A Toolkit to Support
Dynamic Social Network Visualization
Yiwei Cao, Ralf Klamma, Marc Spaniol and Yan Leng
Informatik 5 (Information Systems), RWTH Aachen University,
Ahornstr. 55, 52056 Aachen, Germany
{cao, klamma, spaniol, leng}@i5.informatik.rwth-aachen.de
Abstract. In this paper we introduce the design, implementation and evaluation
of the Dynamic Visualization Toolkit (DyVT) to support complex dynamic
social network visualization. Dynamic aspects of social networks such as
spatiotemporal as well as personalized information can be visualized in a
common toolkit. To that end, an XML-based target language DyVTML is an
extension of existing schemata enabling expression, storage and interchange of
rich animated social network data. With the language and the available tool
support, even less-experienced users can visualize temporal data in animations
and spatial data in maps and personalize it with icons and colors. The prototype
is evaluated by the visualization of large mailing list data sets.
Keywords: Information systems, Information visualization, Social network
analysis, XML.
1 Introduction
Social networks present a determinable structure that shows how people know each
other either directly or indirectly. They are a special kind of networks in which nodes
are entities that have values in a social context and the related social relationships.
Although one of the key characteristics of social relationships is changes, most social
network visualization is static due to the high complexity of social network data. For
instance, the temporal attributes are often neglected which indicate when social
relations take place or are discarded. However, understanding networks from a
dynamic perspective is essential, because it facilitates reasoning real objects such as
complex dynamic systems that evolve over time in the real world.
Besides just creating some nice graphics, social network visualization can generate
learning situations [14]. It also provides investigators with new insights into network
structures and helps them communicate with others [5]. So far, the research
community has developed a number of tools for building, analyzing and visualizing
social networks. However, these existing tools have several problems. First of all,
they have “structural bias” which implicitly denies much of the dynamic nature of
social relations [9]. The static data lacks other network data attributes such as spatial
data [10]. Researchers are more and more interested in how networks develop and
change timely and spatially. Secondly, the visualization results are not intuitive
enough. Users are not allowed to choose the appearance of social network
visualization. Finally, there is no specified language that covers a wide range of
dynamic aspects for social network visualization in terms of interoperability, yet.
Our research intends to surmount these limitations. An XML-based target language
is specified to be used in a metadata repository where temporal, spatial metadata are
integrated into a uniform XML file. Furthermore, this target language also supports
visualization. Based on this XML language, we have developed a Dynamic
Visualization Toolkit (DyVT) to give end users a better insight into their social
networks. It uses animations to visualize temporal data that shows how relationships
emerge over time. Moreover, graphs with map backgrounds are used to represent
spatial data. Users’ personalized information is also represented dynamically as well.
In sum, DyVT aims to visualize temporal, geospatial and personalized information
by extracting, integrating, and processing data from diverse data sources. The results
of the system can be also transformed into various multimedia formats. In the
prototype of DyVT, we use data from the mailing lists of the EU Network of
Excellence project PROLEARN (www.prolearn-academy.org) focusing on
technology enhanced learning and professional training within Europe.
The rest of this paper is organized as follows. Section 2 gives an overview of the
state of the art in the field of dynamic social network visualization. Section 3 is
devoted to the architectural design of DyVT. Section 4 describes the corresponding
implementations and presents the prototype. Finally, we summarize the research work
and give the perspective of future work in Section 5.
2 Related Work
Social network concepts are the background knowledge for social network
visualization. We also observe the diverse media in the Internet which builds up a
digital social environment [12]. Visualization methods are discussed with regard to
temporal, geospatial and personalized data. Furthermore, several XML based
interchange formats are introduced, owing to the requirement of defining a target
language to express rich social network data.
2.1 Basic Social Network Concepts
Social networks are based on an assumption of the importance of relationships among
interacting units. The social network perspective encompasses theories, models, and
applications that are expressed in terms of relational concepts or processes. The
important concepts of social networks are as follows [4]. Actors and their actions are
viewed as interdependent rather than independent and autonomous units. Relational
ties or linkages between actors are channels for transfer or flow of either material or
nonmaterial resources. Network models focus on individuals and consider the network
structural environment as opportunities for or constraints on individual actions.
Network models conceptualize social, economic, and political structures etc. as
lasting patterns of relations among actors.
