Title:
DyNetML: Interchange Format for Rich Social Network
Data
Authors:
Maksim Tsvetovat - Jeff Reminga - Kathleen M. Carley
Carnegie Mellon University, Pittsburgh, PA
[email protected],
[email protected], [email protected]
Abstract:
To facilitate cooperation between research groups within the field
of social network analysis and exchange of social network data, it
is essential to provide a data interchange language that (a)
provides sufficient expressive power to represent rich datasets,
(b) is human-readable and (c) can be used with a large number of
programming languages and architectures. In this paper, we present
DyNetML: an XML-based data interchange language that combines
ability to express complex dynamic SNA datasets with ease of
integration within a variety of software platforms.
Acknowledgement:
This work was supported in part by the Department of Defense, the
NSF ITR 1040059 and the Office of Naval Research N00014-02-1-0973,
and the National Science Foundation under the IGERT program for
training and research in CASOS. Additional support was provided
by CASOS - the center for Computational Analysis of Social and
Organizational Systems at Carnegie Mellon University. The views
and conclusions contained in this document are those of the author
and should not be interpreted as representing the official
policies, either expressed or implied, of the Department of
Defense, the Office of Naval Research, the National Science
Foundation, or the U.S. government.
Citation:
Maksim Tsvetovat, Jeff Reminga and Kathleen M. Carley, 2003,
"DyNetML: Interchange Format for Rich Social Network Data,"
NAACSOS conference proceedings, Pittsburgh, PA.
DyNetML: Interchange Format for Rich Social Network Data
Maksim Tsvetovat - Jeff Reminga - Kathleen M. Carley
Carnegie Mellon University
[email protected] - [email protected] - [email protected]
Current state of the art in social network data representation presents a
fairly bleak picture. Each of the analysis and simulation packages uses its own,
proprietary and incompatible data format. Some of the file formats do not even
have a specification document, making the files unreadable without the software
that produced them.
Data formats that were designed for interoperability (such as DL) are rarely
expressive enough to fully represent the datasets.
As a result, most researchers are forced to deal with data interchange in a makeshift
fashion, at best increasing the workload and at worst resulting in loss of data integrity.
To improve cooperation between researchers and to promote interoperability of
software, the community needs to agree on a common data interchange language. In
an informal meeting at CASOS 2002, a number of prominent developers and users of
social network analysis tools agreed to cooperate in the development of an
interchange language and to support it when it is available.
This paper proposes an XML-derived language that addresses requirements
for expressivity and compatibility. We proceed to outline our vision for the development
of social network analysis toolchains which will increase the ability of researchers
to share and analyze data
As we mentioned above, the current social network data formats have a number of deficiencies:
Binary files are very difficult to read if exact specification of the file format
is not provided. Significant extra efforts are required to keep compatibility with other tools
or between versions of the same tool.
Multiple files used for specification of rich data or saving analysis output present
a number of problems. First of all, there is a significant potential for data loss due to misplaced
or corrupted files (for example, while sent through email). Secondly, a consistent naming scheme
for all files and a file catalogue are required to prevent data loss - an extra burden on the
researcher (as these features are not included in the analysis software)
Raw Data files such as binary matrices or edge lists lack the expressiveness required
to represent multiple relations between nodes or evolution of social networks over time.
Human-Readable Data in text files or spreadsheets solves the expressivity problem but
requires extensive post-processing by hand or with post-processing scripts. However,
these programs often represent the weakest link in the software chain (due to hasty
design and dependance on outside tools such as Perl or Awk).
In light of the problems outlined above, we proceed to define requirements for a universal data interchange
format that would facilitate the task of exchanging rich social network data and improving compatibility
of analysis and visualization tools.
- The data interchange format shall be contained in human-readable text files that are at the same time
easily parsable by computers.
- The data interchange format shall allow an entire dataset, complete with all computed measurements,
to be stored in one file
- The data interchange format shall provide maximum expressive power to its users, allowing:
- Typed nodes (types may include "person", "resource", "organization", "knowledge", etc)
- Multiple sets of nodes of the same type (to express multiple units within the company, etc)
- Multiple typed attributes per node
- Typed edges
- Multiple typed attributes per edge
- Multiple graphs (sets of edges) expressed within the same file
- Dynamic network data expressed in a single file
- The data interchange format shall allow developers to extend it in a fashion that will not break existing
software
- The data interchange format shall be flexible enough to be used as both input and output of analysis tools.
Faced with a pressing need for tool interoperability within our laboratory,
we have developed DyNetML - and XML derivative language that addresses the above requirements.
Figure 1:
Structure of DyNetML
 |
Figure 1 shows the hierarchical structure of the DyNetML files.
The <DynamicNetwork> element encapsulates all time periods within a dynamic network.
