Exploration of Component Diagrams with Multifocal Highlighting

Ladislav Cmolik

1,2

and Lukas Holy

Faculty of Electrical Engineering, Czech Technical University in Prague, Technicka 2, 166 27 Prague, Czech Republic

NTIS - New Technologies for the Information Society, Faculty of Applied Sciences, University of West Bohemia,

Univerzitni 8, 306 14 Pilsen, Czech Republic

Keywords:

Graph exploration, Highlighting, Component Diagram.

Abstract:

In the paper we present multifocal highlighting in reverse engineered component diagrams to support software

engineers in answering questions on relations between a number of components. With component oriented

systems such questions arise quite often. We use color to highlight all components relevant to selected focus

components. Further, we allow the users to ﬁlter the diagram. Our approach, unlike the state-of-the-art meth-

ods allows analysis of relations between dozens of components. We have performed an user study to evaluate

our multifocal highlighting. The results of the subjective evaluation show that the multifocal highlighting

supports software engineers in answering questions on relations between components.

1 INTRODUCTION

Software engineers maintaining a component oriented

software repeatedly deal with questions on relations

between several components: What functionality of

the system may be broken if certain component/s do

not work properly? If several components do not

work properly, what is the functionality they all re-

quire (as this is the potential source of the problems)?

The architecture of a component oriented system

is typically visualized as a component diagram. A

component (a node of the diagram) provides certain

functionality to other components and may require

functionality from other components. Often several

components cooperate together to provide a higher

level functionality. A component is seen as a black

box, in the system. A component can be replaced with

another one providing the same functionality. Unfor-

tunately, the number of relations that a component has

with other components is typically high, therefore the

component diagram is not a sparse graph.

Visualizations of graph data-sets often aspire to

present the user with the whole graph. In other words,

to ﬁt the whole graph into the available space on the

screen. However, if the graph is large then the user see

mainly the global structure of the graph, but cannot

distinguish between the individual nodes and edges.

This makes even very basic tasks such as locating

certain node or following and edge almost impossible

(Shneiderman and Aris, 2006).

To reduce the structural complexity we can uti-

lize clustering techniques. However, the clusters are

typically formed based on the topological structure of

the graph. In the case of the component diagram of a

software system the topological structure can be very

different from the semantics of the graph. In conse-

quence also the resulting clusters do not reﬂect se-

mantics of the graph which can be confusing for the

user. Therefore, we rather provide the user with tools

to explore the graph as it is.

We can solve the problem with limited screen

space by zoom and pan, however in such case we

introduce temporal separation. When we zoom out

we cannot distinguish between the individual nodes

and edges and when we zoom in it is hard to see the

broader structure of the graph and follow long edges.

In consequence we will very often zoom in and then

again zoom out while concentrating to not loose the

desired nodes and edges from our sight.

The main contribution of the paper is the multifo-

cal highlighting that allows arbitrary number of focus

nodes. We use color to highlight the relevant com-

ponents. The multifocal highlighting is a relatively

simple technique that is easy to implement, yet allows

the users to solve a number of different tasks needed

when analyzing component diagrams. Results of our

user study conﬁrm that the users are able to ﬁnd an-

swers to such tasks in short time when the multifocal

highlighting is utilized. Further, they reported that the

multifocal highlighting supported them in solving the

tasks.

148

Cmolik, L. and Holy, L.

Exploration of Component Diagrams with Multifocal Highlighting.

DOI: 10.5220/0005781301460152

In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 2: IVAPP, pages 148-154

ISBN: 978-989-758-175-5

Figure 1: Reverse engineered component diagram of Nuxeo

system (consisting of 203 components) depicted in notation

of Holy et al. (2012) . Layout of the nodes is determined by

force-based layout of Kamada and Kawai (1989) .

When combined with ﬁlters the multifocal high-

lighting, unlike the state-of-the-art methods, can be

used to analyze mutual relations between many com-

ponents. In the paper we use this approach to analyze

subsystem composed from 58 components, which

yields to 58 focus nodes.

Further, the components can be ﬁltered based on

software metrics. Software metrics allow us to ﬁlter

the diagram based on information that is not encoded

directly in the diagram (e.g., reliability of the compo-

nents or coverage by unit tests).

In this paper we use reverse-engineered compo-

nent diagrams (hundreds of nodes) of component

based software systems to demonstrate our approach

(see Figure 1 for an example). Nevertheless, we be-

lieve that the presented approach can be adapted to

any kind of graph.