In practices, the Internet is increasingly serving as a mediator of social activities. It
facilitates the social processes of communities in many categories of environments
and mechanisms. The digital social environment can be defined as social
environments that provide diverse forms of support to the social processes. It includes
a variety of systems ranging from very explicit and centralized community systems
that directly support people’s interactions, to some decentralized community systems
that support peer-to-peer mode of interaction and that are directly controlled by their
users [12]. The communication links are key objects to be visualized in social
network visualization.
Centralized media support one-to-many mode including forums, Wikis,
newsletters, and centralized mailing lists etc. Forums are public online open spaces
for discussion in which the communication link exits between every two members
participating in the same online forum. Virtual community systems are a community
of people sharing common interests, ideas, and feelings over the Internet or other
collaborative networks. The communication link exists between two members if two
persons share resources. In newsletters the communication link exists between the
administrator and every other member who is subscribed to the newsletter. A
centralized mailing list is a list of people who subscribe to a periodic mailing
distribution on a particular topic. When an email message is sent to the mailing list, it
is automatically forwarded to all addresses in the list. So senders have communication
links to every member in the list. Wikis allow users freely creating and editing Web
page content in Web browsers. The communication link between two persons could
be two members who edit the same Web page.
Decentralized media support peer-to-peer mode including Weblogs, emails, and
mailing lists etc. For emails, a communication link is defined between two persons if
they send emails to each other. Compared to the centralized mailing list, in
decentralized mailing lists a communication link is similar to email. Weblogs are
basically a journal published on the Web. They have two kinds of communication
links. One is that one member makes comments to the Weblog entry of another
member. The other is that two members make comments to the same Weblog entry.
In conclusion, all these communication links on the Internet is valuable social
network data to research on forming and development of communities and on social
processes within as well as cross communities.
2.2 Visualization of Social Networks
Social network analysts use two kinds of tools from mathematics to represent
information about patterns of ties among social actors: node-link graphs and matrixes.
The more common visualization is node-link graphs which consist of nodes
(actors) connected by edges (ties). The main goal is optimization of graphs’ layout for
comprehensibility and aesthetics. For example, certain mechanism is used to place
nodes in adjacent positions according to topological and structural criteria [1].
Considering various types of social network data, temporal, geospatial, and
personalized visualization are discussed respectively in the following.
Temporal visualization. Much recent research shows that the most static network
images do a poor job of understanding how networks develop and change. In order to
represent the structure of social network efficiently, at least two dimensions are
needed. Thus, there is no dimension to visualize time. Based on the literature survey,
temporal visualization approaches based on node-link graphs are represented by
animation layout. It is considered as the most suitable way to represent changes of
underlying network structures. It is complicated to determine the effectiveness of
animation, which is influenced by many factors. Among them, two important factors
are stated in [3]. Readability of visualization depends on aesthetic criteria to get
visualization comprehensive. And mental map preservation means those nodes that
exist in the series of networks remain in the same positions. An intuitive dynamic
visualization should balance both criteria.
Compared with the static layout, animation layout considers much on dynamic
social network relations which usually have two dimensions [10]. One is the relational
pace concerning the rate of change in relations. The pace of change information can
be described with regard to levels, changes or stabilities. The other is sequence which
focuses on the order of relations. A better understanding of network changes depends
on identifying such order of relations. With the both aforementioned criteria in mind,
multiple images of networks are produced according to the relational pace and then
these series of images are placed in sequences. In addition, sliders or other controls
are often used to navigate the animation loop directly.
Geospatial visualization. With the development of network and information
technology, the spatial data collection, sharing and analyzing is becoming more and
more important. Spatial objects have spatial relationships such as overlap and
containing [11], which adds complexity to social network data. Compared to
traditional visualization methods, background maps are a common way to visualize
and understand geospatial data of social networks. Moreover, Web map services are
effective approaches to geospatial data visualization.
Personalized visualization. Another interesting category of visualization is to
represent users’ preference of data visualization. We call it personalized visualization
here. It could visualize icons, sizes, and colors of nodes, colors, weights, line types of
links, even the layout of social network according to various layout algorithms.
2. 3 Languages for Dynamic Social Network Data
Languages for social network data are used to describe the structures and contents of
the sets of observations [6]. One of the most general characteristics of social network
data is that they have values in a social context. Referred to languages for dynamic
social network data, the focus is how to represent attributes that change over time.
Besides, the principle types of dynamic social network data are relational data and
attribute data.
