%-------------------------------------------------------------------------------
% UF Sparse Matrix Collection, Tim Davis
% http://www.cise.ufl.edu/research/sparse/matrices/SNAP/Oregon-1
% name: SNAP/Oregon-1
% [(9 graphs) AS peering info inferred from Oregon route-views, 3/31-5/26/01]
% id: 2323
% date: 2001
% author: J. Leskovec, J. Kleinberg and C. Faloutsos
% ed: J. Leskovec
% fields: name title A id date author ed kind notes aux
% aux: G Gname nodename
% kind: undirected graph sequence
%-------------------------------------------------------------------------------
% notes:
% 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.                           
%-------------------------------------------------------------------------------
