Thanks, the algorithm is a heuristic which optimize a quantity I've called social cohesion (http://arxiv.org/pdf/1107.3231.pdf), the article presenting the algorithm itself is currently under review, but basically: first it computes the best possible group for selected edges in the graph, then it merge groups which overlap too much. The whole difficulty is to identify how to select those edges, how to expand around them and then what does "too much overlap" means. I'll link to the preprint as soon as I upload it to arxiv.
What do you mean exactly by "detect" issues? C^3 should work if there is a notion of transitivity in the network (that is, if A and B are similar and B and C are similar, then with very high probability A and C are similar).
During Camp Wellstone training (grassroots political organizing), they taught us a tool called a Power Map. The two axis are yes v no and influence. You graph each person voting. Then you draw arrows to show who influences who. It helps to identify where you should focus your lobbying.
One point that stood out is that a Power Map is needed to be done for each issue, because many many votes don't come down on partisan lines (eg local and state, ymmv).
You have a bipartite graph of bills and representatives. You use co-voting for the similarity metric to cluster representatives. Flip that around and cluster the bills using similarity of voters.
Can you use this to detect issue clusters rather than voting clusters?
I have tried to build subgroup-in-graph detectors with only partial success.