Relational data are connections defined by the different rules in digital social
environments. Relational data focuses on the investigation of the social network
structure. Attribute data is about attributes, opinions and behaviors of nodes. Both
relational and attribute data can be gathered as a whole from various data sources,
such as questionnaires, direct observation, written records, and experiments etc.
In order to represent relational data and attribute data, three important XML based
markup languages for graphics have been surveyed.
GraphML stands for Graph Markup Language and is a graph exchange format that
aims to represent either relational data or attribute data. GraphML is also the only
published format that supports manipulation of dynamic graph data. In addition,
GraphML is supported in large number of graph analysis and visualization software,
such as JUNG, yFile, etc. Its major features are listed as below [2]:
• Simplicity. It is easy to parse and interpret for both humans and machines.
• Generality. There is no limitation with respect to the graph models such as hyper
graphs and hierarchical graphs etc.
• Extensibility. It is possible to extend the format in a well-defined way to represent
additional data required by arbitrary applications or more sophisticated use, e.g.
sending a layout algorithm together with the graph.
• Robustness. Systems that are not capable of handling the full range of graph
models or added information can be easily recognized. Models can be extracted to
the subsets that can be handled in the systems.
The graph model used in GraphML is: G = (V , E , D) , Where V is the set of nodes,
E is the set of edges and D represents data labels. A valid GraphML data file has two
parts: the header is used to define some basic features of the GraphML, such as XML
standard, XML Schema and a root element. The graph topology is a central part of the
GraphML including definition of both nodes and edges.
The GraphML elements can contain any number of graphs. Edges and nodes may
be ordered arbitrarily. For instance, it is unnecessary to list all nodes before all edges.
The complexity of GraphML is low, since the space requirement for saving a graph
with n nodes and m edges in GraphML is only Ο(n + m) .
KML is a file format used to display geographic data in a special browser for maps,
such as Google Earth, Google Maps, and Google Maps for mobile. A KML file is
processed in the same way as HTML (and XML) files are processed by Web
browsers. Like HTML, KML has a tag-based structure with names and attributes used
for specific display purposes. Thus, Google Earth and Google Maps are the common
browsers for KML files [8].
DyNetML is an XML-based social network language to address the needs of data
interchange, developed at Carnegie Mellon University [13]. DyNetML represents
dynamic network data as sets of time slices. Each of the time slices is a descriptive
snapshot of the organization at a given time. A dynamic network element is defined as
a sequence of MetaMatrix elements representing a snapshot of the organization for
one time period. Each of the MetaMatrix elements consists of the following objects.
TimePeriod allows clear identification of each time slice. Properties and measures
represent data about all of the time slices. Node contains one or more node sets.
Networks contain all networks in one time slice. And Anthrop facilitates the link of
network data to anthropological data.
Summarily, DyNetML can be used to describe temporal data, while KML format
can be used to represent geospatial metadata. Since GraphML is designed to be
extended easily, the implementation of logical integration of these three concepts can
be performed by extending GraphML.
3 System Design of DyVT
With reference to the state-of-the-art technologies, suitable methods are chosen to
develop the DyVT. In this section the design issues of DyVT is discussed. It begins
with the requirement analysis, followed by the system concepts and the data model.
3.1 Requirements Analysis
Both functional and non-functional requirements are analyzed. The main motivations
are both the limitations of existing social network visualization systems and userspecified requirements.
The limitations of the existing social network tools are critical. First, these tools
lack rich social network data representation. The interoperability oriented target
languages are rarely expressive enough to fully represent the rich social network data.
Second, current dynamic social network visualization forms are quite restricted. Line
graphs simulate the social network changes as lines in the graphs. However, it is
impossible to represent the global change of network over time, because such
summary statistics provide information on a single dimension of a network structure.
The other approach is to examine separate images over time, whereas this approach
lacks readability. The reason is that it is impossible to identify the sequence linking
node position in one frame to the position in the next. Third, the function of user
customization is often missing. Customization refers to user preferred data about
social network visualization. Fourth, the dynamic nature of social relations is ignored.
The dynamic nature refers to contextualized information such as changing spatial,
temporal and personalized information from users. A survey of the existing dynamic
social network visualization tools shows that normally only one of these dynamic
natures is visualized.
Based on the state of the art and potential users, functional requirements can be
divided into four categories.
1. Integration and interoperability of metadata. A target language records all kinds of
data including raw data, temporal data and spatial data as well. Thus, it allows
expressing and exchanging rich social network data.
