Matrix: Newman/netscience

Description: co-authoship of scientists in network theory & experiments

Newman/netscience graph
(undirected graph drawing)


Newman/netscience
scc of Newman/netscience

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  • Matrix group: Newman
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  • download as a MATLAB mat-file, file size: 24 KB. Use UFget(2401) or UFget('Newman/netscience') in MATLAB.
  • download in Matrix Market format, file size: 20 KB.
  • download in Rutherford/Boeing format, file size: 19 KB.

    Matrix properties
    number of rows1,589
    number of columns1,589
    nonzeros5,484
    # strongly connected comp.396
    explicit zero entries0
    nonzero pattern symmetrysymmetric
    numeric value symmetrysymmetric
    typereal
    structuresymmetric
    Cholesky candidate?no
    positive definite?no

    authorM. Newman
    editorM. Newman
    date2006
    kindundirected weighted graph
    2D/3D problem?no

    Additional fieldssize and type
    nodenamefull 1589-by-19

    Notes:

    Network collection from M. Newman                                          
    http://www-personal.umich.edu/~mejn/netdata/                               
                                                                               
    The graph netscience contains a coauthorship network of scientists         
    working on network theory and experiment, as compiled by M. Newman in May  
    2006.  The network was compiled from the bibliographies of two review      
    articles on networks, M. E. J. Newman, SIAM Review 45, 167-256 (2003) and  
    S. Boccaletti et al., Physics Reports 424, 175-308 (2006), with a few      
    additional references added by hand.  The version given here contains all  
    components of the network, for a total of 1589 scientists, and not just the
    largest component of 379 scientists previously published.  The network is  
    weighted, with weights assigned as described in M. E. J. Newman,           
    Phys. Rev. E 64, 016132 (2001).                                            
                                                                               
    If you make use of these data, please cite M. E. J. Newman, Finding        
    community structure in networks using the eigenvectors of matrices,        
    Preprint physics/0605087 (2006).                                           
    

    SVD-based statistics:
    norm(A)9.72857
    min(svd(A))0
    cond(A)Inf
    rank(A)1,416
    null space dimension173
    full numerical rank?no
    singular value gap2.45288e+08

    singular values (MAT file):click here
    SVD method used:s = svd (full (A)) ;
    status:ok

    Newman/netscience svd

    For a description of the statistics displayed above, click here.

    Maintained by Tim Davis, last updated 12-Mar-2014.
    Matrix pictures by cspy, a MATLAB function in the CSparse package.
    Matrix graphs by Yifan Hu, AT&T Labs Visualization Group.