Pajek 5.10

Analyze and visualize network structures
Access the data and settings of large networks and run an analysis of their structure and functionality. Check out the number, placement, and validity of subnetworks, partitions, vectors, monitor all permutations, view the clusters and their hierarchy. Manage the displayed data.

Pajek, PajekXXL and Pajek3XL are program packages for analysis and visualization of very large networks (networks containing up to ten billions of vertices, there is no limit - except the memory size - on the number of lines). Pajek has been available since 1996. The program, documentation and supporting material can be downloaded and used for free for noncommercial use from its web page: In Pajek analysis and visualization of large networks are performed using six data types (objects): network (graph); partition (nominal or ordinal properties of vertices); vector (numerical properties of vertices); cluster (subset of vertices); permutation (reordering of vertices, ordinal properties); and hierarchy (general tree structure on vertices). The main goals in the design of Pajek are: to support abstraction by (recursive) decomposition of a large network into several smaller networks that can be treated further using more sophisticated methods; to provide the user with some powerful visualization tools; to implement a selection of efficient (subquadratic) algorithms for analysis of large networks. According to main goals, Pajek contains several basic operations on its objects. Pajek is not 'a one click program', some users call it 'the network calculator'. That means that for obtaining some result several basic operations must be executed in a sequence. In fact possibility to combine different basic operations gives Pajek a special power. In addition to standard Pajek there exist also special versions PajekXXL and Pajek3XL. PajekXXL and Pajek3XL are a special editions of program Pajek which memory consumptions are much lower. PajekXXL has upper limit two billions and Pajek3XL ten billions of vertices. For the same sparse network they need at least 2?3 times less physical memory than Pajek. Operations that are memory intensive are therefore much faster. They are usually used for huge networks that do not fit to available computer memory.

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