# Using Sparse Matrix

AI and Robotics

Nov 25, 2013 (4 years and 5 months ago)

85 views

Using Sparse Matrix
Reordering Algorithms for
Cluster Identification

Chris Mueller

Dec 9, 2004

Visualizing a Graph as a Matrix

Each row and column in the matrix corresponds to
a node in the graph. The nodes are ordered the
same in the rows and columns, so node 10 is
represented by
row=10

and
col=10.

Each

edge

between

two

nodes

(a,b)

is

rendered

as

a

dot

at

(i,j)

where

i

is

the

row

for

a

and

j

is

the

column

for

b
.

The

solid

diagonal

shows

the

identity

relationship

for

each

node
.

Undirected graphs can be rendered as
lower triangles, with each edge is
displayed so that
i <= j.

Visually Identifying Clusters

Reordering the nodes (rows/cols)
can reduce the noise in the display
and highlight clusters.

Dense areas in the matrix reveal potential
clusters.

Some dense areas may be in the same
row or column as others, suggesting a
relationship.

(Some) Previous Work

The basic idea of visualizing relational data as a reordered matrix has been
around since the early days of computer science. Some examples are:

Bertin (1981),
Graphics and Graphic Information Processing
. From http://www.math.yorku.ca/SCS/Gallery/bright
-
ideas.html

The Reorderable Matrix (Bertin, 1981)

Block Clustering (Hartigan, 1972)

GAP Generalized Association Plots (Chen, 2002)

www.stat.sinica.edu.tw/SLR/PDF/

-
Cluster_Lecture_040206
-
new.pdf

Sparse Matrices

Sparse matrices can be stored in memory
in data structures that are more compact
that 2D arrays:

The banded representation stores only the
diagonals that have values:

Matrices are the basic data structure for
most numerical computations:

0 1 2 3

9 3 0 3

4 8 0 1

3 5 8 0

1

9 3 0

4 0 1

8 0

Sparse matrices

are matrices that do not
need explicit values for each element:

Note that zeros may be
important and cannot always
be excluded from that matrix.

1

3 9 0

4 0 1

8 0

1

9 3 0

4 0 1

8 0

[

1 0 1 n 3 0 0 9 n 8 4 n
]

The
bandwidth

is the number
of diagonals required to store
the matrix. In this example,
the bandwidth is 4.

Sparse matrix reordering algorithms reorder
the elements in the matrix to achieve better
use of memory or computational resources:

[
n 0 1 1 9 0 0 3 4 8
]

Swapping column 1 and 2
reduced the bandwidth to 3,
decreased the amount of
storage required by 2
elements, and removed 2
empty elements.

Sparse Matrix Reordering Algorithms

Bandwidth Minimization: Reverse Cuthill
-
McKee and King’s Algorithm

RCM(matrix):

Represent the matrix as a graph

Choose a suitable starting node

For each node reachable from the current node:

Output the node

Find all unvisited neighbors

Order them based on increasing degree

Visit them in that order

1

2

3

4

5

6

7

Minimizing Non
-
Zero Structure: Modified Minimum Degree

MMD(matrix):

Represent the matrix as a graph

Order nodes based on degree

1

2

3

4

5

7

6

8

9

Note that these algorithms are stochastic in the choice of starting nodes and ordering for nodes
with the same degree.

King’s algorithm is similar but it orders based on edges out of the current cluster rather than total edges.

Reordering the COG Database

Basic Protocol:

1.
Filter edges based on FASTA score

1.
cmp2 is original data, cmp90, cmp200 are filtered

2.
Shuffle the data

3.
For each sorted and shuffled graph

1.
Identify the connected components
?

2.
Apply RCM and King’s algorithm to each component

3.
Apply MMD to the entire graph

Results by the Numbers

(but the pictures show sooo much more…)

Visualization Key

Red dots are edges

Both axes have the nodes in the same order

Blue dots are the
COG families for the
node in column j.

Green lines show the
extent of a COG family.

Black dots show the
elements in the family.

Discussion

All algorithms worked as expected

However, the matrix ordering goals were too simple
to yield good cluster clusters.

Possible Future Work