TR2018-070

On spectral partitioning of signed graphs


    •  Knyazev, A., "On spectral partitioning of signed graphs", SIAM Workshop on Combinatorial Scientific Computing, July 11, 2018.
      BibTeX TR2018-070 PDF
      • @inproceedings{Knyazev2018jul,
      • author = {Knyazev, Andrew},
      • title = {On spectral partitioning of signed graphs},
      • booktitle = {SIAM Workshop on Combinatorial Scientific Computing},
      • year = 2018,
      • month = jul,
      • url = {https://www.merl.com/publications/TR2018-070}
      • }
  • Research Area:

    Signal Processing

Abstract:

We argue that the standard graph Laplacian is preferable for spectral partitioning of signed graphs compared to the signed Laplacian. Simple examples demonstrate that partitioning based on signs of components of the leading eigenvectors of the signed Laplacian may be meaningless, in contrast to partitioning based on the Fiedler vector of the standard graph Laplacian for signed graphs. We observe that negative eigenvalues are beneficial for spectral partitioning of signed graphs, making the Fiedler vector easier to compute.