Matrix: SNAP/Oregon-2

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

SNAP/Oregon-2 graph
(undirected graph drawing)


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  • Matrix group: SNAP
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  • download as a MATLAB mat-file, file size: 1 MB. Use UFget(2324) or UFget('SNAP/Oregon-2') in MATLAB.
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  • download in Rutherford/Boeing format, file size: 799 KB.

    Matrix properties
    number of rows11,806
    number of columns11,806
    # strongly connected comp.346
    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 11806-by-1


    Networks from SNAP (Stanford Network Analysis Platform) Network Data Sets,     
    Jure Leskovec                         
    email jure at                                                  
    Autonomous systems - Oregon-2                                                  
    Dataset information                                                            
    9 Autonomous systems graphs, 1 per week between March 31 2001 and May 26 2001. 
    Graphs represent AS peering information inferred from Oregon route-views,      
    Looking glass data, and Routing registry, all combined.                        
    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 with lowest number of nodes - 3 31 2001           
    Nodes   10900                                                                  
    Edges   31180                                                                  
    Nodes in largest WCC    10900 (1.000)                                          
    Edges in largest WCC    31180 (1.000)                                          
    Nodes in largest SCC    10900 (1.000)                                          
    Edges in largest SCC    31180 (1.000)                                          
    Average clustering coefficient  0.5009                                         
    Number of triangles     82856                                                  
    Fraction of closed triangles    0.03855                                        
    Diameter (longest shortest path)    9                                          
    90-percentile effective diameter    4.3                                        
    Dataset statistics for graph with highest number of nodes - 5 26 2001          
    Nodes   11461                                                                  
    Edges   32730                                                                  
    Nodes in largest WCC    11461 (1.000)                                          
    Edges in largest WCC    32730 (1.000)                                          
    Nodes in largest SCC    11461 (1.000)                                          
    Edges in largest SCC    32730 (1.000)                                          
    Average clustering coefficient  0.4943                                         
    Number of triangles     89541                                                  
    Fraction of closed triangles    0.03701                                        
    Diameter (longest shortest path)    9                                          
    90-percentile effective diameter    4.3                                        
    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, Looking glass 
            data, and Routing registry,  ...                                       
    oregon2_010331.txt.gz from March 31 2001                                       
    oregon2_010407.txt.gz from April 7 2001                                        
    oregon2_010414.txt.gz from April 14 2001                                       
    oregon2_010421.txt.gz from April 21 2001                                       
    oregon2_010428.txt.gz from April 28 2001                                       
    oregon2_010505.txt.gz from May 05 2001                                         
    oregon2_010512.txt.gz from May 12 2001                                         
    oregon2_010519.txt.gz from May 19 2001                                         
    oregon2_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, oregon2_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 dimension7,981
    full numerical rank?no
    singular value gap9.24486e+10

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

    SNAP/Oregon-2 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.