A Multiple-Aspects Visualization Tool for Exploring
Social Networks
Jie Gao, Kazuo Misue, and Jiro Tanaka
Department of Computer Science, Graduate School of System and Information Engineering,
University of Tsukuba{gao,misue,jiro}@iplab.cs.tsukuba.ac.jp

Abstract. Social network analysis (SNA) has been used to study the
relationships between actors in social networks, revealing their features andpatterns. In most cases, nodes and edges in graph theory are used to represent
actors and relationships, and graph representations are used to visually analyzesocial networks. However, many visualization tools using network diagrams
tend to depict most information about social networks by using the properties ofnodes, which result in a visual burden when identifying actors or relationships
according to certain properties. There is a lack of tools to support work by
investigators to provide insights into multiple-aspect networks. We considered
actors, relationships, and communities to be three important elements, anddeveloped a tool called MixVis that integrates a tagcloud, network diagrams,
and a list to show the elements. Our tool allows users to explore social networks
from elements of interest, and acquire details through links with the threedifferent viewpoints.
Keywords: Social network analysis, visualization, human interface.

1 Introduction
Social network analysis (SNA) is used to investigate the interactions between one
person and another, one company and another, or one country and another. By using
SNA, investigators attempt to find an important actor (person, company, or country),
to understand the information flow and to master the trends in the whole network.
SNA can facilitate an investigator’s decision-making processes. SNA has been
applied to many areas such as social science, politics, and psychology.
Actors, links, and communities are the main factors in social networks. A
community can be defined in two different ways. In some research, a community ismade up of an exclusive disjoint group of actors, and often extracted by using aclustering algorithm such as Newman’s method [6]. However, in the real world, theword community can also mean an actor’s affiliations. The difference between thetwo definitions is that a person can only join in one exclusive group, but can havemultiple affiliations. The latter has recently been attracting a great deal of attention
with the increasing popularity of the Social Networking Service (SNS), where end
users can interact with friends by participating in a variety of virtual communities.
M.J. Smith and G. Salvendy (Eds.): Human Interface, Part II, HCII 2009, LNCS 5618, pp. 277–286, 2009.
© Springer-Verlag Berlin Heidelberg 2009

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Nodes and edges in graph theory are used to represent actors and links. A
community is a set of actors and can be regarded as a cluster in a graph. A graph canbe drawn as a network diagram. Communities are overlaid on the network diagram,and can be drawn as a closed area [7], an adjacent matrix [5], or others. A refinedlayout for the network can provide an intuitive and comprehensive image of networks.
We regard traditional work in exploring social networks as a network-centeredapproach. Most operations and information are attached to networks. These methods
can give a general impression of entire networks and even an actor’s most important
properties. However, these methods pose two problems. The first is that images
become cluttered since communities overlap, when actors are allowed to join morethan one community. The second problem is the great visual burden imposed on theinvestigator in having to explore networks in detail because of the excessive amounts
of information on the networks.
To solve these problems, we present an approach that uses a combination of threeviews corresponding to three aspects: nodes, links, and communities. Based on theapproach, we developed a tool that integrates the network diagram, a tagcloud [8],and a list to support SNA. The network diagram shows links between actors and thewhole network, the tagcloud displays actors with their attributes, and the list presents
the communities that the actors belong to. We also provide a set of techniques ofinteraction to explore social networks.

2 Task Description
SNA aims at identifying important actors, crucial links, communities, and network
characteristics [2]. We classified the tasks for SNA into three specific categories.
2.1 Task to Identify Actors, Links, and Communities
SNA should identify actors, links and communities with specific properties.
There are several properties of actors that an investigator is interested in. Of these,
centrality is widely used to identify actors that occupy important positions. The
representative criteria of centralities include degree centrality and betweenness
centrality.
− Degree centrality evaluates the importance of an actor by the number of actors
linked to it. An actor linked to most of the other actors is regarded as being themost important. An actor with high degree centrality can be interpreted as an
outgoing or popular person in a friendship network.
− Betweenness centrality evaluates the importance of an actor by the number oftimes that the shortest path of any pair of other actors passes through the actor. The
most mediated actor is considered to be the most important. The one with highbetweenness centrality can be translated to be an intermediary of subgroups in afriendship network.
SNA should also identify crucial links such as those that bridge many pairs ofactors, and communities of actors who have tight internal connections.