2 RELATED WORK

In this section we present work related to the graph

exploration. We expect that the graph has a reason-

able layout and thus we do not focus on this area. A

multitude of automatic layout algorithms have been

developed (see bibliography by Battista et al. (1994)

or handbook by Tamassia (2013)). In our implemen-

tation we are using force-based layout of Kamada and

Kawai (1989) implemented on GPU. We focus mainly

on existing techniques that can be utilized to explore

relations of one or several focus nodes.

Focus+Context. The Focus+Context techniques

can be divided into two categories. In the ﬁrst cate-

gory are techniques that are built directly into the cal-

culation of the layout of the graph. Such technique is

the layout of a graph in hyperbolic space (Munzner,

1997). However, in the hyperbolic space only one fo-

cus region is possible.

In the second category are techniques that de-

form the space on which is the graph rendered af-

ter the layout is calculated. These techniques are

independent of the layout algorithm. Basic exam-

ple of such technique is ﬁsh-eye deformation used by

Sarkar et al. (1992). Their ﬁsh-eye deformation is

based on degree-of-interest and it is an extension of

generalized ﬁsh-eye views by Furnas (1986).

Later, the ﬁsh-eye deformation was extended for

multiple foci by Sheelagh et al. (1996) and Kea-

hey et al. (1996). However, even with the multiple

focus regions it is hard to track relations between the

nodes in the focus regions.

Movable Filters. Another category of tools to ex-

plore graphs are movable ﬁlters also known as lenses.

Movable ﬁlters are rectangular or circular area where

information is ﬁltered out or emphasized. By overlay-

ing several movable ﬁlters we can combine their ef-

fects. Note that movable ﬁlters used to explore graphs

are also Focus+Context techniques where the focus

are edges or nodes inside of the ﬁlter area.

Wong et al. (2003) are using circular movable ﬁl-

ter to separate close edges with ﬁsh-eye deformation.

Hurter et al. (2011) and Panagiotidis et al. (2011) use

movable ﬁlters to bundle and unbundle edges of the

graph. Tominsky et al. (2006) are using movable ﬁl-

ter to ﬁlter out edges that are not incident with nodes

in the area of the ﬁlter.

Further, Tominsky et al. (2006) are using circu-

lar movable ﬁlter to bring neighbors of one or sev-

eral nodes of the graph in the area of the ﬁlter into

the area by changing positions of the neighbors in

the graph layout. Similar technique was applied by

Moscovich et al. (2009) with the difference that the

whole screen was used as the area of the ﬁlter. Unfor-

tunately, these approaches cannot be easily extended

to work with multiple foci in order to track relations

between the focus nodes.

Highlighting. Static highlighting (e.g., with color)

of information relevant to one selected node is quite

common. Barsky et al. (2007) is using color to high-

light neighbors of a node in network representing bi-

ological system or process. Holy et al. (2013) is using

color to highlight neighbors of a node in a compo-

nent diagram of software system. Unfortunately, with

Exploration of Component Diagrams with Multifocal Highlighting

149

Figure 2: Visually emphasizing interacting components (yellow) of the focus component (red) in the diagram (shown for two

different components) does not help to identify components that interact with both selected components quickly.

highlighting that allows only one focus node it is still

very hard to gain insight into relations between more

than one node (see Figure 2 for an example).

Heer and Boyd (2005) allow speciﬁcation of two

focus nodes and use color to highlight the neighbors

of both nodes. Ware and Bobrow (2005) use both

static (with color) and motion highlighting to high-

light neighbors of two nodes in artiﬁcial graphs of

varying complexity. By performing a user study they

found out that by using color to highlight neighbors of

the ﬁrst focus node and motion to highlight neighbors

of the second focus node the users are able to identify

which nodes are neighbors of the ﬁrst node, neigh-

bors of the second node and neighbors of both focus

nodes. Unfortunately, extension of this approach for

more than two focus nodes is not possible.

Byelas and Telea (2006) use color to highlight

several sets of interest in software architecture di-

agrams. An area containing all nodes of each set

is constructed and ﬁlled or outlined with a chosen

color. All areas are rendered underneath the visual-

ized graph. Nodes belonging to several areas can be

identiﬁed when the areas are outlined, or ﬁlled with

semitransparent color. However, if the shapes of the

areas are complex or the number of areas is high it

can be difﬁcult to identify nodes belonging to several

sets.

