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ismultigraph

Determine whether graph has multiple edges

Description

example

tf = ismultigraph(G) returns logical 1 (true) if G has multiple edges between any two nodes. Otherwise, it returns logical 0 (false).

By convention, ismultigraph returns logical 0 (false) for graphs that contain self-loops, but no repeated edges. However, a graph with multiple self-loops is considered to be a multigraph.

Examples

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Use ismultigraph to determine whether an input graph has multiple edges between two nodes.

Create a graph.

G = graph([1 1 1 1 1 2 2 2],[2 2 3 4 5 6 7 8]);
plot(G)

Check to see if G is a multigraph. The result is logical 1 (true) because there are two edges between nodes 1 and 2.

tf = ismultigraph(G)
tf = logical
   1

Use ismultigraph to determine whether a graph needs to be simplified.

It is common to encounter duplicate edges when you create an empty graph and programmatically add edges to it with addedge. The data used for the edges needs to be unique to avoid duplicates.

To demonstrate this, create an empty graph and a matrix with two columns of random numbers. Since the random numbers are only between 1 and 5, this data produces multiple edges.

G = graph;
rng default % for reproducibility
X = randi(5,15,2)
X = 15×2

     5     1
     5     3
     1     5
     5     4
     4     5
     1     4
     2     1
     3     5
     5     5
     5     4
      ⋮

Instead of cleaning the source data to ensure the rows are unique, add all of the edges to the graph. Plot the graph for reference.

G = addedge(G,X(:,1),X(:,2));
plot(G)

Test to see if the graph is a multigraph and, if it is, use simplify to remove repeated edges and self-loops.

if ismultigraph(G)
    G = simplify(G);
end

Plot the resulting graph.

plot(G)

Input Arguments

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Input graph, specified as either a graph or digraph object. Use graph to create an undirected graph or digraph to create a directed graph.

Example: G = graph(1,2)

Example: G = digraph([1 2],[2 3])

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