GraphCrunch
A Tool for Large Network Analyses
About
T. Milenkovic, J. Lai, and N. Przulj, GraphCrunch: A Tool for Large Network Analyses, BMC Bioinformatics, 9:70, January 30, 2008. Highly accessed.
GraphCrunch is a software tool for analyzing large biological and other real-world networks and comparing them against random graph models. Finding adequate null-models for biological networks is a challenge and thus GraphCrunch addresses this research problem. It generates random networks (with the number of nodes and edges within 1% of those in the real-world networks) for user-specified random graph models and evaluates the fit of a variety of network models to real-world networks with respect to a series of global and local network properties. Furthermore, it can be easily upgraded to include additional network models and properties. GraphCrunch has parallel computing capabilities: processing can be performed in a parallel fashion by distributing jobs over a compute cluster.
GraphCrunch is created by T. Milenković, J. Lai, and N. Pržulj at the School of Information and Computer Science at UC Irvine.
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On-line GraphCrunch
Want to try out some of GraphCrunch's features without setting things up on your computer? Try On-line GraphCrunch (recommended for first-time users).
Download
GraphCrunch runs under Linux, MacOS, and Windows Cygwin. The versions for Linux and MacOS are statically compiled and thus are ready to use after downloading and unpacking the compressed archive. Due to the licensing issues, the Windows Cygwin version requires the LEDA Cygwin license and the gcc compiler (we recommend LEDA 5.0.1 and the gcc 3.4). See the Documentation for further details.
Get the GraphCrunch software package:
· for Cygwin (instructions for Cygwin)
System Requirements
· We recommend that Perl 5.6+ as well as dialog 0.3+ or Xdialog are also installed for each of the three operating systems.
· The system needs to have up to 20MB of disk space available (depending on the operating system) for installing GraphCrunch.
· Note that processing a large number of model networks may put a demand on the available disk space in the system (storing a single network takes about 600 KB of disk space). We recommend processing of up to 30 networks per network model.
Documentation
Acknowledgments
This project was supported by the NSF CAREER IIS-0644424 grant.