%-------------------------------------------------------------------------------
% UF Sparse Matrix Collection, Tim Davis
% http://www.cise.ufl.edu/research/sparse/matrices/DIMACS10/venturiLevel3
% name: DIMACS10/venturiLevel3
% [DIMACS10 set: numerical/venturiLevel3]
% id: 2498
% date: 2011
% author: H. Antz et al.
% ed: H. Meyerhenke
% 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.                                        
%                                                                          
%                                                                          
% numerical: graphs from numerical simulations                             
%                                                                          
% For the graphs adaptive and venturiLevel3, please refer to the preprint: 
%                                                                          
% Hartwig Anzt, Werner Augustin, Martin Baumann, Hendryk Bockelmann,       
% Thomas Gengenbach, Tobias Hahn, Vincent Heuveline, Eva Ketelaer,         
% Dimitar Lukarski, Andrea Otzen, Sebastian Ritterbusch, Bjo"rn Rocker,    
% Staffan Ronnås, Michael Schick, Chandramowli Subramanian, Jan-Philipp    
% Weiss, and Florian Wilhelm.  Hiflow3 - a flexible and hardware-aware     
% parallel Finite element package. In Parallel/High-Performance Object-    
% Oriented Scientific Computing (POOSC'10).                                
%                                                                          
% For the graphs channel-500x100x100-b050 and packing-500x100x100-b050,    
% please refer to:                                                         
%                                                                          
% Markus Wittmann, Thomas Zeiser. Technical Note: Data Structures of       
% ILBDC Lattice Boltzmann Solver.                                          
% http://www.cc.gatech.edu/dimacs10/archive/numerical-overview-Erlangen.pdf
%-------------------------------------------------------------------------------
