includes implementations of a number of algorithms from graph theory, data mining, and social network analysis, such as routines for clustering, decomposition, optimization, random graph generation, statistical analysis, and calculation of network distances, flows, and importance measures (centrality, PageRank, HITS, etc.).

• Python language data structures for graphs, digraphs, and multigraphs. • Many standard graph algorithms • Network structure and analysis measures • Generators for classic graphs, random graphs, and synthetic networks • Nodes can be "anything" (e.g. text, images, XML records) • Edges can hold arbitrary data (e.g. weights, time-series) • Open source BSD license • Well tested: more than 1800 unit tests, >90% code coverage • Additional benefits from Python: fast prototyping, easy to teach, multi-platform