The strategy consists of three steps:
- (re)weight edges by using a particular random walker;
- calculate the distance between each pair of connected nodes;
- partition the network into communities so to optimize the weighted network modularity.
CONCLUDE is computationally efficient since its cost is near linear with respect to the number of edges in the network. The adoption of this community detection method has been proved worthy in different contexts, such as for studying online social networks and biological networks.
The only term of usage of this algorithm is the following: The corresponding papers are cited
Related Papers
Please cite this work if you use CONCLUDE:
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).
CONCLUDE is an improved version of the algorithm presented in the following paper, you might cite too:
P De Meo, E Ferrara, G Fiumara, and A Provetti. Generalized Louvain method for community detection in large networks. ISDA ’11: Proceedings of the 11th International Conference on Intelligent Systems Design and Applications, 2011.
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