This is an alternative Python implementation of hierarchical link communities in complex networks, published by Ahn et al [1]. The implementation is based on the igraph module of Python and is somewhat faster than the original Python implementation. Of course it cannot compete with the C++ version yet -- pushing down the Jaccard similarity calculation to igraph's C core would probably help in the long run.
Should be self-explanatory, just run ./hlc.py and you should get a
short help message.
To run a single clustering on a graph, selecting the threshold automatically:
$ ./hlc.py filename
The implementation supports weighted graphs and converts directed graphs to undirected ones automatically. However, weights are lost when converting a directed graph to an undirected one unless you are using igraph 0.6 or above.
Results are written to the standard output, one cluster per line. Communities
with two nodes only are excluded. Use the -t switch to specify the
similarity threshold explicitly and -s to control the minimum size of
clusters to be reported.
This implementation on the University of South Florida word association network (weighted, converted to undirected):
$ time ./hlc.py data/freeassoc.txt >freeassoc_clusters.txt Processing data/freeassoc.txt Calculating clusters, please wait... Threshold = 0.200000 D = 0.041974 real 0m15.246s user 0m15.010s sys 0m0.220s
The original Python implementation:
$ time ./link_clustering.py data/freeassoc.txt -d ' ' # loading network from edgelist... clustering... computing similarities... # D_max = 0.041974 # S_max = 0.200000 real 2m32.575s user 2m31.770s sys 0m0.750s
Note that both the threshold and the D value is the same, which is reassuring. Let's comparing the number of clusters as well:
$ wc -l freeassoc_clusters.txt
7676 freeassoc_clusters.txt
$ awk '{ if (NF > 3) print }' freeassoc_maxS0.2*.comm2nodes.txt | wc -l
7676
(Note that the result file from the original implementation includes
communities with only two nodes as well, and the first column in the result
is the index of the community, that's why we needed that awk magic to
count the number of communities with at least 3 vertices).
| [1] | Ahn YY, Bagrow JP and Lehmann S: Link communities reveal multiscale complexity in networks. Nature 466, 761 (2010). |