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For example, n = 10; A = spdiags([ones(n,1), zeros(n,1), ones(n,1)],[-1 0 1], n, n); cc = clustering_coefficients(A)’ % transpose the output for display comps = components(A)’ % transpose the output for display constructs a 10-node line graph as a Matlab sparse matrix, computes cluster- ing coefficients with the MatlabBGL clustering_coefficients function, and computes the index of a strongly connected component for each vertex. For both of these functions, the output consists of one number per vertex. For clustering_coefficients, it is the clustering coefficient of that vertex, and for components, it is the index of the strong component for that vertex. Examining the output shows that the clustering coefficient for each node is 0, which is expected for the line graph A, and that each node is in the same connected component, which is also expected for a line graph. To describe the library and the remainder of the implementation, we begin by reviewing the Boost graph library. 6.3.1 The Boost graph library (BGL) The Boost graph library or BGL [Siek et al., 2001] is a large set of C++ codes that implement generic graph algorithms. The advantage of these generic graph algorithms is that they specify the algorithm independently of the data structure. This independence is not accomplished using interfaces or ab- stract classes, which are common in standard object oriented programming. Instead, the Boost graph library uses C++ templates and techniques from generic programming [Alexandrescu, 2001] to write their data-structure-free algorithms. These techniques, in theory, allows the compiler to view the algo- rithm and data structure simultaneously and optimize the entire package. In particular, the compiler can generate in-line optimizations between function calls. In the Boost graph library, each algorithm places certain requirements on the C++ graph type. Concepts codify these requirements. In the BGL, the concepts largely express mutability (support for changing the graph during the algorithm) and access (support for querying the existing graph structure 6.3 ⋅ matlabbgl 135 cc = 0000000000 comps = 1111111111PDF Image | Instagram Cheat Sheet
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