Members of a social network user's social graph are automatically segregated into overlapping clusters according to patterns of their past communications. Each cluster within the social graph represents a group of members having a high degree of intra-cluster communication or other connection with one another. The clustering is performed according to a sorting or ranking in accordance with non-principal eigenvectors of connectivity matrices describing the intra-cluster communications/connections. The overlapping clusters exhibit maximum internal density and minimum external sparsity.