Current Slide


Graph Clustering: Sparsest Cut

  • G = (V,E). The cut set of a cut is the set of edges {(u, v) ∈ E | u ∈ S, v ∈ T } and S and T are in two partitions

  • Size of the cut: # of edges in the cut set
  • Min-cut (e.g., C1) is not a good partition
  • A better measure: Sparsity:

φ=the size of the cut / min{|S|,|T|}

  • A cut is sparsest if its sparsity is not greater than that of any other cut
  • Ex. Cut C2 = ({a, b, c, d, e, f, l}, {g, h, i, j, k}) is the sparsest cut
  • For k clusters, the modularity o a clustering assesses the quality of the clustering: 


li: # edges between vertices in the i-th cluster

di: the sum of the degrees of the vertices in the i-th cluster

  • The modularity of a clustering of a graph is the difference between the fraction of all edges that fall into individual clusters and the fraction that would do so if the graph vertices were randomly connected
  • The optimal clustering of graphs maximizes the modularity

Speaker notes:

Content Tools


There are currently no sources for this slide.