WERWKpath (Weighted Edge Random Walks – K Path) is a fast algorithm to weight edges in complex networks exploiting global topological features adopting Simple Random Walks of bounded length (up to K).
The strategy relies on three steps:
 initial weight assignment;
 message propagation simulations on the network exploiting Simple Random Walks of fixed length up to K;
 final weight computation.
WERWKpath is computationally efficient since its cost is near linear with respect to the number of edges in the network.
You can also use WERWKpath to weight a network and then running an optimized community detection algorithm such as CONCLUDE to enhance performance and quality of results.
The only condition of use of this algorithm is the following:
 The corresponding papers are cited
Related Papers
The following papers are all related to WERWKpath.
Please cite those which are relevant to your purposes if you use WERWKpath.

A novel measure of edge centrality in social networks.
P De Meo, E Ferrara, G Fiumara and A Ricciardello.
Knowledgebased Systems, 30:136150, 2012
PDF  Journal page 
Enhancing community detection using a network weighting strategy.
P De Meo, E Ferrara, G Fiumara, and A Provetti.
Information Sciences, 222:648668, 2013
PDF  Journal page 
Mixing local and global information for community detection in large networks.
P De Meo, E Ferrara, G Fiumara, and A Provetti.
Journal of Computer and System Sciences, 80(1):7287, 2014
PDF  Journal page
Download WERWKpath
You can download an early version of WERWKpath from HERE.
The package contains the following user guide.
USER GUIDE ********** To launch the WERWKpath algorithm type:
 java jar werwkpath.jar inputfilename outputfilename kpathlength delimiter(default: tabseparatedvalue)
 java jar werwkpath.jar facebooklinks.txt weightedfacebooklinks.txt 10
 java Xmx4G jar werwkpath.jar facebooklinks.txt weightedfacebooklinks.txt 10
 java Xmx4G jar werwkpath.jar facebooklinks.txt weightedfacebooklinks.txt " " 10