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158 6 ⋅ software reproducible research There is some cause for hope. Jon Claer- bout at Stanford has long advocated reproducible research for scientific compu- tations [Schwab et al., 2000]. Others have adopted his philosophy [Buckheit and Donoho, 1995; Donoho et al., 2009]. Indeed, an entire issue of the Com- puting in Science and Engineering journal was recently devoted to this topic [Fomel and Claerbout, 2009]. The software packages of this thesis fit the general motivation of reproducible research, although some of the specifics differ. summary Three software packages support the computations in this thesis. matlabbgl This library brings the power of the Boost graph library to Matlab. It uses Matlab’s own sparse matrix type as the adjacency matrix of a graph and supports a wide range of graph analysis algorithms. gaimc In contrast with MatlabBGL, the gaimc package is small and com- pact, but adds graph algorithms to Matlab in the same way. It is de- signed for cases when performance is not paramount. libbvg/bvgraph These paired libraries make it easy to work with ex- tremely large web graphs in C and Matlab. Internally, they use graphs compressed in the Boldi-Vigna scheme [Boldi and Vigna, 2005], and keep them compressed throughout the computations. Finally, we briefly highlight three publication packages that make all the results of two publications and this thesis completely reproducible.PDF Image | Instagram Cheat Sheet
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