> How about using this paper by our own Dr Nepusz? :) > http://arxiv.org/abs/0707.1646 That's nice :) Actually, the clustering algorithm described in that paper probably won't scale up to the size of the graph you are working with. However, you can probably still make use of the "bridgeness" measure by "making up" membership scores for each vertex and each cluster as follows. Let vertex i belong to cluster j with a score that corresponds to the total weight of edges connecting vertex i to members of cluster j, divided by the total weight of edges incident on vertex i. You can then either calculate "bridgeness" scores from these membership scores.
Alternatively, you can calculate the exponentiated entropy of the membership score vector corresponding to a single vertex -- this gives you the "effective number of clusters" that the vertex belongs to. For instance, if 60% of the edges of a vertex connect the vertex to cluster 1, 30% of the edges connect it to cluster 2 and 10% connect it to cluster 3, the membership vector is defined as [0.6, 0.3, 0.1]. The exponentiated entropy of this vector is then exp(-(0.6*log(0.6) + 0.3*log(0.3) + 0.1*log(0.1))), which tells us that the vertex effectively belongs to 2.45 clusters. If the membership vector were [0.9, 0, 0.1], the exponentiated entropy would have yielded exp(-0.9*log(0.9)-0.1*log(0.1)) 1.38. You can then set an arbitrary threshold and say that the bridges are those vertices for which the exponentiated entropy is above 1.5. T. _______________________________________________ igraph-help mailing list [email protected] https://lists.nongnu.org/mailman/listinfo/igraph-help
