Fast consensus is an implementation of the fast consensus clustering procedure laid out in -
- Aditya Tandon, Aiiad Albeshri, Vijey Thayananthan, Wadee Alhalabi and Santo Fortunato: “Fast consensus clustering in complex networks”, arXiv:1902.04014; 2019
The procedure generates median or consensus partitions from multiple runs of a community detection algorithm. Tests on artificial benchmarks show that consensus partitions are more accurate than the ones obtained by the direct application of the clustering algorithm.
The script requires the following:
You can run the script with
python fast_consensus.py -f path_to_file/edgelist.txt [--alg algorithm] [-np n_p] [-t tau] [-d delta]
with the following options -
-f filename.txt
(Required) where filename.txt is an edgelist of the network.
The file can be of the form
0 1 0.5
0 4 1
1 3 0.3
.
.
.
where the first two numbers in each row are connected nodes and the third number is the edge weight. If only two numbers are provided the graph is treated as unweighted.
--alg algorithm
(Optional) Here algorithm is the community detection method used on the network and it can be one of louvain (Louvain algorithm), cnm (Fast greedy modularity maximization), lpm (Label Propagation Method), infomap (Infomap). If no algorithm is provided the script uses louvain for this purpose.
-np n_p
(Optional) n_p is the number of partitions created by repeated application of the community detection algorithm. If no value is provided, n_p = 20
-t tau
(Optional) tau is a float between 0 and 1. Elements of the consensus matrix with weight less than tau are set to zero in each step of the algorithm. If no value is provided, the code uses the value for which the chosen clustering algorithm gives the best performance on the LFR benchmark graph
-d delta
(Optional) delta should be a float between 0.02 and 0.1. The procedure ends when less than delta fraction of the edges have a weight not equal to 1. If no value is provided, delta is set to 0.02
python fast_consensus.py -f examples/karate_club.txt --alg louvain -np 50 -t 0.2 -d 0.1
The file examples/karate_club.txt is provided.
A folder out_partitions is created with n_p different files. Each file represents a partition; each line in the file lists all nodes belonging to a community.
For example, a run with n_p = 2 will create two files 1 and 2. Each file will be in the form:
0 1 2 5 7 8 9
3 4 6 10 11
This represents a partition with two communities : {0, 1, 2, 5, 7, 8, 9} and {3, 4, 6, 10, 11}