Matrix properties
number of rows	27,240
number of columns	27,240
nonzeros	342,437
# strongly connected comp.	19,565
explicit zero entries	0
nonzero pattern symmetry	0%
numeric value symmetry	0%
type	binary
structure	unsymmetric
Cholesky candidate?	no
positive definite?	no

author	KDD Cup 2003
editor	V. Batagelj
date	2003
kind	directed graph
2D/3D problem?	no

Additional fields	size and type
nodename	full 27240-by-7

Notes:

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Pajek network converted to sparse adjacency matrix for inclusion in UF sparse 
matrix collection, Tim Davis.  For Pajek datasets, See V. Batagelj & A. Mrvar,
http://vlado.fmf.uni-lj.si/pub/networks/data/.                                
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 High Energy Particle Physics (HEP) literature                                
 ---------------------------------------------                                
 Citation data from KDD Cup 2003, a knowledge discovery and data mining       
 competition held in conjunction with the Ninth Annual ACM SIGKDD Conference. 
   http://www.cs.cornell.edu/projects/kddcup/index.html                       
 The Stanford Linear Accelerator Center SPIRES-HEP database has been          
 comprehensively cataloguing the High Energy Particle Physics (HEP) literature
 online since 1974, and indexes more than 500,000 high-energy physics related 
 articles including their full citation tree.                                 
 The network contains a citation graph of the hep-th portion of the arXiv.    
 The units names are the arXiv IDs of papers; the relation is  X cites Y .    
 Note that revised papers may have updated citations. As such, citations may  
 refer to future papers, i.e. a paper may cite another paper that was publishe
 after the first paper.                                                       
 Update May 12, 2003 is not included.                                         
                                                                              
 transformed in Pajek format: V. Batagelj, 26. July 2003                      
 -----                                                                        

SVD-based statistics:
norm(A)	84.3235
min(svd(A))	5.59609e-66
cond(A)	1.50683e+67
rank(A)	21,162
null space dimension	6,078
full numerical rank?	no
singular value gap	1.3948e+08

singular values (MAT file):	click here
SVD method used:	s = svd (full (A))
status:	ok

For a description of the statistics displayed above, click here.

Maintained by Tim Davis, last updated 12-Mar-2014.
Matrix pictures by cspy, a MATLAB function in the CSparse package.
Matrix graphs by Yifan Hu, AT&T Labs Visualization Group.