Each time period is represented by a <MetaMatrix> element, which encapsulates network
data for a single time period, including multiple matrices and node and properties.
Optional ``timePeriod'' attribute identifies the time at which a given metamatrix has been collected.
Optional <measures> element encapsulates a set of MetaMatrix-level measures that have been
computed on the given time period.
<measure name=''sampleMeasure'' type=''double'' value=''1''>
Each measure is specified with a unique name, type (double, string, boolean) and value
<nodes> element encapsulates all of the nodesets in a given MetaMatrix.
<nodeset id=''nodeset1'' type=''agent''>
A nodeset is a grouping of nodes by type; types include agent, knowledge, resource, task,
organization, location. More the one nodeset of the same type can be defined; nodeset ID must
be unique.
Each <node> within a <nodeset> has to be supplied with a unique ID and can contain
an arbitrary number of innate <properties> or computed <measures>. This allows the
data collectors to specify arbitrarily complex data about nodes
while separating collected data from results of analysis.
The <networks> element encapsulates network data stored as graph connection lists.
The <graph> nodes are specified with a unique ID and IDs of the source and target nodesets.
Each Graph contains a collection of Edge elements whose source and target are nodes
previously declared in a Nodeset.
This allows the user to specify an arbitrary number of networks involving the same (e.g friendship and advice networks) or different types of actors (e.g.
communication and resource distribution networks).
<edge source=''node1'' target=''node2'' type=''double'' value=''1''>
Edges are represented by specifying the source and target of the edge. Each edge also has a value
and a value type (double, string or boolean).
Each graph and edge can also be followed by a set of innate Properties and computed
Measures.
For more information, please refer to the Document Type Definition (DTD) and a sample dataset in the
appendix of this paper.
DyNetML is currently supported through a C and Java libraries that are a part of the CASOS software suite. Since XML parsers exist
for practically all platforms and languages, integration of DyNetML into existing tools can be completed in one day or less.
While the research community has developed a number of very powerful data gathering, analysis and visualization tools, the
tools rarely operate well with each other. While file import/export options make it possible to use multiple analysis
tools within a single project, a lack of automation and scripting features does not allow for batch-processing of data
and report generation, thus vastly increasing labour requirements for analysis of complex datasets.
In our vision, the future of social network analysis lies in creating a seamless toolchain, enabling researchers to
mix and match data gathering, analysis and visualization tools and to create analysis scripts for batch-mode processing of
large datasets or for repeating the same analysis on different datasets. Publishing analysis scripts would allow the research community to more easily reproduce and verify experimental or empirical results.
Each of the tools on the toolchain shall:
- Take the accepted data interchange format (such as DyNetML) as input and produce it as output (with the
exception of conversion tools)
- Analysis tools shall integrate computational results into the dataset, using accepted measure identifiers
- Each tool that modifies the dataset shall mark its modifications with tool name or ID.
- Each tool shall provide a command-line interface that allows full access to its features via a scripting language
- A C-like scripting language shall be developed for integration of tools within the toolchain. Alternatively,
existing scripting languages such as Java, Perl or Python can be used.
- Visual analysis builder tools shall be developed to allow creation of analysis scripts by non-programmers
An integrated toolchain such as the one outlined above can only be created through cooperation of members of
the research community through an open-source development process, but the first step
is to create a
uniform data interchange language. In this paper, we proposed one such language:
DyNetML, an XML-derived language for specification of rich social network data.
It is important to note that since DyNetML is intended as a service to the social network analysis and simulation community,
comments and requests for revisions are welcome at any time. Once the project has considerable community support, we
shall establish a revision process that will respond to the requirements of the community
while maintaining backward compatibility with existing software.
