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Highlights from
MatlabBGL

MatlabBGL

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30 Apr 2006 (Updated )

MatlabBGL provides robust and efficient graph algorithms for Matlab using native data structures.

new_in_3_0.m
%% New features in MatlabBGL version 3.0
% Although MatlabBGL 3.0 was never officially released, here are some of
% it's key features.

%% Better performance
% We redid the backend interface to the BGL routines.  This optimization
% gave a considerable performance increase.

%%
% test_benchmark on MatlabBGL 2.1
%
%  2008-10-07, Version 2.1, Matlab 2007b, boost 1.33.0, 
%    g++-3.4 (lib), gcc-? (mex)
%
%         airfoil       west    cs-stan    minneso      tapir   
%  large   0.223 s    0.024 s    0.390 s    0.073 s    0.046 s  
%    med     NaN s    0.955 s      NaN s      NaN s    6.621 s  
%  small     NaN s    0.758 s      NaN s      NaN s      NaN s  

%% 
% test_benchmark on MatlabBGL 3.0
%
%  2008-10-07: Version 3.0, Matlab 2007b, boost 1.34.1, 
%    g++-4.0 (lib), gcc-? (mex)
%
%         airfoil       west    cs-stan    minneso      tapir   
%  large   0.183 s    0.017 s    0.222 s    0.048 s    0.037 s  
%    med     NaN s    0.593 s      NaN s      NaN s    3.901 s  
%  small     NaN s    0.543 s      NaN s      NaN s      NaN s  

%% Graph construction functions
% MatlabBGL 2.1 had a few graph construction functions.  MatlabBGL 3.0 adds
% the grid_graph function for line, grid, cube, and hyper-cube graphs

[G xy] = grid_graph(6,5); gplot(G,xy,'.-');

%% 
% In more dimensions...

[G xyz] = grid_graph(6,5,3);
G = grid_graph(2,2,2,2);
G = grid_graph([3,3,3,3,3]);

%% Targeted search
% The graph search algorithms now let you specify a target vertex that will
% stop the search early if possible. 

A = grid_graph(50,50);
tic; d = bfs(A,1,struct()); toc
tic; d = bfs(A,1,struct('target',2)); toc 

%%
% Also implemented for astar_search, shortest_paths, and dfs.

%% Edge weights
% In Matlab, there is no way to create a sparse matrix with a structural
% non-zero (used for MatlabBGL edges) and a value of 0 (used for MatlabBGL
% weights).  Consequently, it's impossible to run algorithms on graphs
% where the edge weights are 0. 
%
% Consequently, some algorithms now take an 'edge_weight' parameter that
% allows you to provide a different set of edge weights which allow
% structural non-zeros and 0 values.
%
% This behavior is a bit complicated, so see the REWEIGHTED_GRAPHS example
% for more information.

%% Matching algorithms
% While maximum cardinality bipartite matching is just a call to max-flow,
% general graph matching algorithms are not.  MatlabBGL 3.0 contains the
% matching algorithms in Boost 1.34.0.

load('../graphs/matching_example.mat');
m = matching(A);
sum(m>0)/2 % matching cardinality should be 8


%% New graph statistics
% We added a few new statistics functions.

%%
% Test for a topological ordering of a graph (only applies to DAGs or
% directed acyclic graphs)

n = 10; A = sparse(1:n-1, 2:n, 1, n, n); % construct a simple dag
p = topological_order(A);

test_dag(A)
test_dag(cycle_graph(6)) % a cycle is not acyclic!

%% 
% Core numbers can help identify important regions in a graph.  MatlabBGL
% includes weighted and directed core numbers.  Also, the algorithms return
% the removal time of a particular vertex, which gives interesting graph
% orderings.

% See EXAMPLES/CORE_NUMBERS_EXAMPLE

%%
% New algorithms for clustering_coefficients on weighted and directed
% graphs.

A = clique_graph(6) - cycle_graph(6); % A is a clique - a directed cycle
ccfs = clustering_coefficients(A)
B = sprand(A);
ccfs = clustering_coefficients(B)
C = A|A'; % now it's a full clique again
ccfs = clustering_coefficients(C)

%% Max-flow algorithms
% Since Boost added the Kolmogorov max-flow function, we added the
% full collection of flow algorithms to MatlabBGL.

load('../graphs/max_flow_example.mat');

push_relabel_max_flow(A,1,8)
kolmogorov_max_flow(A,1,8)
edmunds_karp_max_flow(A,1,8)

max_flow(A,1,8,struct('algname','push_relabel'));
max_flow(A,1,8,struct('algname','kolmogorov'));
max_flow(A,1,8,struct('algname','edmunds_karp'));

%% Dominator tree
% Dominator trees are relations about presidence in certain types of
% graphs.  These are also called flow-graphs.

load('../graphs/dominator_tree_example.mat');
p = lengauer_tarjan_dominator_tree(A,1);


%% New utility functions
% MatlabBGL 3.0 introduces some new utility functions.  

%%
% The output of a shortest path algorithm is a predecessor matrix.  To
% convert these predecessor relationships to a path, use the path_from_pred
% function.

[A xy] = grid_graph(6,5); n= size(A,1);
[d dt pred] = bfs(A,1); % 
path = path_from_pred(pred,n) % sequence of vertices to upper corner

%% 
% Let's draw the path
gplot(A,xy,'r.-'); 
[px,py]=gplot(sparse(path(1:end-1),path(2:end),1,n,n),xy,'-');
hold on; plot(px,py,'-','LineWidth',2); hold off;

%%
% We can also create a full shortest path tree using the tree_from_pred
% function.
T = tree_from_pred(pred);
gplot(A,xy,'r.-'); 
[px,py]=gplot(T,xy,'-');
hold on; plot(px,py,'-','LineWidth',2); hold off;

%%
% Finally, there are a few new routines to make working with reweighted
% graphs easier.  See EXAMPLES/REWEIGHTED_GRAPHS for information about
% the INDEXED_SPARSE and EDGE_WEIGHT_INDEX functions.

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