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-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.                 
                                                                               
Files                                                                          
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
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.