# Connected components

Graphs.jl includes several functions dealing with connected components.

## Full docs

Graphs.attracting_componentsFunction
attracting_components(g)

Return a vector of vectors of integers representing lists of attracting components in the directed graph g.

The attracting components are a subset of the strongly connected components in which the components do not have any leaving edges.

Examples

julia> using Graphs

julia> g = SimpleDiGraph([0 1 0 0 0; 0 0 1 0 0; 1 0 0 1 0; 0 0 0 0 1; 0 0 0 1 0])
{5, 6} directed simple Int64 graph

julia> strongly_connected_components(g)
2-element Vector{Vector{Int64}}:
[4, 5]
[1, 2, 3]

julia> attracting_components(g)
1-element Vector{Vector{Int64}}:
[4, 5]
source
Graphs.componentsMethod
components(labels)

Given a vector of component labels, return a vector of vectors representing the vertices associated with a given component id.

source
Graphs.components_dictMethod
components_dict(labels)

Convert an array of labels to a map of component id to vertices, and return a map with each key corresponding to a given component id and each value containing the vertices associated with that component.

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Graphs.condensationFunction
condensation(g[, scc])

Return the condensation graph of the strongly connected components scc in the directed graph g. If scc is missing, generate the strongly connected components first.

Examples

julia> using Graphs

julia> g = SimpleDiGraph([0 1 0 0 0; 0 0 1 0 0; 1 0 0 1 0; 0 0 0 0 1; 0 0 0 1 0])
{5, 6} directed simple Int64 graph

julia> strongly_connected_components(g)
2-element Vector{Vector{Int64}}:
[4, 5]
[1, 2, 3]

julia> foreach(println, edges(condensation(g)))
Edge 2 => 1
source
Graphs.connected_components!Method
connected_components!(label, g)

Fill label with the id of the connected component in the undirected graph g to which it belongs. Return a vector representing the component assigned to each vertex. The component value is the smallest vertex ID in the component.

Performance

This algorithm is linear in the number of edges of the graph.

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Graphs.connected_componentsMethod
connected_components(g)

Return the connected components of an undirected graph g as a vector of components, with each element a vector of vertices belonging to the component.

For directed graphs, see strongly_connected_components and weakly_connected_components.

Examples

julia> using Graphs

julia> g = SimpleGraph([0 1 0; 1 0 1; 0 1 0]);

julia> connected_components(g)
1-element Vector{Vector{Int64}}:
[1, 2, 3]

julia> g = SimpleGraph([0 1 0 0 0; 1 0 1 0 0; 0 1 0 0 0; 0 0 0 0 1; 0 0 0 1 0]);

julia> connected_components(g)
2-element Vector{Vector{Int64}}:
[1, 2, 3]
[4, 5]
source
Graphs.is_connectedMethod
is_connected(g)

Return true if graph g is connected. For directed graphs, return true if graph g is weakly connected.

Examples

julia> using Graphs

julia> g = SimpleGraph([0 1 0; 1 0 1; 0 1 0]);

julia> is_connected(g)
true

julia> g = SimpleGraph([0 1 0 0 0; 1 0 1 0 0; 0 1 0 0 0; 0 0 0 0 1; 0 0 0 1 0]);

julia> is_connected(g)
false

julia> g = SimpleDiGraph([0 1 0; 0 0 1; 1 0 0]);

julia> is_connected(g)
true
source
Graphs.is_strongly_connectedFunction
is_strongly_connected(g)

Return true if directed graph g is strongly connected.

Examples

julia> using Graphs

julia> g = SimpleDiGraph([0 1 0; 0 0 1; 1 0 0]);

julia> is_strongly_connected(g)
true
source
Graphs.is_weakly_connectedMethod
is_weakly_connected(g)

Return true if the graph g is weakly connected. If g is undirected, this function is equivalent to is_connected(g).

Examples

julia> using Graphs

julia> g = SimpleDiGraph([0 1 0; 0 0 1; 1 0 0]);

julia> is_weakly_connected(g)
true

julia> g = SimpleDiGraph([0 1 0; 1 0 1; 0 0 0]);

julia> is_connected(g)
true

julia> is_strongly_connected(g)
false

julia> is_weakly_connected(g)
true
source
Graphs.isdigraphicalMethod
isdigraphical(indegree_sequence, outdegree_sequence)

Check whether the given indegree sequence and outdegree sequence are digraphical, that is whether they can be the indegree and outdegree sequence of a simple digraph (i.e. a directed graph with no loops). This implies that indegree_sequence and outdegree_sequence are not independent, as their elements respectively represent the indegrees and outdegrees that the vertices shall have.

Implementation Notes

According to Fulkerson-Chen-Anstee theorem, a sequence $\{(a_1, b_1), ...,(a_n, b_n)\}$ (sorted in descending order of a) is graphic iff $\sum_{i = 1}^{n} a_i = \sum_{i = 1}^{n} b_i\}$ and the sequence obeys the property -

$$$\sum_{i=1}^{r} a_i \leq \sum_{i=1}^n min(r-1,b_i) + \sum_{i=r+1}^n min(r,b_i)$$$

for each integer 1 <= r <= n-1.

