Matrix: SNAP/Oregon-1

Description: (9 graphs) AS peering info inferred from Oregon route-views, 3/31-5/26/01

SNAP/Oregon-1 graph
(undirected graph drawing)


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  • download as a MATLAB mat-file, file size: 973 KB. Use UFget(2323) or UFget('SNAP/Oregon-1') in MATLAB.
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  • download in Rutherford/Boeing format, file size: 633 KB.

    Matrix properties
    number of rows11,492
    number of columns11,492
    # strongly connected comp.319
    explicit zero entries0
    nonzero pattern symmetrysymmetric
    numeric value symmetrysymmetric
    Cholesky candidate?no
    positive definite?no

    authorJ. Leskovec, J. Kleinberg and C. Faloutsos
    editorJ. Leskovec
    kindundirected graph sequence
    2D/3D problem?no

    Additional fieldssize and type
    Gcell 9-by-1
    Gnamefull 9-by-14
    nodenamefull 11492-by-1


    Networks from SNAP (Stanford Network Analysis Platform) Network Data Sets,     
    Jure Leskovec                         
    email jure at                                                  
    Autonomous systems - Oregon-1                                                  
    Dataset information                                                            
    9 graphs of Autonomous Systems (AS) peering information inferred from Oregon   
    route-views between March 31 2001 and May 26 2001.                             
    Dataset statistics are calculated for the graph with the lowest (March 31 2001)
    and highest (from May 26 2001) number of nodes: Dataset statistics for graph   
    witdh lowest number of nodes - 3 31 2001)                                      
    Nodes   10670                                                                  
    Edges   22002                                                                  
    Nodes in largest WCC    10670 (1.000)                                          
    Edges in largest WCC    22002 (1.000)                                          
    Nodes in largest SCC    10670 (1.000)                                          
    Edges in largest SCC    22002 (1.000)                                          
    Average clustering coefficient  0.4559                                         
    Number of triangles     17144                                                  
    Fraction of closed triangles    0.009306                                       
    Diameter (longest shortest path)    9                                          
    90-percentile effective diameter    4.5                                        
    Dataset statistics for graph with highest number of nodes - 5 26 2001          
    Nodes   11174                                                                  
    Edges   23409                                                                  
    Nodes in largest WCC    11174 (1.000)                                          
    Edges in largest WCC    23409 (1.000)                                          
    Nodes in largest SCC    11174 (1.000)                                          
    Edges in largest SCC    23409 (1.000)                                          
    Average clustering coefficient  0.4532                                         
    Number of triangles     19894                                                  
    Fraction of closed triangles    0.009636                                       
    Diameter (longest shortest path)    10                                         
    90-percentile effective diameter    4.4                                        
    Source (citation)                                                              
    J. Leskovec, J. Kleinberg and C. Faloutsos. Graphs over Time: Densification    
    Laws, Shrinking Diameters and Possible Explanations. ACM SIGKDD International  
    Conference on Knowledge Discovery and Data Mining (KDD), 2005.                 
    File    Description                                                            
    *        AS peering information inferred from Oregon route-views ...           
    oregon1_010331.txt.gz   from March 31 2001                                     
    oregon1_010407.txt.gz   from April 7 2001                                      
    oregon1_010414.txt.gz   from April 14 2001                                     
    oregon1_010421.txt.gz   from April 21 2001                                     
    oregon1_010428.txt.gz   from April 28 2001                                     
    oregon1_010505.txt.gz   from May 05 2001                                       
    oregon1_010512.txt.gz   from May 12 2001                                       
    oregon1_010519.txt.gz   from May 19 2001                                       
    oregon1_010526.txt.gz   from May 26 2001                                       
    NOTE: for the UF Sparse Matrix Collection, the primary matrix in this problem  
    set (Problem.A) is the last matrix in the sequence, oregon1_010526, from May 26
    The nodes are uniform across all graphs in the sequence in the UF collection.  
    That is, nodes do not come and go.  A node that is "gone" simply has no edges. 
    This is to allow comparisons across each node in the graphs.                   
    Problem.aux.nodenames gives the node numbers of the original problem.  So      
    row/column i in the matrix is always node number Problem.aux.nodenames(i) in   
    all the graphs.                                                                
    Problem.aux.G{k} is the kth graph in the sequence.                             
    Problem.aux.Gname(k,:) is the name of the kth graph.                           

    SVD-based statistics:
    null space dimension8,171
    full numerical rank?no
    singular value gap7.10717e+11

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

    SNAP/Oregon-1 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.