Boost graph library

From TrustLet, a free, collaborative project for collecting and analyzing information about trust metrics.

Jump to: navigation, search

The Boost Graph Library Python bindings ("BGL-Python") expose the functionality of the Boost Graph Library and Parallel Boost Graph Library as a Python package, allowing one to perform computation-intensive tasks on graphs (or networks) from within an easy-to-use scripting language.

BGL-Python contains many of the data structures and algorithms found in the C++ (Parallel) Boost Graph Library, including:

  • Undirected and directed adjacency list data structures
  • Arbitrary properties can be attached to vertices and edges.
  • Breadth-first and depth-first search
  • Single-source shortest paths (Dijkstra, Bellman-Ford)
  • Betweenness centrality
  • Graph layout algorithms (circle, Kamada-Kawai, Fruchterman-Reingold)
  • Connected, biconnected, and strongly-connected components
  • Sparse matrix ordering (minimum degree ordering, King, Cuthill-McKee)
  • Minimum spanning tree algorithms (Prim, Kruskal)
  • Topological sort
  • Transitive closure
  • GraphViz file input/output.

There is a Powerpoint presentation on "Large-Scale Network Analysis with the Boost Graph Libraries" (.ppt file) which might be useful.

Retrieved from "http://www.trustlet.org/wiki/Boost_graph_library"