The document discusses graph algorithms and PageRank and how they can be implemented using MapReduce. It covers graph representations like adjacency matrices and sparse matrices that are suitable for distributed computing. It also describes how breadth-first search, shortest path finding, and PageRank calculations can be broken down into MapReduce jobs by iteratively processing portions of the graph in parallel. While not optimal for highly iterative algorithms, MapReduce can help distribute the computation across multiple machines to process large graphs.

1. Distributed Computing Seminar Lecture 5: Graph Algorithms & PageRank Christophe Bisciglia, Aaron Kimball, & Sierra Michels-Slettvet Summer 2007 Except as otherwise noted, the content of this presentation is © 2007 Google Inc. and licensed under the Creative Commons Attribution 2.5 License.

21. PageRank: Visually