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2.2 Task to Comprehend Details of Certain Actors, Links, and Communities
Details are also necessary for understanding the indentified actor, links, andcommunities. SNA should provide investigators with actors that are directly linked tothe interested actor and communities that the actor belongs to. SNA should alsopresent links between actors in a way that is understandable. Basically, people want toknow the two actors involved in a link, as well as actors linked to a certain actor. In
addition, investigators want to know how actors in a community are connected, what
the attributes of its members are, and who bridges multiple communities. SNA should
comprehensively and clearly answer these substantive questions.
2.3 Task to Understand the Whole Network
SNA should provide an overview of the whole network. The datasets for SNA studies
are not only about the attributes of an actor, but also systematic features of all theactors. These features reflect various social phenomena or affairs. SNA reveals these
phenomena or affairs by providing an overview of the whole network.

3 Traditional Tools for SNA
There are a number of visualization systems used to support SNA. We divided thesesystems into two groups, i.e., statistical analysis-oriented and visual presentationoriented methods.
Statistical analysis-oriented methods calculate statistical values that represent avariable feature of social networks. For example, UCINET [1] enables centrality to bemeasured, communities to be identified, and other functions for analysis. As UCINET
has too many complex functions, users always need help from experts or need to
study manuals. Pajek [3] is a tool embedded into UCINET. It enables users tounderstand social networks more easily by drawing the network as a diagram.
However, it only depicts the statistical values in colors or the sizes of the nodes in a
network diagram. When identifying actors and communities, users are very easily
distracted by the complicated denotations in the network diagram.
Recent research has tended to take advantage of human vision, and provides visualpresentations and various techniques of interaction to enhance exploration done byinvestigators. NodeTrix [5] integrates a network diagram and a matrix to enable theglobal structure of the network to be visualized and also to allow local communities tobe analyzed in detail. However, it lacks the capacity for fine exploration of actors.
This is because users have to take their time to distinguish an actor’s centrality byassessing the brightness of nodes in a large network diagram. Perer et al. suggest a
combinatorial view of a network diagram and a list [7]. However, it only provides
dependent views of an actor list, while communities are drawn as closed areas andoverlaid on the network. Their method only divides the network into disjointed
subgroups. If these approaches were applied to displaying communities that allintersected without discrimination, a great number of overlaps would occur and resultin visual clutter. As a result, it would be difficult to identify communities and content.

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4 Our Approach: Views Corresponding to Three Aspects
We considered actors, links, and communities as three aspects from which investigators
could start their explorations since these provide very distinct information. Each of these
aspects has its own features and provides different information.
Our idea was to divide presentations of social networks into three appropriate
views (see Fig. 1) that corresponded to the three aspects and synchronize these views
to reveal other information that concerned aspects in real time. By doing so,
information in networks can be dispersed in different views, so that investigators canrapidly and effectively identify all aspect by only focusing on one. In addition, otherrepresentations (e.g., lists) can fill in gaps where the network does not lay out
networks sufficiently systematically such as in sorting. The following describes thethree different views and the techniques of interaction linking them.

(a)

(b)

(c)

(d)
Fig. 1. Snapshot of MixVis. Actor clicked in tagcloud is highlighted by blue box in network.
Actor’s communities are highlighted in different colors.

4.1 Tagcloud to Rapidly Identify Actors
The most important properties of an actor are its centrality, its absolute value, andhow important it is in the whole network. We used a tagcloud to represent actors to
identify and comprehend them with specified properties. The tagcloud is a visualpresentation of a set of words. The attributes of text in a tagcloud such as size andcolor are used to represent features (such as frequency) of associated terms [8]. This isfor “impression formation” and provides a pre-attentive image of the “key person”.
Actors’ names in our tagcloud were lined up on several lines. The layout of theactors was changed automatically according to the size of the tagcloud panel and thenumber of actors. The size and background of each actor was determined by its
centralities. An actor’s size was proportional to its degree centrality and the brightnessof its background was inversely proportional to its betweenness centrality.

