%-------------------------------------------------------------------------------
% SuiteSparse Matrix Collection, Tim Davis
% https://sparse.tamu.edu/SNAP/sx-superuser
% name: SNAP/sx-superuser
% [SNAP network: SuperUser temporal network]
% id: 2795
% date: 2017
% author: A. Paranjape, A. R. Benson, and J. Leskovec
% ed: J. Leskovec
% fields: name title A id date author ed kind notes aux
% aux: temporal_edges temporal_edges_a2q temporal_edges_c2a temporal_edges_c2q nodeid A2Q C2A C2Q
% kind: directed temporal multigraph
%-------------------------------------------------------------------------------
% notes:
% SNAP (Stanford Network Analysis Platform) Large Network Dataset Collection,
% Jure Leskovec and Anrej Krevl, http://snap.stanford.edu/data, June 2014.   
% email: jure at cs.stanford.edu                                             
%                                                                            
% Super User temporal network                                                
%                                                                            
% https://snap.stanford.edu/data/sx-superuser.html                           
%                                                                            
% Dataset information                                                        
%                                                                            
% This is a temporal network of interactions on the stack exchange web site  
% Super User (http://superuser.com/). There are three different types of     
% interactions represented by a directed edge (u, v, t):                     
%                                                                            
% user u answered user v's question at time t (in the graph sx-superuser-a2q)
% user u commented on user v's question at time t (in the graph              
% sx-superuser-c2q) user u commented on user v's answer at time t (in the    
% graph sx-superuser-c2a)                                                    
%                                                                            
% The graph sx-superuser contains the union of these graphs. These graphs    
% were constructed from the Stack Exchange Data Dump. Node ID numbers        
% correspond to the 'OwnerUserId' tag in that data dump.                     
%                                                                            
% Dataset statistics (sx-superuser)                                          
% Nodes   194,085                                                            
% Temporal Edges  1,443,339                                                  
% Edges in static graph   924,886                                            
% Time span   2773 days                                                      
%                                                                            
% Dataset statistics (sx-superuser-a2q)                                      
% Nodes   167,981                                                            
% Temporal Edges  430,033                                                    
% Edges in static graph   403,578                                            
% Time span   2773 days                                                      
%                                                                            
% Dataset statistics (sx-mathoverflow-c2q)                                   
% Nodes   94,548                                                             
% Temporal Edges  479,067                                                    
% Edges in static graph   291,030                                            
% Time span   2769 days                                                      
%                                                                            
% Dataset statistics (sx-superuser-c2a)                                      
% Nodes   101,052                                                            
% Temporal Edges  534,239                                                    
% Edges in static graph   289,487                                            
% Time span   2735 days                                                      
%                                                                            
% Source (citation)                                                          
% Ashwin Paranjape, Austin R. Benson, and Jure Leskovec. "Motifs in Temporal 
% Networks." In Proceedings of the Tenth ACM International Conference on Web 
% Search and Data Mining, 2017.                                              
%                                                                            
% Files                                                                      
% File    Description                                                        
% sx-superuser.txt.gz All interactions                                       
% sx-superuser-a2q.txt.gz Answers to questions                               
% sx-superuser-c2q.txt.gz Comments to questions                              
% sx-superuser-c2a.txt.gz Comments to answers                                
%                                                                            
% Data format                                                                
%                                                                            
%     SRC DST UNIXTS                                                         
%                                                                            
%     where edges are separated by a new line and                            
%                                                                            
%     SRC: id of the source node (a user)                                    
%     TGT: id of the target node (a user)                                    
%     UNIXTS: Unix timestamp (seconds since the epoch)                       
%                                                                            
% ---------------------------------------------------------------------------
% Notes on inclusion into the SuiteSparse Matrix Collection, July 2018:      
% ---------------------------------------------------------------------------
%                                                                            
% The SNAP graph is 1-based, with nodes in all graphs numbered 1 to          
% n=567,315.                                                                 
%                                                                            
% In the SuiteSparse Matrix Collection, the primary matrix, Problem.A, is    
% the overall static graph, with 924,886 edges, of size n-by-n with          
% n=194,085.  These edges represent the 1,443,339 temporal edges.  A(i,j) is 
% the number of times person u=nodeid(i) interacted with person v=nodeid(j), 
% with a temporal edge (u,v,t), with any kind of interaction.                
% Problem.aux.nodeid is a list of the node id's that appear in the SNAP data 
% set.                                                                       
%                                                                            
% A2Q = Problem.aux.Q2A is the static sx-superuser-a2q graph.                
% C2Q = Problem.aux.C2Q is the static sx-superuser-c2q graph.                
% C2A = Problem.aux.C2A is the static sx-superuser-c2a graph.                
% These sum together to give the the overall graph.  That is,                
% A = A2Q + C2Q + C2A.                                                       
%                                                                            
% A2Q(u,v) is the number of times person u answered v's questions.           
% C2Q(u,v) is the number of times person u commented on v's question.        
% C2A(u,v) is the number of times person u commented on v's answer.          
%                                                                            
% The temporal edges are held in:                                            
% Problem.aux.temporal_edges:     [1,443,339 x 3]                            
% Problem.aux.temporal_edges_a2q: [  430,033 x 3]                            
% Problem.aux.temporal_edges_c2q: [  479,067 x 3]                            
% Problem.aux.temporal_edges_c2a: [  534,239 x 3]                            
%                                                                            
% Each row in these matrices is a single temporal edge, (u,v,t).  Summing up 
% all entries in A gives 1,443,339 and likewise the sum of entries in the    
% other graphs gives the number of temporal edges they represent.            
%-------------------------------------------------------------------------------
