ML_Graph/usps_norm_5NN
machine learning graph: usps_norm_5NN
Name 
usps_norm_5NN 
Group 
ML_Graph 
Matrix ID 
2887 
Num Rows

11,000 
Num Cols

11,000 
Nonzeros

81,112 
Pattern Entries

81,112 
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

Notes 
ML_Graph: adjacency matrices from machine learning datasets, Olaf
Schenk. D. Pasadakis, C. L. Alappat, O. Schenk, and G.
Wellein, "Kway pspectral clustering on Grassmann manifolds," 2020.
https://arxiv.org/abs/2008.13210
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
\begin{equation}
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} )
\end{equation}
with $\sigma_i$ standing for the Euclidean distance between the $i$th
data point and its nearest knearest 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 c1.
Graph: usps_norm_5NN Classes: 10