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The tagcloud also provided an accurate value for degree and betweenness
centrality. When the cursor crossed over an actor, a label popped up, and the absolutevalues of degree and betweenness centrality were displayed on the label. The actors
in a tagcloud could be sorted by their order of centrality and in alphabetical order.
Fig. 1(d) shows a tagcloud with about 150 actors.
4.2 Network Diagram for Links and Overview of Networks
People are concerned about who is connected to whom in links, especially thoseactors connected to central actors. Other important information is in the overview of
the network, which is the entire content for the links. Networks are drawn as network
diagrams. We developed a force-oriented method to lay out the network diagram, bywhich we showed the links between actors, and also made it possible to overlay
communities on the network diagram [4].
The method of drawing a compound graph was leveraged to draw the network. We
defined a compound graph as a combination of an inclusion graph and an adjacency
graph. It could not only represent links between actors, but also sets of actors(communities). We drew links as straight-line segments with our method, and drew
communities as inclusion areas. Our method of drawing allowed communities tointersect. There is a drawing of a network in Fig. 1(b).
It is also possible to depict an actor’s properties by using a node’s attributes. This
makes it convenient to check the link’s features. For example, it is easy to knowwhether one link is connecting central actors or not, by checking the connected
actors’ colors. We designed three styles to draw nodes (actors). 1) Labels indicate
actors’ names and a label’s background color represent an actor’s degree centrality. A
red background indicates that an actor’s degree centrality is high (see Fig. 2). 2)
Labels indicate actors’ names and a label’s background color represents an actor’s
betweenness centrality. A red background indicates actor’s betweenness centrality ishigh (see Fig. 1). 3) An actor’s image represents the actor.
The layout of the network can be refined to observe both the overview of the wholenetwork and the details of one link. We provided a control panel (Fig. 1(a)), in whicha slider was used to adjust several parameters to lay out the network diagram.
4.3 List to Flexibly Select Communities of Interest
A community indicates a set of actors sharing some common properties. Flexible
interactions with communities are required to search and choose communities ofinterest. An investigator can comprehend the structure (pattern) of communities and findintersections through interactions. We used a list to select communities (see Fig. 1(c)).
The list contained all the communities actors belonged to. These communities werearranged in alphabetical order. An investigator could select single or multiple
communities of interest.
The brightness of a community’s background indicates its size, i.e., the number ofmembers belonging to it. A dark community indicates that it has many members.
When highlighting communities, e.g., when multiple communities are selected, we
use another set of colors to distinguish them. We used a hue, saturation, and value(HSV) color model to assign colors to the communities to avoid confusion. More

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importantly, we produced different bright colors, rather than gray, by controlling thehue, saturation, and value.
4.4 Linking Three Views
The network diagram, tagcloud, and list were not isolated views, but an integrated,synchronized system. We introduced the concept of “linking and brushing” to design
our system. Linking and brushing is a technique to combine different representations
to reveal different aspects of data. Interaction with one will cause corresponding
changes in the other representations.
We leveraged the technique to produce insights into each aspect that was
concurrently visible, so that the investigator could rapidly acquire details about the
aspect of concern. We mainly harmonized the three views by highlighting related
information in the two other views when selecting an object in the one view.
Moreover, we used the same color to highlight a community and its members. By
doing so, the investigator could visually distinguish members and their affiliations.

5 MixVis: A Tool for Exploring Social Networks
Our system is constructed of three main views including a network diagram, a list,
and a tagcloud. The middle panel (Fig. 1(b)) is the network diagram, the left of which
has the control panel (Fig. 1(a)) that contains widgets to adjust the diagram. The
community list (Fig 1(c)) is at the right of the network diagram. A tagcloud (Fig 1(d))
is at the bottom of the whole user interface.
The size of the view can be adjusted by users themselves according to theirindividual needs. They can also hide or maximize one view. In some cases, anappropriate size can enhance identification. For example, an extended network
diagram can clearly show large-scale nodes and their relationships.
Our tool provides techniques for interactively exploring multiple aspects of
insights into social networks. These insights contain an overview of the wholenetwork, insights into communities or parts of the network, and details on some actors
or links. They are consistent with people’s interests, i.e., properties of a single actor,
links, communities, connectivities between or intersections of communities, and thebalance or density of the whole network. In the following, we will describe techniquesof interaction with MixVis.
5.1 Interactions from Tagcloud
We assumed that an investigator would be focusing on actors and want to find those
who were central. Tagcloud shows actors in an impressive way in that: 1) actors withhigh degree centrality are outstanding among others since they are larger and 2) actors
with high betweenness centrality are easy to find because their backgrounds aredarker. Consequently, an investigator can find them at a quick glance in a tagcloud.
Moreover, they can accurately identify central actors by sorting them by degree
centrality, betweenness centrality, or in alphabetical order. Popup labels that post theA Multiple-Aspects Visualization Tool for Exploring Social Networks

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value of centrality can facilitate this process. After this, the investigator can know theposition of the actor in the network and its communities by clicking a tag. The sameactor will be highlighted in the network and its communities will also be highlighted.
Fig. 1 illustrates how the other two views change after the tag is clicked. One tag is
identified and selected. Due to the three views being linked, the actor is also
highlighted by a box in the network diagram and the communities that the actor
belongs to are highlighted by different colors on a list.
5.2 Interactions from Network Diagram
When browsing the network diagram, an investigator may notice an actor and want tofind out whom it is related to, which community it belongs to, and what its centrality
is. The role of the actor is investigated by clicking it in the network. Actors directly
connected to the one that was clicked are highlighted by a red box, while the otheractors are blurred. Communities that the clicked actor belongs to are highlighted on alist by the color of the background. The actor is also highlighted in a tagcloud, where
we can see its other properties. The investigator can adjust the layout of the network
diagram by using the widgets in the control panel.
Fig. 2 is snapshot of MixVis after one actor in the network has been clicked. Bytaking a glance at the three views, we can know its links, its properties such as its
degree centrality, and its communities.