2. Visualization of temporal data. The system should provide appropriate animation
visualization for end users to know how data changes along time.
3. Visualization of spatial data. The user should be able to obtain the graph on maps
where nodes and edges are located according to their real geographical location, if
the social network data is location relevant.
4. Visualization of personalized data. The system should enable the user to have
flexible choices to define the graphs in their own ways by selecting colors, sizes or
icons etc.
3. 2 System Concepts and Data Modeling
The main concept of DyVT is depicted in Figure 1. Social network data together with
temporal, spatial, and appearance or personalized data are the input to DyVT. In
DyVT temporal, spatial and personalized visualization are supported via an XMLbased target language. An XML-based target language, so-called DyVTML, is
defined to integrate metadata from different sources. It enables the system to visualize
temporal, spatial and personalized data in a highly collaborative unified interface.
XML is chosen because of its simplicity, extensibility, interoperability, and openness.
Fig. 1. System concepts of a Dynamic Visualization Tool (DyVT)
Users can make good use of temporal data by an animation that shows how
relationships emerge over time. At the same time, users get spatial insight into their
data with the layout of these nodes on the map according to the geographic
information. Users have also flexible options to specify the graph styles. It is possible
for users to choose their favorite colors, icons to represent graph. In addition, the
visualization results are changed dynamically by the users’ different choices.
Furthermore, with the help of the DyVTML, the visualization results are compatible
to various multimedia formats such SVG, GIF and JPEG etc. Consequently, they can
be well exported into the diverse formats.
After presenting the system concepts, we introduce DyVT on its data level. The
data used in DyVT mainly comes from three sources: mailing list databases, spatial
data, and personalized data which refers to user chosen appearance data. The mailing
list data is stored in relational database, spatial data is in some spatial databases and
user chosen appearance data is defined by users through a graphical user interface.
The DyVTML is used to interoperate data extracted from different data sources and to
transform them into data elements with the corresponding predefined tags.
However, the major drawback of XML-based format stems from the size and
complexity of XML files. The growth in size is dictated by needs for rigorous
markup, as every data element requires a number of delimiter tags to describe its
function to the parser. In order to solve this problem, we extract some redundant data
elements from the DyVTML and define a new data format called Appearance Data
Markup Language (ADML).
DyVTML is also used to store social network data for visualization component.
The major characteristic of these data is independence of visualization tier which
includes relational social network data, temporal data and spatial data. Relational
social network data is extracted from the mailing list data and contributes to build the
structure of the social network. Temporal data is also from the mailing lists and
provides temporal attributes to define both nodes and edges. Spatial data is extracted
from some existing geospatial databases and used to determine the location of each
node. Summarily, in the context of the mailing list the data stored in DyVTML
describes email communication events with attributes about when and where they
occur.
Fig. 2. Data modeling of DyVT in the entity relationship diagram
ADML is used to store user chosen appearance data. It comprises node appearance
data such as colors, sizes, shapes, and label colors, etc. as well as edge appearance
data such as colors, widths, weights, and label colors, etc. correspondingly.
There are two advantages to use these two data formats separately. On the one
hand, redundancy is reduced. As the number of nodes increases, it results in great
complexity to define appearance for each node and edge in the system. It leads to
redundant expression for describing both nodes and edges in DyVTML. On the other
hand, processing time is saved. In order to get a better visualization result, DyVT
enables users to change settings of graphical appearance for both nodes and edges. An
updating process will be called several times based on the same social network data,
when the user changes the settings. Usually, the appearance data is much smaller than
the underlying social network data. Using this method, the processing time will be
reduced by only parsing the updated appearance data rather than the whole social
network data.
The relationship of ADML and DyVTML as well as the data entities are illustrated
in Figure 2. This data model is the guideline for the system implementation.
4. Implementation of DyVT
The implementation of DyVT is based on three tiers: the database tier, the metadata
enrichment tier and the visualization tier.
The database tier contains raw data sources. In DyVT, we have three data sources:
the mailing list database of the EU project PROLEARN, the GeoLiteCity database to
get the mapping information between IP addresses to cities, and user chosen network
appearance data.
Since DyVT reads data from different data sources, then in the metadata
enrichment tier we need to refine the raw data. Raw data consists of two types:
relational data and attribute data. The former can be extracted from database by some
constraints such as content or time etc. The latter can be attained from the graphic
user interfaces.
