# simplify

Reduce multigraph to simple graph

## Syntax

## Description

specifies a method to choose between multiple edges. Edge properties are preserved.
`H`

= simplify(`G`

,`pickmethod`

)`pickmethod`

can be `'first'`

(default),
`'last'`

, `'min'`

, or
`'max'`

.

specifies a method to combine the edge weights of multiple edges into the weight of
a single new edge. All other edge properties in `H`

= simplify(`G`

,`aggregatemethod`

)`G`

are dropped.
`aggregatemethod`

can be `'sum'`

or
`'mean'`

.

specifies whether to preserve or remove self-loops from the graph using any of the
input argument combinations in previous syntaxes. For example,
`H`

= simplify(___,`selfloopflag`

)`'keepselfloops'`

specifies that nodes with one or more
self-loops will have one self-loop in the simplified graph.

specifies additional options with one or more name-value pair arguments. For
example, you can specify `H`

= simplify(___,`Name,Value`

)`'PickVariable'`

and a variable in
`G.Edges`

to use that variable with the
`'min'`

or `'max'`

selection methods.

## Examples

### Simplify Multigraph to Simple Graph

Create a weighted, undirected multigraph with several edges between node 1 and node 2.

G = graph([1 1 1 1 2 3],[2 2 2 3 3 4], 1:6); G.Edges

`ans=`*6×2 table*
EndNodes Weight
________ ______
1 2 1
1 2 2
1 2 3
1 3 4
2 3 5
3 4 6

Simplify the multigraph into a simple graph, such that there is only one edge between node 1 and node 2. `simplify`

keeps the first edge between those two nodes, `G.Edges(1,:)`

, and drops the others.

G = simplify(G); G.Edges

`ans=`*4×2 table*
EndNodes Weight
________ ______
1 2 1
1 3 4
2 3 5
3 4 6

### Pick or Combine Multiple Graph Edges

Use the second input of `simplify`

to select a method that picks between multiple edges or combines multiple edges into one.

Create a weighted multigraph. In this graph, five edges occur between node 3 and node 4, but the edges have random weights. View the edges table and plot the graph for reference.

G = graph([1 2 3 3 3 3 3 3 ],[2 3 1 4 4 4 4 4],randi(10,1,8)); G.Edges

`ans=`*8×2 table*
EndNodes Weight
________ ______
1 2 9
1 3 2
2 3 10
3 4 10
3 4 7
3 4 1
3 4 3
3 4 6

`plot(G,'EdgeLabel',G.Edges.Weight)`

The command `simplify(G)`

keeps the first of the repeated edges. However, you can specify a different pick/combine method with the second input.

The options for picking between multiple edges are: `'first'`

(default), `'last'`

, `'min'`

, and `'max'`

. Keep the repeated edge with maximum weight.

H_pick = simplify(G,'max'); plot(H_pick,'EdgeLabel',H_pick.Edges.Weight)

The options for combining multiple edges into one are: `'sum'`

and `'mean'`

. Sum repeated edges together to produce a single edge with a larger weight.

H_comb = simplify(G,'sum'); plot(H_comb,'EdgeLabel',H_comb.Edges.Weight)

### Preserve Self-Loops in Graph

Simplify a graph while preserving self-loops using the `'keepselfloops'`

option.

Create a multigraph with two nodes and several self-loops. Simplify the graph and preserve self-loops.

```
G = graph([1 1 1 1 1 1 1 2 2 2 2],[1 1 1 1 2 2 2 2 2 2 2 ]);
plot(G)
axis equal
```

G = simplify(G,'keepselfloops'); plot(G) axis equal

### Edge Indices and Counts of Repeated Edges

Use the second and third outputs of `simplify`

to get information about how many (and which) edges are combined.

Create an undirected multigraph with three nodes and four edges.

G = graph([1 1 1 2],[2 2 3 3]); G.Edges

`ans=`*4×1 table*
EndNodes
________
1 2
1 2
1 3
2 3

Simplify the graph and specify three outputs to get additional information about the combined edges.

