WERW-Kpath (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.
WERW-Kpath is computationally efficient since its cost is near linear with respect to the number of edges in the network.
You can also use WERW-Kpath 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 WERW-Kpath. Please cite those which are relevant to your purposes if you use WERW-Kpath.
P De Meo, E Ferrara, G Fiumara and A Ricciardello. A novel measure of edge centrality in social networks. Knowledge-based Systems, 30:136-150, 2012
P De Meo, E Ferrara, G Fiumara, and A Provetti. Enhancing community detection using a network weighting strategy. Information Sciences, 222:648-668, 2013
P De Meo, E Ferrara, G Fiumara, and A Provetti. Mixing local and global information for community detection in large networks. Journal of Computer and System Sciences, 80(1):72-87, 2014
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