To integrate these two types of data efficiently, the metadata refining module deals
with this piece of integration work through defining an XML-based data format for
metadata storage. On the other hand, in the metadata modeling module, a built-in
parser makes it possible to extract key fields from the XML-based DyVTML data file
created in the previous modules for the further use. In the visualization tier, a
resulting metadata model is responsible for containing all the information needed to
draw a visual representation of the data. Two handlers are used, the temporal view
handler and the map view handler. They are used to create visual features according
to their algorithms. Contents of the visual abstraction are put into three types of views
including animation view, static view and map view by users’ choices. These
visualization results can be exported into several formats such as SVG, GIF, and
JPEG etc.
Moreover, like the basic visualization model, we provide user interaction in this
tier as well. DyVT provides two levels of user interactions. One is for details of each
time window in animation views. User can extract any static graph generated in any
time window. The other interaction mainly serves the needs of experts who focus on
studying the detail of layout algorithms. From the user interfaces, users can see and
edit more details on configuration parameters for each algorithm. Two layout
algorithms are currently implemented. The cycle layout arranges nodes within the
predefined radius. The Kamada-Kawai layout (KK layout) is based on the algorithm
by Kamada and Kawai [7] which optimizes a graph with the goal of the lowest energy
sum. The energy is defined as the topological distances between each pair of nodes.
Fig. 3. An instance of DyVTML
In short, after we have captured the all needed kinds of metadata in the database
tier and have refined and remodeled this metadata in the metadata enrichment tier, the
visualization component is also executed in this tier. The actual rendering of the
metadata onto views is done, too. These views can provide varying perspective onto
data. For example, the use of maps shows the visualization with geographical
attachment, and animations show the dynamic temporal data visualization.
The DyVTML (cf. Figure 3) plays an important role to interchange the data among
the three tiers to visualize the dynamic social network. The DyVT screenshot in
Figure 4 illustrates the relationships among senders and receivers of the PROLEARN
mailing list within a certain time period. The location of each sender or receiver is
also visualized via Google Map API. A user-friendly interface is available by means
of a set of wizards.
The evaluation is performed by a task-oriented usability testing within the
PROLEARN user communities. We defined tasks and asked users to finish the tasks
and to record the time. Tasks are selected to get better feedback from users’ point of
view of functional and non-functional requirements of the system. Evaluation results
show that DyVT helps users build and specify the social network data visualization
effectively via the dynamic views.
5 Conclusions and Future Work
As a prototype for dynamic social network visualization, DyVT provides the
possibilities to realize temporal visualization with animation in multiple views, spatial
visualization, and personalized visualization in one uniform platform. So DyVT is
innovative at unifying the three types of visualization in one solo toolkit. The XML-
based language DyVTML enhances data interoperability among various multimedia
data formats.
Besides, according to users’ feedback some improvement work can still be done in
future. Currently, only email exchange between two persons are implemented, which
is a single relationship among actors. However, social relations among actors are
usually more complicated, in which actors are connected in multiple ways
simultaneously. We call this kind of network multi-relational network representing a
heterogeneous set of nodes and edges. Therefore, there exist not only multiple entities
of varying types, but also different ways or semantics by which these entities are
connected.
Fig. 4. The screenshot DyVT for geospatial visualization in continuous time slices and with
user chosen icons
In addition, only node-link graphs are used to visualize both temporal and
geospatial network data. With regard to the usability testing results, the readability of
node-link graph deteriorates significantly, when the size of the graph increases.
Hence, a node-link diagram is only suitable for small graphs. To visualize large
graphs, other representation should be employed such as matrix-based representation.
Compared with node-link graphs, matrix has no links overlapping problems and is
less affected by the increase of the number of nodes.
With regard to the layout algorithms, there existed several well-developed layout
algorithms for traditional static layout. It also provides many possibilities to either
modify these algorithms to be suitable for dynamic visualizations or develop new
layout algorithms for dynamic social networks.
Moreover, the evaluation of the results in DyVT depends on the users themselves.
It lacks the objective view of result. Statistical analysis tools should be integrated to
give objective evaluation results.
Now DyVT is used to visualize the mailing list data. However, mailing lists are
just one pattern in the digital social environments. A series of other patterns choosing
and executing components should be added in the database tier.
Not limited in social network, visualization is an intuitive way to get a better
insight into underlying data. With the aforementioned improvement possibilities,
DyVT is a useful visualization toolkit to be integrated into other applications easily
and quickly in future.
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