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The edge weight vector ws also satisfies the following property: [i j] = find(As); Aw = sparse(j,i,ws,size(As,1),size(As,2)); isequal(Aw,A) ans = 1 In other words, the order of the edge weight vector corresponds to the order of non-zeros in the transposed matrix. The function has to return weights in the order of the transposed matrix because MatlabBGL transposes the sparse matrix on a standard function call. Using the vector is easy. The trivial usage [d pred] = shortest_paths(A,1); d(2) gives the wrong answer: ans = 1 Instead, use [d pred] = shortest_paths(As,1,struct(’edge_weight’,ws)); d(2) to find the correct distance using all the zero-weight edges: ans = 0 6.3.7 Usage MatlabBGL has been used in over 10 publications (that we are aware of) and downloaded over 7,000 times. We highlight three of these publications. In Honey et al. [2007], the authors utilize the shortest path computations to analyze the functional connectivity of a macaque neocortex. Next, Lin et al. [2008] use the connected component, betweenness centrality, and cluster coefficients functions to study a network of co-authorship relationships in abstracts presented at the Society for Neuroscience. Third, and finally, Raman and Chandra [2008] use the shortest path routine to analyze a subset of the interactome network of M. tuberculosis. MatlabBGL continues to be a highly downloaded file from the Matlab central file exchange and we hope to continue improving it in the future. 6.3 ⋅ matlabbgl 129PDF Image | MODELS AND ALGORITHMS FOR PAGERANK SENSITIVITY
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