[G,ei,ec] = simplify(G)

G = graph with properties: Edges: [3x1 table] Nodes: [3x0 table]

`ei = `*4×1*
1
1
2
3

`ec = `*3×1*
2
1
1

`ei(i)`

is the edge in the simplified graph that represents edge `i`

in the old graph. Since the first two edges are repeated, `ei(1) = ei(2) = 1`

. Also, `ec(1) = 2`

, since there are two edges in the new graph corresponding to edge 1 in the old graph.

### Simplify Graph Using Specific Edge Variables

Show how to simplify a multigraph using the `'PickVariable'`

and `'AggregationVariables'`

name-value pairs.

Create a multigraph where the nodes represent locations and the edges represent modes of transport. The edges have properties that reflect the cost and time of each mode of transportation. Preview the edges table.

G = graph([1 1 1 1 1 1 2 2 2],[2 2 2 3 3 3 3 3 3],[],{'New York', 'Boston', 'Washington D.C.'}); G.Edges.Mode = categorical([1 2 3 1 2 3 1 2 3],[1 2 3],{'Air' 'Train' 'Bus'})'; G.Edges.Cost = [400 80 40 250 100 75 325 150 100]'; G.Edges.Time = [1 7 5 1.5 10 8 1.75 11 9]'; G.Edges

`ans=`*9×4 table*
EndNodes Mode Cost Time
___________________________________ _____ ____ ____
{'New York'} {'Boston' } Air 400 1
{'New York'} {'Boston' } Train 80 7
{'New York'} {'Boston' } Bus 40 5
{'New York'} {'Washington D.C.'} Air 250 1.5
{'New York'} {'Washington D.C.'} Train 100 10
{'New York'} {'Washington D.C.'} Bus 75 8
{'Boston' } {'Washington D.C.'} Air 325 1.75
{'Boston' } {'Washington D.C.'} Train 150 11
{'Boston' } {'Washington D.C.'} Bus 100 9

Plot the graph for reference. Label the transportation mode on each edge, make the edge line widths proportional to the time, and the color of each edge proportional to the cost.

plot(G,'EdgeLabel',cellstr(G.Edges.Mode),'LineWidth',G.Edges.Time./min(G.Edges.Time),'EdgeCData',G.Edges.Cost) colorbar

Use the `'min'`

selection method and specify the value of `'PickVariable'`

as the `'Time'`

variable to find the quickest mode of transport between each set of nodes.

t = simplify(G,'min','PickVariable','Time'); plot(t,'EdgeLabel',cellstr(t.Edges.Mode))

Use the `'sum'`

aggregation method and specify the value of `'AggregationVariables'`

as `'Cost'`

to compute how much money is made on each connection.

c = simplify(G,'sum','AggregationVariables','Cost'); plot(c,'EdgeLabel',c.Edges.Cost)

## Input Arguments

`pickmethod`

— Edge picking method

`'first'`

(default) | `'last'`

| `'min'`

| `'max'`

Edge picking method, specified as `'first'`

,
`'last'`

, `'min'`

, or
`'max'`

. The edge picking method provides a way to
choose which of several edges to preserve when more than one edge exists
between the same two nodes.

If the method is

`'first'`

or`'last'`

, then`simplify`

preserves only the first or last edge that occurs in the edges table`G.Edges`

.If the method is

`'min'`

or`'max'`

, then`simplify`

preserves only the edge with minimum or maximum weight. The`Weight`

variable must exist in`G.Edges`

, unless you use the`'PickVariable'`

name-value pair to base the selection on a different variable.

**Example: **`simplify(G,'last')`

`aggregatemethod`

— Aggregation method

`'sum'`

| `'mean'`

Aggregation method, specified as either `'sum'`

or
`'mean'`

. The aggregation method provides a way to
combine several edges into a single edge when there is more than one edge
between the same two nodes.

By default, `simplify`

only sums or averages the edge
weights in the graph and drops all other edge properties. However, you can
use the `'AggregationVariables'`

name-value pair to specify
which numeric variables in `G.Edges`

to preserve and
aggregate.

