machine learning graph: cnae9_10NN
Name cnae9_10NN
Group ML_Graph
Matrix ID 2859
Num Rows 1,080
Num Cols 1,080
Nonzeros 18,278
Pattern Entries 18,278
Kind Undirected Weighted Graph
Symmetric Yes
Date 2020
Author D. Pasadakis, C.L. Alappat, O. Schenk, G. Wellein
Editor O. Schenk
Structural Rank
Structural Rank Full
Num Dmperm Blocks
Strongly Connect Components -1
Num Explicit Zeros 0
Pattern Symmetry 100%
Numeric Symmetry 100%
Cholesky Candidate no
Positive Definite no
Type real
Download MATLAB Rutherford Boeing Matrix Market
ML_Graph: adjacency matrices from machine learning datasets, Olaf      
Schenk.  D.  Pasadakis,  C.  L.  Alappat,  O.  Schenk,  and  G.        
Wellein, "K-way p-spectral clustering on Grassmann manifolds," 2020.                                       
For $n$ data points, the connectivity matrix $G \in \mathbb{R}^{n\times
n}$ is created from a k nearest neighbors routine, with k set such that
the resulting graph is connected. The similarity matrix $S \in         
\mathbb{R}^{n\times n}$ between the data points is defined as          
    s_{ij} = \max\{s_i(j), s_j(i)\} \;\; \text{with}\;                 
    s_i(j) = \exp (-4 \frac{\|x_i - x_j \|^2}{\sigma_i^2} )            
with $\sigma_i$ standing for the Euclidean distance between the $i$th  
data point and its nearest k-nearest neighbor. The adjacency matrix $W$
is then created as $W = G \odot S$.                                    
Besides the adjacency matrices $W$, the node labels for each graph are 
part of the submission.  If the graph has c classes, the node labels   
are integers in the range 0 to c-1.                                    
Graph: cnae9_10NN Classes: 9