%-------------------------------------------------------------------------------
% UF Sparse Matrix Collection, Tim Davis
% http://www.cise.ufl.edu/research/sparse/matrices/DIMACS10/uk
% name: DIMACS10/uk
% [DIMACS10 set: walshaw/uk]
% id: 2531
% date: 2000
% author: C. Walshaw
% ed: C. Walshaw
% fields: name title A id date author ed kind notes
% kind: undirected graph
%-------------------------------------------------------------------------------
% notes:
% 10th DIMACS Implementation Challenge:                                   
%                                                                         
% http://www.cc.gatech.edu/dimacs10/index.shtml                           
%                                                                         
% As stated on their main website (                                       
% http://dimacs.rutgers.edu/Challenges/ ), the "DIMACS Implementation     
% Challenges address questions of determining realistic algorithm         
% performance where worst case analysis is overly pessimistic and         
% probabilistic models are too unrealistic: experimentation can provide   
% guides to realistic algorithm performance where analysis fails."        
%                                                                         
% For the 10th DIMACS Implementation Challenge, the two related           
% problems of graph partitioning and graph clustering were chosen.        
% Graph partitioning and graph clustering are among the aforementioned    
% questions or problem areas where theoretical and practical results      
% deviate significantly from each other, so that experimental outcomes    
% are of particular interest.                                             
%                                                                         
% Problem Motivation                                                      
%                                                                         
% Graph partitioning and graph clustering are ubiquitous subtasks in      
% many application areas. Generally speaking, both techniques aim at      
% the identification of vertex subsets with many internal and few         
% external edges. To name only a few, problems addressed by graph         
% partitioning and graph clustering algorithms are:                       
%                                                                         
%     * What are the communities within an (online) social network?       
%     * How do I speed up a numerical simulation by mapping it            
%         efficiently onto a parallel computer?                           
%     * How must components be organized on a computer chip such that     
%         they can communicate efficiently with each other?               
%     * What are the segments of a digital image?                         
%     * Which functions are certain genes (most likely) responsible       
%         for?                                                            
%                                                                         
% Challenge Goals                                                         
%                                                                         
%     * One goal of this Challenge is to create a reproducible picture    
%         of the state-of-the-art in the area of graph partitioning       
%         (GP) and graph clustering (GC) algorithms. To this end we       
%         are identifying a standard set of benchmark instances and       
%         generators.                                                     
%                                                                         
%     * Moreover, after initiating a discussion with the community, we    
%         would like to establish the most appropriate problem            
%         formulations and objective functions for a variety of           
%         applications.                                                   
%                                                                         
%     * Another goal is to enable current researchers to compare their    
%         codes with each other, in hopes of identifying the most         
%         effective algorithmic innovations that have been proposed.      
%                                                                         
%     * The final goal is to publish proceedings containing results       
%         presented at the Challenge workshop, and a book containing      
%         the best of the proceedings papers.                             
%                                                                         
% Problems Addressed                                                      
%                                                                         
% The precise problem formulations need to be established in the course   
% of the Challenge. The descriptions below serve as a starting point.     
%                                                                         
%     * Graph partitioning:                                               
%                                                                         
%       The most common formulation of the graph partitioning problem     
%       for an undirected graph G = (V,E) asks for a division of V into   
%       k pairwise disjoint subsets (partitions) such that all            
%       partitions are of approximately equal size and the edge-cut,      
%       i.e., the total number of edges having their incident nodes in    
%       different subdomains, is minimized. The problem is known to be    
%       NP-hard.                                                          
%                                                                         
%     * Graph clustering:                                                 
%                                                                         
%       Clustering is an important tool for investigating the             
%       structural properties of data. Generally speaking, clustering     
%       refers to the grouping of objects such that objects in the same   
%       cluster are more similar to each other than to objects of         
%       different clusters. The similarity measure depends on the         
%       underlying application. Clustering graphs usually refers to the   
%       identification of vertex subsets (clusters) that have             
%       significantly more internal edges (to vertices of the same        
%       cluster) than external ones (to vertices of another cluster).     
