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Pajek

Pajek runs on Windows and is free for noncommercial use.
It also runs on Linux (64) or Mac.
Pajek manual. History.

Pajek 3.* and Pajek 4.*

Pajek3.* and Pajek 4.* are a significantly reconstructed new versions of 'standard' Pajek. Faster and more efficient memory managers are included. All menu items that were available in Pajek 2.* are available also in Pajek 3.* and Pajek 4.* . But some of them are on new positions.

Pajek 3.* and Pajek 4.* for Windows 32 bit

Download the latest installation pack: mirror1 / mirror2 / mirror3. To install it run Pajek32 and follow the installer messages.

Pajek 3.* and Pajek 4.* for Windows 64 bit

Download the latest installation pack: mirror1 / mirror2 / mirror3. To install it run Pajek64 and follow the installer messages.

Pajek 2.*

Pajek for Windows 32 bit

Download the ESNA 2version 2.05 / Book edition 2, (September 25, 2011) - installation pack. To install it run pajekBE2 and follow the installer messages. All data sets, macro files, and R scripts used in the book are avaliable here.
Download the previous version 2.04, (May 16, 2011) of Pajek installation pack. To install it run pajek204 and follow the installer messages.
Download the ESNA Book Edition version (October 1, 2004) of Pajek.

Pajek for Windows 64 bit

On Windows 64 bit a special 32 bit version of Pajek can use up to 4GB of available computer memory. Download the version 2.05 - 32bit / 4G, (September 25, 2011) of Pajek installation pack. To install it run pajek4G-205 and follow the installer messages.

The genuine 64 bit version of Pajek was published on September 25, 2011. It can use all the available computer memory. Download the version 2.05 - 64bit, (September 25, 2011) of Pajek installation pack. To install it run pajek64-205 and follow the installer messages.

PajekXXL

PajekXXL / mirror is a special edition of program Pajek. Its memory consumption is much lower. For the same network it needs at least 2-3 times less physical memory than Pajek. Operations that are memory intensive are also faster.

Sparse networks containing up to 100 millions of vertices can be analysed on computers with 4G RAM memory. Networks containing up to one billion of vertices can be analysed with 16G RAM (or more) using PajekXXL.

Python library TQ (Temporal Quantities)

The Python library TQ supports the analysis of temporal networks based on temporal quantities.

Data sets

Slides

San Diego Sunbelt XXIX workshop: slides 1, slides 2, data.

Download tools related to Pajek:

download.txt · Last modified: 2014/09/02 09:50 by andrej
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