### Highlights from gaimc : Graph Algorithms In Matlab Code

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# gaimc : Graph Algorithms In Matlab Code

### David Gleich (view profile)

Efficient pure-Matlab implementations of graph algorithms to complement MatlabBGL's mex functions.

File Information
Description

While MatlabBGL uses the Boost Graph Library for efficient graph routines,
gaimc implements everything in pure Matlab code. While the routines are
slower, they aren't as slow as I initially thought. Since people often
have problems getting MatlabBGL to compile on new versions of Matlab
or on new architectures, this library is then a complement to MatlabBGL.

See the published M-files for a few examples of the capabilities.

Functions
depth first search (dfs)
connected components (scomponents)
maximum weight bipartite matching (bipartite_matching)
Dijkstra's shortest paths (dijkstra)
Prim's minimum spanning tree (mst_prim)
clustering coefficients (clustercoeffs)
directed clustering coefficients (dirclustercoeffs)
core numbers (corenums)

The project is maintained at github : http://github.com/dgleich/gaimc/tree/master

Acknowledgements

Matlab Bgl inspired this file.

This file inspired Fgt Fold Geometry Toolbox.

MATLAB release MATLAB 7.5 (R2007b)
09 Jun 2014 Ernesto Céspedes Montero

### Ernesto Céspedes Montero (view profile)

23 May 2013 Lingji Chen

### Lingji Chen (view profile)

Great work!

There seems to be a bug in dfs(): The line

else v=mod(u+i-1,n)+1; if d(v)>0, continue; end, end

should be

else v=mod(u+i-1-1,n)+1; if d(v)>0, continue; end, end

22 Jul 2011 Alok

### Alok (view profile)

David/all:

Under what license is this available? Can I use this for commercial purposes?

Thanks for the great work.

Comment only
25 Feb 2011 Leon

### Leon (view profile)

Thanks for your excellent library! It's great to be able to see how you've solved the technical issues. Good work!

I believe there is a small bug in your clusteringcoeffs.m code, Tt occurs when you try and calculate unweighted clustering with a normal format network (not csr).

Calling clustercoeffs.m(A,0) always exits with

"Error in ==> clustercoeffs at 42
if any(ai)<0, error('gaimc:clustercoeffs',... "

This is because ai is not defined; the easy solution is to nest:
if any(ai) <0, error( ....
end
within the "if usew, [rp ci ai]..." loop.

if usew, [rp ci ai]=sparse_to_csr(A);
else [rp ci]=sparse_to_csr(A);
end
if any(ai)<0, error('gaimc:clustercoeffs',...
['only positive edge weights allowed\n' ...
'try clustercoeffs(A,0) for an unweighted comptuation']);
end

proposed fix (lines 39-45)
if usew, [rp ci ai]=sparse_to_csr(A);
if any(ai)<0, error('gaimc:clustercoeffs',...
['only positive edge weights allowed\n' ...
'try clustercoeffs(A,0) for an unweighted comptuation']);
end
else [rp ci]=sparse_to_csr(A);
end

Hope that helps,
L.

25 Nov 2010 Forrest Bao

### Forrest Bao (view profile)

18 May 2009 Jeremy Kozdon