Abstract: A computer based method is provided for clustering related data representing objects of interest and information about levels of relatedness between objects. A weighted graph G is established on a computer. The graph has vertices and weighted edges joining pairs of vertices. Using the computer, the method finds all possible subgraphs H of G satisfying the following dynamic “edge-to-vertex” ratio: ? min ? ? P ? ? E ? ( H / P ) ? ? P ? - 1 > k where the minimum is taken over all possible partitions P of the vertex set of H, and E(H/P) is the set of edges crossing between parts of P. The subgraphs H found are identified as a level-k community if they are maximal, which means that there are no larger subgraphs containing it that satisfy the dynamic “edge-to-vertex” ratio for the same k. All level-k communities are output.

Abstract: A computer based method is provided for clustering related data representing objects of interest and information about levels of relatedness between objects. A weighted graph G is established on a computer. The graph has vertices and weighted edges joining pairs of vertices. Using the computer, the method finds all possible subgraphs H of G satisfying the following dynamic “edge-to-vertex” ratio: ? ? min ? ? ? P ? ? E ? ( H / P ) ? ? P ? - 1 > k where the minimum is taken over all possible partitions P of the vertex set of H, and E(H/P) is the set of edges crossing between parts of P. The subgraphs H found are identified as a level-k community if they are maximal, which means that there are no larger subgraphs containing it that satisfy the dynamic “edge-to-vertex” ratio for the same k. All level-k communities are output.