Glyphs. Glyphs are graphical objects that convey

multiple data values. Here we focus on glyphs that

support identiﬁcation of nodes belonging to several

overlapping sets in the graph. Termeer et al. (2005)

use colored icons to indicate that nodes belong to sev-

eral sets. The number of icons corresponds with the

number of sets. An icon is displayed if the node be-

longs to the set associated with the icon and hidden

otherwise. Vehlow et al. (2013) and Liu et al. (2014)

are using pie-charts to depict nodes that belong to sev-

eral sets. However, if the number of overlapping sets

is high (e.g., higher than 8) than it is hard to quickly

identify all nodes belonging to the desired sets.

3 MULTIFOCAL HIGHLIGHTING

In this section we present static highlighting that is

using color to visually emphasize relevant neighbor

nodes of focus nodes in the graph. We allow arbi-

trary number of focus nodes, although usually only

low number of nodes is needed to support answering

questions stemming from our use cases. We demon-

strate our approach on reverse-engineered component

diagrams of a component system. In the following

text we will refer to the nodes of the graph as compo-

nents.

Our objective is to support rapid visual identiﬁca-

tion of components that interact with all focus com-

ponents. We share the opinion of Ware and Bo-

brow (2005) that the most common task is to iden-

tify if two or several subsets of the diagram have

nodes/components in common. Further, we aim to

provide information also about those components that

do interact at least with one (but not with all) of the

focus components.

Our approach is based on assigning importance to

each component in the diagram. The visual emphasis

of component C

is driven by importance I

∈ [0, 1]

calculated as

(1)

where N

is the number of focus components that in-

teracts with component C

and N

is the total num-

ber of focus components. For each focus component

the user can specify which edges will be considered

in calculation of N

: edges representing functionality

that is provided to other components, edges represent-

ing functionality that is required from other compo-

nents, or both types of edges.

We map the importance on color of the compo-

nents in the diagram. As people cannot perceive dif-

IVAPP 2016 - International Conference on Information Visualization Theory and Applications

150

(a)

(b)

Figure 3: (a) Multifocal highlighting of two selected components. The components interacting with all focus components

are emphasized (orange). (b) Side panel utilized to display software metrics (1), the user can choose desired metric using

tabs (2) and specify low and high thresholds for the metric with range slider (3). The graph shows the result of ﬁltering out

components with low connectivity to other components (M1) and high coverage of code with Unit tests (M2) from component

diagram of Nuxeo system. We show here the used metrics and thresholds side by side.

ference in color hue precisely (Baker and Wickens,

1995) we use only four colors: red for focus com-

ponents, orange for components interacting with all

focus components, yellow for components interacting

with at least one of the focus components (we denote

the orange and yellow components as context of the

focus components), and green for components that

do not interact with any focus component (we denote

these components as components outside of the con-

text).

To reduce visual clutter and make the important

components and the edges between them more promi-

nent we render the components and edges in speciﬁc

order:

1. We render the edges that are not incident with any

focus component.

2. We render the components outside of the context.

3. If the number of focus components is not zero we

render white semitransparent layer over the whole

diagram otherwise we end the rendering process.

4. We render edges between the components in the

context and the focus components.

5. We render components in the context.

6. We render edges between the focus components

in red color.

7. Finally we render the focus components.

We use painters algorithm to render the compo-

nents, the edges and components that are rendered

later are rendered over those rendered previously

which ensures their visibility. Step 3 visually sup-

presses the edges and components rendered in Step 1

and Step 2.

The multifocal highlighting not only allows to vi-

sually emphasize context of a focus component (see

Figure 2), but also to emphasize components that are

interacting with two or more components without in-

troducing spatial or temporal separation (see Figure

4(d)).

To provide additional information about the com-

ponents in the context we visualize their importance

as a length of horizontal bars that are displayed in the

side panel (see Figure 4(f)) and allow the users to ﬁlter

the components according to the importance. We map

the importance on the length as difference in length is

perceived by people much more precisely than differ-

ence in color (Baker and Wickens, 1995).

4 FILTERS

We allow the users to ﬁlter the diagram according to

different software metrics. A metric is a number as-

sociated with each component that expresses certain

characteristic. In our prototype we use two types of

software metrics. The software metrics of the ﬁrst

type are connected to the structure of the diagram.

We are using two such metrics: connectivity to other

components and connectivity to focus components.

