This page last changed on Mar 28, 2011 by scott.

Table 8.1 provides an overview of existing tools used in scientometrics research, see also (Fekete and Börner-chairs 2004). The tools are sorted by the date of their creation. Domain refers to the field in which they were originally developed such as social science (SocSci), scientometrics (Scientom), biology (Bio), geography (Geo), and computer science (CS). Coverage aims to capture the general functionality and types of algorithms available, e.g., Analysis and Visualization (A+V), see also description column.
Table 8.1 Network analysis and visualization tools commonly used in scientometrics research.

Tool Year Domain Coverage Description
UI Open Source Operating System References
S&T Dynamics Toolbox 1985 Scientom. Scientom. Tools from Loet Leydesdorff for organization analysis, and visualization of scholarly data. Command-line No Windows (Leydesdorff 2008)
In Flow 1987 SocSci A + V Social network analysis software for organizations with support for what-if analysis. Graphical No Windows (Krebs 2008)
Pajek 1996 SocSci* A + V A network analysis and visualization program with many analysis algorithms, particularly for social network analysis. Graphical No Windows (Batagelj and Mrvar 1998)
UCINet 2000 SocSci* A + V Social network analysis software particularly useful for exploratory analysis. Graphical No Windows (Borgatti, Everett et al. 2002)
Boost Graph Library 2000 CS Analysis and Manipulation Extremely efficient and flexible C++ library for extremely large networks. Library Yes All Major (Siek, Lee et al. 2002)
Visone 2001 SocSci A + V Social network analysis tool for research and teaching, with a focus on innovative and advanced visual methods. Graphical No All Major (Brandes and Wagner 2008)
GeoVISTA 2002 Geo GeoVis GIS software that can be used to lay out networks on geospatial substrates. Graphical Yes All Major (Takatsuka and Gahegan 2002)
Cytoscape 2002 Bio* Visualization Network visualization and analysis tool focusing on biological networks, with particularly nice visualizations. Graphical Yes All Major (Cytoscape-Consortium 2008)
Tulip 2003 CS Visualization Graph visualization software for networks over
1,000, 000 elements.
Graphical Yes All Major (Auber 2003)
iGraph 2003 CS Analysis and Manipulation A library for classic and cutting edge network analysis usable with many programming languages. Library Yes All Major (Csárdi and Nepusz 2006)
CiteSpace 2004 Scientom A + V A tool to analyze and visualize scientific literature, particularly co-citation structures. Graphical Yes All Major (Chen 2006)
HistCite 2004 Scientom A + V Analysis and visualization tool for data from the Web of Science. Graphical No Windows (Garfield 2008)
R 2004 Statistics A + V A statistical computing language with many libraries for sophisticated network analyses. Command-line Yes All Major (Ihaka and Gentleman 1996)
Prefuse 2005 Visualization Visualization A general visualization framework with many capabilities to support network visualization and analysis. Library Yes All Major (Heer, Card et al. 2005)
GUESS 2007 Networks Visualization A tool for visual graph exploration that integrates a scripting environment. Graphical Yes All Major (Adar 2007)
GraphViz 2004 Networks Visualization Flexible graph visualization software. Graphical Yes All Major (AT&T-Research-Group 2008)
NWB Tool 2006 Bio,
SocSci, Scientom
A + V Network analysis & visualization tool conducive to new algorithms supportive of many data formats. Graphical Yes All Major (Huang 2007.)
BibExcel 2006 Scientom A + V Transforms bibliographic data into forms usable in Excel, Pajek, NetDraw, and other programs. Graphical No Windows (Persson 2008)
Publish or Perish 2007 Scientom Data Collection and Analysis Harvests and analyzes data from Google Scholar, focusing on measures of research impact. Web-based No Windows, Linux (Harzing 2008)

Many of these tools are very specialized and capable. For instance, BibExcel and Publish or Perish are great tools for bibliometric data acquisition and analysis. HistCite and CiteSpace each support very specific insight needs – from studying the history of science to the identification of scientific research frontiers. The S&T Dynamics Toolbox provides many algorithms commonly used in scientometrics research and it provided bridges to more general tools. Pajek and UCINET are very versatile, powerful network analysis tools that are widely used in social network analysis. Cytoscape is excellent for working with biological data and visualizing networks.

The Network Workbench Tool has fewer analysis algorithms than Pajek and UCINET, and less flexible visualizations than Cytoscape. Network Workbench, however, makes it much easier for researchers and algorithm authors to integrate new and existing algorithms and tools that take in diverse data formats. The OSGi (http://www.osgi.org) component architecture and CIShell algorithm architecture (http://cishell.org) built on top of OSGi make this possible. Cytoscape is also adopting an architecture based on OSGi, though it will still have a specified internal data model and will not use CIShell in the core. Moving to OSGi will make it possible for the tools to share many algorithms, including adding Cytoscape's visualization capabilities to Network Workbench.

Several of the tools listed in the table above are also libraries. Unfortunately, it is often difficult to use multiple libraries, or sometimes any outside library, even in tools that allow the integration of outside code. Network Workbench, however, was built to integrate code from multiple libraries (including multiple versions of the same library). For instance, two different versions of Prefuse are currently in use, and many algorithms use JUNG (the Java Universal Network/Graph Framework). We feel that the ability to adopt new and cutting edge libraries from diverse sources will help create a vibrant ecology of algorithms.

Although it is hard to discern trends for tools which come from such diverse backgrounds, it is clear that over time the visualization capabilities of scientometrics tools have become more and more sophisticated. Scientometrics tools have also in many cases become more user friendly, reducing the difficulty of common scientometrics tasks as well as allowing scientometrics functionality to be exposed to non-experts. Network Workbench embodies both of these trends, providing an environment for algorithms from a variety of sources to seamlessly interact in a user-friendly interface, as well as providing significant visualization functionality through the integrated GUESS tool.

Many other tools are available outside the scope of network analysis that are still useful for studying the data of science. One such tool is the web-based Data Science Toolkit, a web-based collection of open-source data sets and tools which allows the user to query for geographical data, parse text, and run named entity recognition.

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