The document summarizes the Boost Graph Library (BGL) family of libraries for graph computation. The BGL includes sequential, Python, and parallel versions. The original BGL in C++ is efficient and customizable. BGL-Python provides access to BGL algorithms from Python. The Parallel BGL enables graph processing on clusters for huge graphs over millions of nodes.

1. Large-Scale Network Analysis with the Boost Graph Libraries Douglas Gregor Open Systems Lab Indiana University [email_address]

7. Task: Biconnected Components Input Graph Output Graph Articulation points: B G A Intro BGL

12. Task: Biconnected Components Input Graph Output Graph Articulation points: B G A Intro BGL

15. Example: Biconnected Components import boost.graph as bgl # Pull in the BGL bindings g = bgl.Graph.read_graphviz("biconnected_components.dot") # Compute biconnected components and articulation points bicomponent = g.edge_property_map(‘int’) art_points = bgl.biconnected_components(g, bicomponent); # Save results with bicomponent numbers as edge labels g.edge_properties[‘label’] = bicomponent g.write_graphviz("biconnected_components_out.dot") print "Articulation points: ", node_id = g.vertex_properties[‘node_id’] for v in art_points: print node_id[v],’ ’, print "" Intro BGL Python

17. Performance: Shortest Paths Intro BGL Python

21. Parallel BGL: Distributed Graphs distributed across 3 processors. Intro BGL Parallel Python A simple, directed graph…

23. Performance: Sparse graphs

24. Scalability (~547k vertices/node) Up to 70M Vertices 1B Edges Small-World Graph

25. Performance vs. CGM graph 96k vertices 10M edges Erdos-Renyi 17x 30x Intro BGL Parallel Python

27. Distributed Graph Layout Intro BGL Parallel Python

29. Porting for Performance Intro BGL Parallel Python Porting

34. Comparative Performance Intro BGL