Note that the later metric is in fact the importance

calculated with Equation 1. The software metric of

the second type provides additional information that

is not encoded in the diagram. As a proof of concept

we use coverage of the code with unit tests.

Components can be ﬁltered out from the side

panel based on any given metric, using one or two

thresholds - high and low values of the selected met-

ric. In the ﬁlters panel each metric is represented as a

tab where the components names are sorted according

the given metric. The value of the metric is visualized

Exploration of Component Diagrams with Multifocal Highlighting

151

(a) (b) (c)

(d) (e) (f)

Figure 4: In all ﬁgures we display only the connected part of the ECM subsystem: with all components (a), with connected

components (b), only the focus nodes (c). In the bottom ﬁgures we display only components that are connected to 1 focus

component (d), to 3-5 focus components (e), and to 18-19 focus components (f).

as a length of a horizontal bar. The color of the bar

matches the color of the component in the diagram

which helps to locate the important components in the

diagram quickly.

For example, ﬁltering out components with low

degree of connectivity to other components will leave

only the tightly connected core of the system visible.

Several metrics can be combined together to ﬁlter the

graph. For example, if we want to improve the unit

tests then the components used by many other com-

ponents, but with the low unit test coverage are good

candidates to start with (see Figure 4(f)).

In situations when there is a need to analyze mu-

tual relations between many components the user can

use the connectivity to focus components and the two

thresholds to ﬁlter all components except those inter-

acting with the desired number of focus components.

By sequentially increasing both thresholds the soft-

ware engineer can analyze relations between the fo-

cus components, component interacting only with one

focus component, components interacting with two

focus components, and so on. At the same time the

number of displayed nodes an edges is signiﬁcantly

decreased. For analysis of ECM subsystem (com-

posed from 58 components) of the Nuxeo system see

Figure 4.

5 EVALUATION

We have evaluated the multifocal highlighting with

an user study. We have chosen the tasks to represent

real-life situations (e.g., ﬁnd components that inter-

act with one or two speciﬁc components, ﬁnd depen-

dencies of a subsystem composed from several com-

ponents). We have performed subjective evaluation

where the participants rate the effect of the multifocal

highlighting in terms of conﬁdence, speed, comfort,

and support with regard to the solved tasks.

Six participants were recruited from our univer-

sity. All were daily users of computers and have ba-

sic experience with analysis of component diagrams.

They ranged from 25 to 30 years (Mean = 27.33, SD

= 1.8).

The hardware consisted of standard PC computer

with 24 inches LCD display (resolution 1680 1050

pixels), keyboard and optical mouse with 2 buttons.

The test was performed on reverse engineered compo-

nent diagram of Nuxeo system (see Figure 1) consist-

ing of 203 components depicted in notation of Holy

et al. (2012). Layout of the nodes was determined by

force-based layout of Kamada and Kawai (1989).

Before the experiment the moderator explained

the GUI of our application and the tasks. The ex-

periment began with a training session. The training

session consisted of 7 tasks similar to those in the test,

but performed on different components. The goal was

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152

to let participants to get familiar with the multifocal

highlighting, to get used to the experiment procedure

and to minimize any learning effects. Training ses-

sion was followed by the test where completion time

was measured for each task. The test was consisting

of 7 tasks:

1. Which components are connected to Management

part (*management) of the system?

2. Which dependencies are not mutual for *HTML*

components?

3. Which components are needed by every NXFile-

Manager* component?

4. Which components are connected to Nuxeo ECM

LDAP Directory component?

5. How many components are connected to both

org.nuxeo.ecm.core.storage.sql.management and

org.nuxeo.runtime.management?

6. Which components are connected to Nuxeo ECM

Search API but not to NXFileManager?

7. Which components are removed from common

dependencies when adding component Nuxeo

Platform Expression Language to the set of NX-

Audit Core and Nuxeo Audit API components?

Participants were asked to proceed as quickly and ac-

curately as possible. Between each task the partici-

pants were allowed to take short breaks. Participants

were interacting with the application with mouse.

After the test, participants completed a question-

naire investigating their subjective judgment about the

level of conﬁdence, speed, comfort, and support for

the given tasks. Likert scale 1-5 was used for the sub-

jective evaluation where 5 is the most positive mark

(e.g., the user thinks that the tool helped him to be

absolutely conﬁdent when answering the questions),

3 is a neutral mark, and 1 is the most negative mark

(e.g., the user thinks that the tool did not helped him

at all to be conﬁdent when answering the questions).

