An extensive array of features is included, such as support for arbitrary vertex, edge or graph properties, efficient "on the fly" filtering of vertices and edges, powerful graph I/O using the GraphML, GML and dot file formats, graph pickling, graph statistics (degree/property histogram, vertex correlations, average shortest distance, etc.), centrality measures, standard topological algorithms (isomorphism, minimum spanning tree, connected components, dominator tree, maximum flow, etc.), generation of random graphs with arbitrary degrees and correlations, detection of modules and communities via statistical inference, and much more.

• 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