**Example: **`simplify(G,'sum')`

`selfloopflag`

— Toggle to keep self-loops

`'omitselfloops'`

(default) | `'keepselfloops'`

Toggle to keep self-loops, specified as either:

`'omitselfloops'`

— Remove all self-loops from the graph. This is the default.`'keepselfloops'`

— Nodes with one or more self-loops have a single self-loop in the simplified graph.

**Example: **`simplify(G,'sum','keepselfloops')`

### Name-Value Arguments

Specify optional pairs of arguments as
`Name1=Value1,...,NameN=ValueN`

, where `Name`

is
the argument name and `Value`

is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.

*
Before R2021a, use commas to separate each name and value, and enclose*
`Name`

*in quotes.*

**Example: **```
H = simplify(G,'sum','AggregationVariables',{'Var1'
'Var2'})
```

`PickVariable`

— Variable to base edge selection on

`'Weight'`

(default) | variable name | numeric index

Variable to base edge selection on, specified as the comma-separated
pair consisting of `'PickVariable'`

and a variable name
or numeric index. Use this name-value pair to select an edge variable in
`G.Edges`

other than `'Weight'`

to
use with the `'min'`

or `'max'`

picking methods. `simplify`

preserves only the edge
with the minimum or maximum value of the selected variable when there
are several edges between the same two nodes.

**Example: **`simplify(G,'min','PickVariable',3)`

**Example: **`simplify(G,'min','PickVariable','var3')`

**Data Types: **`single`

| `double`

| `int8`

| `int16`

| `int32`

| `int64`

| `uint8`

| `uint16`

| `uint32`

| `uint64`

| `char`

| `string`

`AggregationVariables`

— Variables to aggregate

`'Weight'`

(default) | variable name | cell array of variable names | string array | numeric vector | logical vector | function handle

Variables to aggregate, specified as the comma-separated pair
consisting of `'AggregationVariables'`

and a variable
name, cell array of variable names, string array, numeric vector,
logical vector, or function handle. Use this name-value pair to select
one or more edge variables in `G.Edges`

that can be
combined with the `'sum'`

or `'mean'`

aggregation methods. `simplify`

combines the values
of these variables into a single value for one new edge when there are
several edges between the same two nodes. The value of
`'AggregationVariables'`

can be one of the following:

Character vector specifying a single table variable name

Cell array of character vectors where each element is a table variable name

String array specifying one or more variable names

Vector of table variable indices

Logical vector whose elements correspond to table variables, where

`true`

includes the corresponding variable and`false`

excludes itA function handle that takes the

`G.Edges`

table as input and returns a logical scalar, such as`@isnumeric`

**Example: **```
simplify(G,'sum','AggregationVariables',[4 5
6])
```

**Example: **```
simplify(G,'mean','AggregationVariables',{'var5
var7'})
```

**Example: **`simplify(G,'mean','AggregationVariables',@isnumeric)`

**Data Types: **`single`

| `double`

| `logical`

| `function_handle`

| `char`

| `string`

| `cell`

## Output Arguments

`H`

— Simplified graph

`graph`

object | `digraph`

object

Simplified graph, returned as a `graph`

or
`digraph`

object. `H`

does not contain
any repeated edges between the same two nodes, such that
`ismultigraph(H)`

returns logical `0`

(`false`

). Self-loops also are removed, unless you
specify the `'keepselfloops'`

option.

`eind`

— Edge indices

vector

Edge indices, returned as a vector. The edge in `H`

that
represents edge `i`

in `G`

is given by
`H.Edges(eind(i),:)`

. If edge `i`

in
`G`

is a self-loop that was removed, then
`eind(i)`

is `0`

.

`ecount`

— Edge counts

vector

Edge counts, returned as a vector. `ecount(i)`

is the
number of edges in `G`

that correspond to edge
`i`

in `H`

.

## Extended Capabilities

### Thread-Based Environment

Run code in the background using MATLAB® `backgroundPool`

or accelerate code with Parallel Computing Toolbox™ `ThreadPool`

.

## Version History

**Introduced in R2018a**

## See Also

`graph`

| `digraph`

| `ismultigraph`

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