Solved – Clustering a fully connected graph

I've a graph representing a social network ( 597 nodes, 177906 edges). Each edge has a weight saying how much two nodes are similar. I'd like to apply some clustering algorithm to this network but I think I need to cut some edge. Is there a commonly used threshold to do this?
Can you suggest any particular algorithm? I was suggested to use K-means but I think it badly fit to my data space.

600 nodes is tiny, so you shouldn't have scalability problems.

Try:

  1. Hierarchical agglomerative clustering (implement it for similarity, not distance!)

  2. Spectral clustering

  3. Affinity propagation

  4. K-medoids with affinity

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