentFlowBetweenness. The ﬁrst few bars of the other centralities are marked in dark gray, indicating that

the corresponding nodes are already ﬁltered out by the applied ﬁlter.

Figure 4: Use-case scenario of ViNCent. Filtering out important proteins in the Mus musculus dataset.

ter and occlusions of nodes. The usage of stacked

bars attached to the outside of the node representa-

tions gives the user the possibility to discover poten-

tially important nodes (e.g., with high centrality val-

ues) at a ﬁrst glance. The visual representation of the

degree marking additionally supports this approach.

Another feature of ViNCent, which improves in-

teractive exploration, is the way of ﬁltering out data.

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By the use of histograms and thus the underlying dis-

tributions of network centrality values, the user can

quickly get an overview and ﬁlter out unimportant

data with a few clicks. The concepts of bar ﬁltering,

range sliders and single-node ﬁltering allow the user

to ﬁlter out any combination of nodes. This leads to

less visual clutter in the drawing. We brieﬂy summa-

rize the advantages in the following and highlight the

most important drawbacks:

Pros: Advantages are the circular arrangement

of nodes, which leads to less visual clutter and allows

the visualization of many nodes up to a few hundred.

Other advantages are the ﬁlter possibilities that facil-

itate the exploration of networks and support the user

in ﬁltering out less important data at a ﬁrst glance.

The presented example shows how easy ﬁlters can

be applied and how powerful they are when the user

wants to reduce the amount of displayed data.

Cons: Current drawbacks are the lack of linking

and brushing between histograms when hovering sin-

gle bars (only already ﬁltered out data is linked and

brushed) and the missing visual links from the cir-

cle view to the histogram windows (so far, just hover-

ing in histograms highlights nodes in the circle view).

Especially the last issue would enhance the ﬁlter pos-

sibilities even more (e.g., hovering nodes in the cir-

cle view would show the location of the node in the

histogram). Another drawback is the current perfor-

mance: when dealing with a lot of data, the perfor-

mance of the tool is not sufﬁcient to maintain the full

interaction possibilities. Linking and brushing fea-

tures are then lacking of immediate feedback and lead

to bad refreshing results for ﬁltering tasks.

6 CONCLUSIONS AND FUTURE

WORK

ViNCent can be used for any kind of network explo-

ration and any kind of centrality visualization as long

as the preconditions for the calculation of centrality

values are met. Thus, it also supports a better un-

derstanding of multivariate networks. Compared to

related tools, ViNCent performs well in visualizing

centrality values for nodes, as it provides a direct vi-

sual feedback by the use of different types of stacked

bars. ViNCent still reveals the network structure and

makes it therefore possible to follow paths in a net-

work too. A number of interaction concepts support

the mentioned features.

The tool scales well up to a few hundred nodes,

depending on the available screen size and resolution.

The size of the inner circle limits the number of nodes.

However, there are features that are not implemented

yet, such as the linking and brushing issue discussed

in the previous section. In the following, we discuss

possible future work by indicating further improve-

ments.

An important future feature are new edge bundling

modes, since they would lead to less visual clutter

in the circle view. There is a lot of work for edge

bundling in graphs, such as (Holten, 2006; Holten

and van Wijk, 2009). As some approaches do not

rely on any further structure in the center of the cir-

cle view, they would be applicable to solve the edge

bundling problem for ViNCent. As the computation

of bundling edges is done in one single point so far,

additional bundling modes could be introduced into

the system.

Closely related to the problem of edge bundling is

the arrangement of nodes on the circle itself. Depend-

ing on the node positions, edge bundling may produce

better results. For this problem, one could take re-

sults of network analyses into account, like informa-

tion concerning cliques or communities, single actors,

or the density of the graph at certain positions. This

information is useful to decide how nodes should be

arranged along the circle, because based on this, we

could calculate the possible amount of edge crossings.

So far, ViNCent uses the Prefuse toolkit (Heer

et al., 2005) to render our visualizations. As

this toolkit relies on Java2D instead of faster Java

OpenGL implementations, the tool has some per-

formance issues. Changing to a faster visualization

toolkit may lead to better results.

Finally, the tool should be tested and evaluated

with more complex biological networks and difﬁcult

tasks as well to clearly ﬁgure out problems of the cho-

sen interaction and visualization techniques.

ACKNOWLEDGEMENTS

The authors wish to thank Ilir Jusuﬁ, ISOVIS Group,

Linnaeus University, as well as Falk Schreiber, Leib-

niz Institute of Plant Genetics and Crop Plant Re-

search (IPK), Germany, for many constructive com-

ments and their contributions to the system.

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