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.