Networks from SNAP (Stanford Network Analysis Platform) Network Data Sets,     
Jure Leskovec http://snap.stanford.edu/data/index.html                         
email jure at cs.stanford.edu                                                  
                                                                               
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.                 
                                                                               
Files                                                                          
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
2001.                                                                          
                                                                               
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.