%-------------------------------------------------------------------------------
% UF Sparse Matrix Collection, Tim Davis
% http://www.cise.ufl.edu/research/sparse/matrices/SNAP/as-735
% name: SNAP/as-735
% [(735 graphs) daily instances(graphs) from 11/8/97-1/2/00]
% id: 2320
% date: 2000
% author: D. Meyer
% 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 AS-735                                                     
%                                                                               
% Dataset information                                                           
%                                                                               
% The graph of routers comprising the Internet can be organized into sub-graphs 
% called Autonomous Systems (AS). Each AS exchanges traffic flows with some     
% neighbors (peers). We can construct a communication network of who-talks-to-  
% whom from the BGP (Border Gateway Protocol) logs.                             
%                                                                               
% The data was collected from University of Oregon Route Views Project          
% (http://www.routeviews.org/) - Online data and reports. The dataset contains  
% 735 daily instances which span an interval of 785 days from November 8 1997 to
% January 2 2000. In contrast to citation networks, where nodes and edges only  
% get added (not deleted) over time, the AS dataset also exhibits both the      
% addition and deletion of the nodes and edges over time.                       
%                                                                               
% Dataset statistics are calculated for the graph with the highest number of    
% nodes and edges (dataset from January 02 2000):                               
%                                                                               
% Dataset statistics                                                            
% Nodes   6474                                                                  
% Edges   13233                                                                 
% Nodes in largest WCC    6474 (1.000)                                          
% Edges in largest WCC    13233 (1.000)                                         
% Nodes in largest SCC    6474 (1.000)                                          
% Edges in largest SCC    13233 (1.000)                                         
% Average clustering coefficient  0.3913                                        
% Number of triangles     6584                                                  
% Fraction of closed triangles    0.009591                                      
% Diameter (longest shortest path)    9                                         
% 90-percentile effective diameter    4.6                                       
%                                                                               
% 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                                                           
% as20000102.txt.gz   Autonomous Systems graph from January 02 2000             
% as.tar.gz   735 Autonomous Systems graphs from November 8 1997 to             
%              January 02 2000                                                  
%                                                                               
% NOTE:  In the UF collection, the primary matrix (Problem.A) is the            
% as20000102 matrix from January 02 2000 (the last graph in the sequence).      
%                                                                               
% 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.                          
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