6 RESULTS AND DISCUSSION

All 6 participants successfully completed all 7 tasks.

We report the results of our user study in Table 1.

In the following text we report arithmetic mean

and standard deviation (SD) for the subjective evalu-

ation. For the completion times we report geometric

mean and 95% conﬁdence interval calculated accord-

ing to Sauro et al. (2012) these methods give the best

estimates for completion times when the number of

participants is lower than 25.

Table 1: Results of our user study. Completion times (in

seconds) needed by participants P1-6 to complete the in-

dividual tasks and cumulative time needed to complete all

tasks (T1-7). Results of subjective evaluation as reported by

the participants. Likert scale 1-5 was used.

P1 P2 P3 P4 P5 P6

Task 1 173 58 80 74 67 133

Task 2 32 121 54 84 215 155

Task 3 40 24 37 25 39 33

Task 4 26 23 30 31 23 47

Task 5 16 9 39 42 20 37

Task 6 74 107 34 59 66 148

Task 7 37 24 67 54 35 62

T1-7 398 366 341 369 465 615

Conﬁd. 5 2 5 4 3 4

Speed 4 4 4 4 4 3

Comfort 5 3 4 3 2 3

Support 5 5 5 5 4 5

In average the participants have strongly agreed

(Mean = 4.83, SD = 0.37) that the multifocal high-

lighting provided support for solving the tasks. In

terms of the conﬁdence (Mean = 3.83, SD = 1.06),

speed (Mean = 3.83, SD = 0.37), and comfort (Mean

= 3.33, SD = 0.94) the participants were in average

positive on those aspects of the multifocal highlight-

ing.

We report the average times and the 95% conﬁ-

dence intervals in seconds. Task 1: Mean = 89.94s,

95% CI (57.4s,140.8s), Task 2: Mean = 91.46s, 95%

CI (43.7s,191.5s), Task 3: Mean = 32.33s, 95% CI

(25.5s, 41s), Task 4: Mean = 29.05s, 95% CI (21.9s,

38.5s), Task 5: Mean = 23.64s, 95% CI (12.4s,45.1s),

Task 6: Mean = 73.3s, 95% CI (43s,124.8s), and

Task 7: Mean = 43.71s, 95% CI (28.8s,66.2s). The

average time needed to complete all 7 tasks: Mean =

416.79s, 95% CI (331.5s,524s).

7 CONCLUSION

In this paper we have proposed multifocal highlight-

ing of nodes in a graph to help users quickly ex-

plore mutual neighbors of several nodes. For situ-

ations when users need to explore mutual relations

and neighbors of dozens of nodes we provide ﬁlter-

ing of the components based on the number of con-

nections to the focus components. Further we allow

the users ﬁlter the diagram based on software met-

rics. We have demonstrated our approach on reverse

engineered component diagrams of component ori-

ented software system where questions regarding mu-

tual neighbors of several components arise very often.

We have evaluated the multifocal highlighting with

6 software engineers. The results of the subjective

Exploration of Component Diagrams with Multifocal Highlighting

153

evaluation show that our multifocal highlighting sup-

ports the engineers in solving tasks related to identify-

ing the mutual neighbors of several nodes. In the fu-

ture work we would like to perform comparative user

study with various different techniques.

ACKNOWLEDGEMENTS

This publication was supported by the project

LO1506 and by the Aktion OE/CZ grant number 68p5

of the Czech Ministry of Education, Youth and Sports.

REFERENCES

Baker, M. P. and Wickens, C. D. (1995). Human factors in

virtual environments for the visual analysis of scien-

tiﬁc data. Technical report, Citeseer.

Barsky, A., Gardy, J. L., Hancock, R. E., and Munzner, T.

(2007). Cerebral: a cytoscape plugin for layout of and

interaction with biological networks using subcellular

localization annotation. Bioinformatics, 23(8):1040–

1042.

Byelas, H. and Telea, A. (2006). Visualization of areas of

interest in software architecture diagrams. In Proceed-

ings of the 2006 ACM symposium on Software visual-

ization, pages 105–114. ACM.

DiBattista, G., Eades, P., Tamassia, R., and Tollis, I. (1994).

Annotated bibliography on graph drawing algorithms.

Computational Geometry: Theory and Applications,

4(5):235–282.

Furnas, G. W. (1986). Generalized ﬁsheye views. In

Proceedings of the ACM SIGCHI ’86 Conference on

Human Factors in Computing Systems, pages 16–23.