Fig. 2. Actor clicked in network diagram is turned blue in tagcloud. Its communities arehighlighted in different colors.

5.3 Interactions from List
If an investigator focuses attention on communities, the list can facilitate his/her work byallowing any of one or more communities to be selected. In the meantime, the selected
communities are drawn as inclusion areas in the network. As actors shared by multiple

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communities are placed at the intersection of closed areas, the investigator can find themeasily. To see the centralities of the members of every community, tags belonging tothese communities are highlighted in the same color as the respective community.
As seen in Fig. 3, two communities on the community list have been selected; theactors are contained in an inclusion area to indicate their membership. The
correspondence is very clear from the color of the community, the inclusion area, andthe actor in the tagcloud. Therefore, that investigator can easily comprehend thedetails of the communities.

Fig. 3. Two communities are selected. Colors of members in tagcloud area in network diagram
are consistent with communities on list.

6 Evaluation
6.1 User Study
To test and verify the efficiency of our approach, we administered a user study inwhich MixVis was compared to two other tools: ToolA and ToolB. ToolA only had
the network diagram in MixVis, and the other information related to the community
and actor was overlaid on the network diagram. ToolA was similar to the traditional
tools that presented all the information in a network diagram. ToolB included a
network diagram and a tagcloud. The list in ToolA and ToolB could be displayed or
hidden all together. ToolB was similar to work that also used combinations ofdifferent representations.
We designed seven tasks and assumed them to be important tasks of SNA. There isa brief description of the tasks in Table 1. The tasks covered the categories given in
Section 2.

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Table 1. Tasks to be solved by subjects
Task No.
1
2
3
4
5
6
7

Content
To identify actor with highest degree centrality
To identify actor with highest betweenness centrality
To explore details on an assigned actor
To explore details on a specified link
To explore details on two specified communities
To find groups of actors by browsing the whole network
To recognize a trend related to the community and actor by using three views

We asked ten students to participate in the user study. They either knew little or agreat deal about visualization. Prior to starting this study, we gave them a short
introduction to SNA and the functions of three tools. Then, they were asked to solve
the seven tasks by using ToolA, ToolB, and MixVis (To eliminate the influence ofnames, we assigned the new name of “ToolC” to MixVis when conducting the study).
After each task, subjects awarded these three tools scores according to how usable
they were. Five points represented the maximum score.
The average scores for the three tools for each task are shown in the bar graph inFig. 4. The figure indicates that MixVis was awarded the highest score for handlingthe seven tasks.

Fig. 4. Average scores for ToolA, ToolB, and MixVis

6.2 Discussion
According to Fig. 4, we assessed that MixVis was better than the other two tools in
usability for most of the tasks.
The differences in the average scores strongly support our perspective that the
representation of multiple aspects can facilitate an investigator’s work. Significant
differences, especially in completing Tasks 5 and 7, appeared and were verified with at-test. We inferred that these differences were caused by the fact that the two tasks
were related to the community and only MixVis could provide a list and a flexible
function to enable selection.
ToolA, ToolB, and MixVis were evaluated similarly in Tasks 1 and 2. The t-test
confirmed that there are no significant differences between ToolA and MixVis inaccomplishing Task 2. There were also no significant differences between ToolB and

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MixVis in accomplishing Task 1. The reason for the lack of difference is that in Tasks 1
and 2, we asked subjects to identify the actor who had the highest degree centrality andhighest betweenness centrality that could be done without using a tagcloud and a list as
in the traditional method.

7 Conclusion
We proposed an approach that used a combination of three views corresponding tonodes, links, and communities. Based on the approach, we developed a tool called
MixVis to support SNA. We adopted “linking and brushing” to enable real-time
interaction between the three different views, and interaction between users and the
proposed tool. Our tool allowed drawings of an actor’s affiliations, which is notsupported by most traditional work. We administered user studies to evaluate how
usable our tool was. Another two tools, which were equipped with only one or twoviews, were compared to the new tool. The experimental results revealed that MixVis
was much easier to use.

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