@techreport{3534,
  title = {A Combinatorial View of Graph Laplacians},
  abstract = {Discussions about different graph Laplacian, mainly normalized and
  unnormalized versions of graph Laplacian, have been ardent with
  respect to various methods in clustering and graph based
  semi-supervised learning. Previous research on graph Laplacians
  investigated their convergence properties to Laplacian operators
  on continuous manifolds. There is still no strong proof on
  convergence for the normalized Laplacian. In this paper, we
  analyze different variants of graph Laplacians directly from the
  ways solving the original graph partitioning problem. The graph
  partitioning problem is a well-known combinatorial NP hard
  optimization problem. The spectral solutions provide evidence that
  normalized Laplacian encodes more reasonable considerations for
  graph partitioning. We also provide some examples to show their
  differences.},
  number = {144},
  organization = {Max-Planck-Gesellschaft},
  institution = {Max Planck Institute for Biological Cybernetics, T{\"u}bingen, Germany},
  school = {Biologische Kybernetik},
  month = aug,
  year = {2005},
  author = {Huang, J.},
  month_numeric = {8}
}