Re: [igraph] clustering coefficients for bipartite networks

[jordi torrents]

Hi Tamás,

2011/2/24 Tamas Nepusz <address@hidden>:
> One thing that seems to be different in your code and
> in the authors' (but I haven't checked it thoroughly) is that when they
> calculate the second-order neighbors of the original vertex, they
> exclude the vertex itself while you don't. This means that your sums and
> averages will include the vertex's "similarity" (cc_dot, cc_min, cc_max)
> with itself, which is always going to be 1.

Thank you very much for your help. This was the problem in my
implementation. We must exclude the focal node from the second-order
neighbors because, as you say, if we do not, the sums and averages
will include the node pairwise CC with itself (which will always be
1). This error was especially problematic in the case of top (or
bottom) nodes that are only connected to bottom (or top) nodes (eg an
author that has only published solo authored papers in arXiv), because
their CC must be 0, but with my original implementation it would be 1.
Thus biasing a lot the CC computation upwards.

I've rerun the test with the modifications that you've proposed (see
script attached) and now it yields the same results that the authors
report in the paper. I've also tested the new implementation in other
networks and the results are much more plausible. So I'm willing to
accept that the implementation is correct. The output of the script
attached is:

=============================================================
address@hidden:~$ python test_bip_cc_correct.py
Code based on the paper:
Matthieu Latapy, Clémence Magnien and Nathalie Del Vecchio.
Basic Notions for the Analysis of Large Two-mode Networks.
Social Networks 30 (1), p. 31-48, 2008

Loading coauthorship network from the paper. You can download it from:
http://www.milnou.net/~jtorrents/bipartite_cc/coauthoring_latapy_2mode.graphml

The authors report the results in Table 3 p. 41
cc_dot_top = 0.29; cc_dot_bottom= 0.31
cc_min_top = 0.56; cc_min_bottom= 0.73
cc_max_top = 0.36; cc_max_bottom= 0.33

Testing implementation of cc_dot:
cc_dot_top = 0.29; cc_dot_bottom= 0.31; time spend = 2 seconds

Testing implementation of cc_min:
cc_min_top = 0.56; cc_min_bottom= 0.73; time spend = 2 seconds

Testing implementation of cc_max:
cc_max_top = 0.36; cc_max_bottom= 0.33; time spend = 2 seconds

The results now do match!
Thanks for your help Tamás ;)
==================================================================

Salut!

PS: Would you (and Gabor) consider to add bipartite clustering
coefficients to igraph 0.6? That would be very useful for me because
many of the networks that I have to deal with are bipartite. And, as
Latapy et al (2008) convincingly show, we do loss a lot of interesting
information if we use the usual procedure of only analyze one mode
projections.

test_bip_cc_correct.py
Description: application/python