ACM.

Heer, J. and Boyd, D. (2005). Vizster: Visualizing online

social networks. In Information Visualization, 2005.

INFOVIS 2005. IEEE Symposium on, pages 32–39.

IEEE.

Holy, L., Jezek, K., Snajberk, J., and Brada, P. (2012).

Lowering visual clutter in large component diagrams.

In Information Visualisation (IV), 2012 16th Interna-

tional Conference on, pages 36–41. IEEE.

Holy, L., Snajberk, J., Brada, P., and Jezek, K. (2013). A

visualization tool for reverse-engineering of complex

component applications. In Software Maintenance

(ICSM), 2013 29th IEEE International Conference on,

pages 500–503. IEEE.

Hurter, C., Telea, A., and Ersoy, O. (2011). Moleview:

An attribute and structure-based semantic lens for

large element-based plots. Visualization and Com-

puter Graphics, IEEE Transactions on, 17(12):2600–

2609.

Kamada, T. and Kawai, S. (1989). An algorithm for draw-

ing general undirected graphs. Information processing

letters, 31(1):7–15.

Keahey, T. A. and Robertson, E. L. (1996). Techniques for

non-linear magniﬁcation transformations. In infovis,

page 38. IEEE.

Liu, S., Wang, X., Chen, J., Zhu, J., and Guo, B. (2014).

Topicpanorama: A full picture of relevant topics.

In Visual Analytics Science and Technology (VAST),

2014 IEEE Conference on, pages 183–192.

Moscovich, T., Chevalier, F., Henry, N., Pietriga, E., and

Fekete, J.-D. (2009). Topology-aware navigation in

large networks. In Proceedings of the SIGCHI Confer-

ence on Human Factors in Computing Systems, pages

2319–2328. ACM.

Munzner, T. (1997). H3: Laying out large directed graphs

in 3d hyperbolic space. In Information Visualization,

1997. Proceedings., IEEE Symposium on, pages 2–10.

IEEE.

Panagiotidis, A., Bosch, H., Koch, S., and Ertl, T. (2011).

Edgeanalyzer: Exploratory analysis through advanced

edge interaction. In System Sciences (HICSS), 2011

44th Hawaii International Conference on, pages 1–

10. IEEE.

Sarkar, M. and Brown, M. H. (1992). Graphical ﬁsh-

eye views of graphs. In Proceedings of the SIGCHI

conference on Human factors in computing systems,

pages 83–91. ACM.

Sauro, J. and Lewis, J. R. (2012). Quantifying the user expe-

rience: Practical statistics for user research. Elsevier.

Sheelagh, M., Carpendale, T., Cowperthwaite, D., Fracchia,

F., and Shermer, T. (1996). Graph folding: Extend-

ing detail and context viewing into a tool for sub-

graph comparisons. In Graph Drawing, pages 127–

139. Springer.

Shneiderman, B. and Aris, A. (2006). Network visualiza-

tion by semantic substrates. Visualization and Com-

puter Graphics, IEEE Transactions on, 12(5):733–

740.

Tamassia, R. (2013). Handbook of graph drawing and vi-

sualization. CRC press.

Termeer, M., Lange, C., Telea, A., and Chaudron, M.

(2005). Visual exploration of combined architectural

and metric information. In Visualizing Software for

Understanding and Analysis, 2005. VISSOFT 2005.

3rd IEEE International Workshop on, pages 1–6.

Tominski, C., Abello, J., Van Ham, F., and Schumann, H.

(2006). Fisheye tree views and lenses for graph vi-

sualization. In Information Visualization, 2006. IV

2006. Tenth International Conference on, pages 17–

24. IEEE.

Vehlow, C., Reinhardt, T., and Weiskopf, D. (2013). Visu-

alizing fuzzy overlapping communities in networks.

Visualization and Computer Graphics, IEEE Transac-

tions on, 19(12):2486–2495.

Ware, C. and Bobrow, R. (2005). Supporting visual queries

on medium-sized node–link diagrams. Information

Visualization, 4(1):49–58.

Wong, N., Carpendale, S., and Greenberg, S. (2003). Edge-

lens: An interactive method for managing edge con-

gestion in graphs. In Information Visualization, 2003.

INFOVIS 2003. IEEE Symposium on, pages 51–58.

IEEE.

IVAPP 2016 - International Conference on Information Visualization Theory and Applications

154