%                                                                         
% There are 10 data sets in the DIMACS10 collection:                      
%                                                                         
% Kronecker:  synthetic graphs from the Graph500 benchmark                
% dyn-frames: frames from a 2D dynamic simulation                         
% Delaunay:   Delaunay triangulations of random points in the plane       
% coauthor:   citation and co-author networks                             
% streets:    real-world street networks                                  
% Walshaw:    Chris Walshaw's graph partitioning archive                  
% matrix:     graphs from the UF collection (not added here)              
% random:     random geometric graphs (random points in the unit square)  
% clustering: real-world graphs commonly used as benchmarks               
% numerical:  graphs from numerical simulation                            
%                                                                         
% Some of the graphs already exist in the UF Collection.  In some cases,  
% the original graph is unsymmetric, with values, whereas the DIMACS      
% graph is the symmetrized pattern of A+A'.  Rather than add duplicate    
% patterns to the UF Collection, a MATLAB script is provided at           
% http://www.cise.ufl.edu/research/sparse/dimacs10 which downloads        
% each matrix from the UF Collection via UFget, and then performs whatever
% operation is required to convert the matrix to the DIMACS graph problem.
% Also posted at that page is a MATLAB code (metis_graph) for reading the 
% DIMACS *.graph files into MATLAB.                                       
%                                                                         
%                                                                         
% Walshaw:  Chris Walshaw's graph partitioning archive                    
%                                                                         
% Chris Walshaw's graph partitioning archive contains 34 graphs that      
% have been very popular as benchmarks for graph partitioning algorithms  
% ( http://staffweb.cms.gre.ac.uk/~wc06/partition/ ).                     
%                                                                         
% 17 of them are already in the UF Collection.  Only the 17 new graphs    
% not yet in the collection are added here in the DIMACS10 set.           
%                                                                         
% DIMACS10 graph:                 new?   UF matrix:                       
% ---------------                 ----   -------------                    
% walshaw/144                      *     DIMACS10/144                     
% walshaw/3elt                           AG-Monien/3elt                   
% walshaw/4elt                           Pothen/barth5                    
% walshaw/598a                     *     DIMACS10/598a                    
% walshaw/add20                          Hamm/add20                       
% walshaw/add32                          Hamm/add32                       
% walshaw/auto                     *     DIMACS10/auto                    
% walshaw/bcsstk29                       HB/bcsstk29                      
% walshaw/bcsstk30                       HB/bcsstk30                      
% walshaw/bcsstk31                       HB/bcsstk31                      
% walshaw/bcsstk32                       HB/bcsstk32                      
% walshaw/bcsstk33                       HB/bcsstk33                      
% walshaw/brack2                         AG-Monien/brack2                 
% walshaw/crack                          AG-Monient/crack                 
% walshaw/cs4                      *     DIMACS10/cs4                     
% walshaw/cti                      *     DIMACS10/cti                     
% walshaw/data                     *     DIMACS10/data                    
% walshaw/fe_4elt2                 *     DIMACS10/fe_4elt2                
% walshaw/fe_body                  *     DIMACS10/fe_body                 
% walshaw/fe_ocean                 *     DIMACS10/fe_ocean                
% walshaw/fe_pwt                         Pothen/pwt                       
% walshaw/fe_rotor                 *     DIMACS10/fe_rotor                
% walshaw/fe_sphere                *     DIMACS10/fe_sphere               
% walshaw/fe_tooth                 *     DIMACS10/fe_tooth                
% walshaw/finan512                       Mulvey/finan512                  
% walshaw/m14b                     *     DIMACS10/m14b                    
% walshaw/memplus                        Hamm/memplus                     
% walshaw/t60k                     *     DIMACS10/t60k                    
% walshaw/uk                       *     DIMACS10/uk                      
% walshaw/vibrobox                       Cote/vibrobox                    
% walshaw/wave                           AG-Monien/wave                   
% walshaw/whitaker3                      AG-Monien/whitaker3              
% walshaw/wing                     *     DIMACS10/wing                    
% walshaw/wing_nodal               *     DIMACS10/wing_nodal              
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