Pajek - Program for Large Network Analysis

By V. Batagelj, Andrej Mrvar. 1999

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Olaf Sporns
2. Yong He
3. Gaolang Gong
4. Alan C. Evans
5. D. Bassett
6. E. Bullmore
7. Jure Leskovec
8. Jinhui Wang

Citations in the corpus

• ForceAtlas2, a Continuous Graph Layout Algorithm for Handy Network Visualization Designed for the Gephi Software (2014, methodology): “Three reference softwares inspired us: Pajek [3], GUESS [4] and TouchGraph.”
• Cytoscape: a software environment for integrated models of biomolecular interaction networks. (2003, positive): “The first plug-in has been instrumental in two previously published research projects (Begley et al. 2002; Ideker et al. 2002), whereas the second and third plug-ins are at earlier stages of research and development.”
• Some bibliometric procedures for analyzing and evaluating research fields (2017, unknown): “Taking into account the input and output, citation networks can be imported directly into CitNetExplorer from the Web of Science database, and the whole network can be exported into Pajek file format [4].”
• Observing and Interpreting Correlations in Metabolic Networks (2003, methodology): “The position of the vertices were determined with the Kamada‐Kawai algorithm, as implemented in the software package Pajek ( Batagelj and Mrvar, 1998 ), using the full correlation matrix.”
• Analysis and Visualization of Network Data using JUNG (2005, methodology): “• Expansion of the input and output options, including full implementation of database connectivity (as discussed in Section 10.2), ability to parse more complex Pajek and GraphML files, and support for other network file formats, such as the .dl and DOT formats defined by the UCINET and Graphviz (Ells”
• Business Intelligence and Analytics: From Big Data to Big Impact (2012, methodology): “Tools such as UCINet (Borgatti et al. 2002) and Pajek (Batagelj and Mrvar 1998) have been developed and are widely used for large-scale network analysis and visualization.”
• Using graph theory to analyze biological networks (2011, methodology): “Many visualization tools [144] such as Medusa [148], Cytoscape [149], Pajek [98] and many others [144] visualize networks in both 2D and 3D, but very few of them like Arena3D [150] try to bridge the gap between clustering analysis and visualization.”
• Mapping the Structural Core of Human Cerebral Cortex (2008, methodology): “To visualize structural patterns within this connection matrix, we extracted the connectivity backbone ([30], see Methods), which is displayed in Figure 3B with a layout derived from the Kamada-Kawai force-spring algorithm [31] implemented in Pajek [ 32 ].”
• Social network analysis: A methodological introduction (2008, methodology): “Examples of such schemes include the Fruchterman-Reingold (Fruchterman & Reingold, 1991) and Kamada-Kawai algorithms (Kamada & Kawai, 1989), both of which may be found in common network visualization and analysis packages (Butts, 2000; Batagelj & Mrvar, 2007; Borgatti, 2007).”
• Visualization of interfirm relations in a converging mobile ecosystem (2008, background): “Pajek is a general, non-commercial program, for analysis and visualization of very large and complex networks (Batagelj and Mrvar, 1998).”