<!-- These entities are just to make the description simpler -->
<!ENTITY % common-elements "properties">
<!ENTITY % nodeTypes "(agent|knowledge|resource|task|organization|location)">
<!ENTITY % edgeTypes "(binary|double|string)">
<!-- ========================================================================= -->
<!-- ========================================================================= -->
<!-- A file consists of a graph definition and, optionally, -->
<!-- addinitional definitions and edit nodes -->
<!-- The real 'content' might be preceeded by optional data -->
<!ELEMENT DynamicNetwork ((MetaMatrix)*)>
<!ELEMENT MetaMatrix ((%common-elements;)|measures|nodes|networks)*>
<!ATTLIST MetaMatrix
timePeriod CDATA #IMPLIED
>
<!-- ========================================================================= -->
<!-- ========================================================================= -->
<!-- ========================================================================= -->
<!-- The definition part -->
<!-- ========================================================================= -->
<!ELEMENT networks ((graph)*)>
<!-- A graph consists of an optional label, references to icons, URL-s to -->
<!-- external data, locally stored data and, finally, nodes and edges -->
<!ELEMENT graph ((%common-elements;)?,
(measures)?,(edge)*)>
<!ATTLIST graph
id CDATA #REQUIRED
sourceType %nodeTypes; #REQUIRED
targetType %nodeTypes; #REQUIRED
isDirected (true|false) #IMPLIED
>
<!-- ========================================================================= -->
<!-- Nodes and edges -->
<!-- ========================================================================= -->
<!ELEMENT nodes ((nodeset)*)>
<!ELEMENT nodeset ((node)*)>
<!ATTLIST nodeset
id CDATA #REQUIRED
type %nodeTypes; #REQUIRED
>
<!-- A node may include element information; it has a unique identifier -->
<!ELEMENT node ((%common-elements;)?,(measures)?)>
<!ATTLIST node
id CDATA #REQUIRED
title CDATA #IMPLIED
>
<!-- An edge may include element information; it may have an identifier, and -->
<!-- it has to give its source and target -->
<!ELEMENT edge ((%common-elements;)?,(measures)?)>
<!ATTLIST edge
name CDATA #IMPLIED
source CDATA #REQUIRED
target CDATA #REQUIRED
type %edgeTypes; #REQUIRED
value CDATA #IMPLIED
>
<!-- ========================================================================= -->
<!-- Properties are placeholders for key-value pairs -->
<!ELEMENT properties ((property)*)>
<!ELEMENT property EMPTY>
<!ATTLIST property
name CDATA #REQUIRED
type %edgeTypes; #REQUIRED
value CDATA #REQUIRED
>
<!--
******************* SOCIAL NETWORK MEASURES **************************
Measures are defined as applicable to the entire metamatrix, individual graph,
node or edge. If further clarification of how the measure was computed is needed,
one can use <rowset> and <colset> attributes to specify which nodesets were used
as input. Multiple rowsets and columnsets are allowed.
-->
<!ELEMENT measures ((measure)*)>
<!ELEMENT measure ((input*))>
<!ATTLIST measure
name CDATA #REQUIRED
type %edgeTypes; #REQUIRED
value CDATA #REQUIRED
>
<!ELEMENT input EMPTY>
<!ATTLIST input
id CDATA #REQUIRED
>
<!DOCTYPE DynamicNetwork SYSTEM "SocNetML.dtd">
<DynamicNetwork>
<!--
Define the metamatrix... watch comments in the XML for explanation of
particular features of the format
-->
<MetaMatrix timePeriod="1">
<!-- A global measure on the entire metamatrix -->
<measures>
<measure name="global" type="double" value="3.14">
<input id="knowledgeNetwork"/>
<input id="friendship"/>
</measure>
</measures>
<!-- First, we specify the nodes -->
<nodes>
<!--
Nodes are broken up into nodesets by type (e.g.
agent, knowledge, resource, task, etc)
-->
<nodeset id="foo" type="agent">
<!-- This is the simple node with no extended data -->
<node id="b"/>
<!-- This is a more complex node with properties and
attached measures -->
<node id="a">
<!-- This is how to specify internal node properties -->
<properties>
<property name="foo" type="double" value="3.14"/>
<property name="bar" type="double" value="3.14"/>
</properties>
<!-- This is how to specify node-level measures -->
<measures>
<!-- Each measure is named and accompanied
by type (double|string|binary) -->
<measure name="centrality" type="double" value="3.14"/>
<measure name="betweenness" type="double" value="3.14"/>
</measures>
</node>
</nodeset>
<!-- Another nodeset -->
<nodeset type="knowledge">
<node id="a1"/>
<node id="a2" title="boss"/>
</nodeset>
</nodes>
<!-- Now we specify the graphs that comprise the metamatrix -->
<networks>
<!--
NOTE: source and target of each edge should be a valid node; however
it's up to the software developer to ensure that - or to check consistency
in any code that imports this data
-->
<!-- A very simple graph -->
<graph id="friendship" sourceType="agent" targetType="agent">
<edge source="a" target="b" type="binary"/>
<edge source="b" target="a" type="binary"/>
</graph>
<!-- A graph with some graph-level measures -->
<graph id="advice" sourceType="agent" targetType="agent">
<measures>
<!-- Just like node-level measures; nothing new here -->
<measure name="centralization" type="double" value="3.14159"/>
<measure name="foo" type="double" value="3.14159"/>
<measure name="bar" type="double" value="3.14159"/>
</measures>
<edge source="a" target="b" type="binary"/>
</graph>
<graph id="knowledgeNetwork" isDirected="true" sourceType="agent" targetType="knowledge">
<edge source="a" target="1" type="string" value="foobar"/>
<edge source="b" target="2" type="double" value="3.14159"/>
</graph>
</networks>
</MetaMatrix>
</DynamicNetwork>
DyNetML: Interchange Format for Rich Social Network Data
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