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[igraph] calculating centrality separately for each connected component
From: |
Michael Bishop |
Subject: |
[igraph] calculating centrality separately for each connected component |
Date: |
Tue, 10 May 2011 14:26:38 -0500 |
Even when I use sparse matrices, I'm having difficulty calculating measures of centrality on a large graph (less than 100,000 vertices and 300,000 edges)
I thought of a workaround though which would be helpful to me in its own right. Due to the way the data was connected, there are no large connected components. Therefore, if I can run functions like alpha.centrality() and bonpow() separately on each connected component my system should be able to handle it without crashing. Even if this results in a slightly different measure, I don't think the difference is substantively important.
Given that I'm starting with a giant igraph object, how should I proceed?
For others with similar interests, I include Gabor's previous wisdom and links on how to implement these functions with sparse matrices:
Hmmmm, 'alpha.centrality' is not very good, it uses the adjacency
matrix, it is thus inappropriate for large graphs. Here is what
you can do using the 'Matrix' package:
and it is possible to use sparse matrices for bonpow as well, see e.g.
- [igraph] calculating centrality separately for each connected component,
Michael Bishop <=