See also: isgraphical

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Graphs.isgraphicalMethod
isgraphical(degs)

Return true if the degree sequence degs is graphical. A sequence of integers is called graphical, if there exists a graph where the degrees of its vertices form that same sequence.

Performance

Time complexity: $\mathcal{O}(|degs|*\log(|degs|))$.

Implementation Notes

According to Erdös-Gallai theorem, a degree sequence $\{d_1, ...,d_n\}$ (sorted in descending order) is graphic iff the sum of vertex degrees is even and the sequence obeys the property -

$$$\sum_{i=1}^{r} d_i \leq r(r-1) + \sum_{i=r+1}^n min(r,d_i)$$$

for each integer r <= n-1.

See also: isdigraphical

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Graphs.neighborhoodMethod
neighborhood(g, v, d, distmx=weights(g))

Return a vector of each vertex in g at a geodesic distance less than or equal to d, where distances may be specified by distmx.

Optional Arguments

• dir=:out: If g is directed, this argument specifies the edge direction

with respect to v of the edges to be considered. Possible values: :in or :out.

Examples

julia> using Graphs

julia> g = SimpleDiGraph([0 1 0 0 0; 0 0 1 0 0; 1 0 0 1 0; 0 0 0 0 1; 0 0 0 1 0]);

julia> neighborhood(g, 1, 2)
3-element Vector{Int64}:
1
2
3

julia> neighborhood(g, 1, 3)
4-element Vector{Int64}:
1
2
3
4

julia> neighborhood(g, 1, 3, [0 1 0 0 0; 0 0 1 0 0; 1 0 0 0.25 0; 0 0 0 0 0.25; 0 0 0 0.25 0])
5-element Vector{Int64}:
1
2
3
4
5
source
Graphs.neighborhood_distsMethod
neighborhood_dists(g, v, d, distmx=weights(g))

Return a a vector of tuples representing each vertex which is at a geodesic distance less than or equal to d, along with its distance from v. Non-negative distances may be specified by distmx.

Optional Arguments

• dir=:out: If g is directed, this argument specifies the edge direction

with respect to v of the edges to be considered. Possible values: :in or :out.

Examples

julia> using Graphs

julia> g = SimpleDiGraph([0 1 0 0 0; 0 0 1 0 0; 1 0 0 1 0; 0 0 0 0 1; 0 0 0 1 0]);

julia> neighborhood_dists(g, 1, 3)
4-element Vector{Tuple{Int64, Int64}}:
(1, 0)
(2, 1)
(3, 2)
(4, 3)

julia> neighborhood_dists(g, 1, 3, [0 1 0 0 0; 0 0 1 0 0; 1 0 0 0.25 0; 0 0 0 0 0.25; 0 0 0 0.25 0])
5-element Vector{Tuple{Int64, Float64}}:
(1, 0.0)
(2, 1.0)
(3, 2.0)
(4, 2.25)
(5, 2.5)

julia> neighborhood_dists(g, 4, 3)
2-element Vector{Tuple{Int64, Int64}}:
(4, 0)
(5, 1)

julia> neighborhood_dists(g, 4, 3, dir=:in)
5-element Vector{Tuple{Int64, Int64}}:
(4, 0)
(3, 1)
(5, 1)
(2, 2)
(1, 3)
source
Graphs.periodFunction
period(g)

Return the (common) period for all vertices in a strongly connected directed graph. Will throw an error if the graph is not strongly connected.

Examples

julia> using Graphs

julia> g = SimpleDiGraph([0 1 0; 0 0 1; 1 0 0]);

julia> period(g)
3
source
Graphs.strongly_connected_componentsMethod
strongly_connected_components(g)

Compute the strongly connected components of a directed graph g.

Return an array of arrays, each of which is the entire connected component.

Implementation Notes

The order of the components is not part of the API contract.

Examples

julia> using Graphs

julia> g = SimpleDiGraph([0 1 0; 1 0 1; 0 0 0]);

julia> strongly_connected_components(g)
2-element Vector{Vector{Int64}}:

[1, 2]

julia> g = SimpleDiGraph(11)
{11, 0} directed simple Int64 graph

julia> edge_list=[(1,2),(2,3),(3,4),(4,1),(3,5),(5,6),(6,7),(7,5),(5,8),(8,9),(9,8),(10,11),(11,10)];

julia> g = SimpleDiGraph(Edge.(edge_list))
{11, 13} directed simple Int64 graph

julia> strongly_connected_components(g)
4-element Vector{Vector{Int64}}:
[8, 9]
[5, 6, 7]
[1, 2, 3, 4]
[10, 11]
source
Graphs.strongly_connected_components_kosarajuFunction
strongly_connected_components_kosaraju(g)

Compute the strongly connected components of a directed graph g using Kosaraju's Algorithm. (https://en.wikipedia.org/wiki/Kosaraju%27s_algorithm).

Return an array of arrays, each of which is the entire connected component.

Performance

Time Complexity : O(|E|+|V|) Space Complexity : O(|V|) {Excluding the memory required for storing graph}

|V| = Number of vertices |E| = Number of edges

Examples

julia> using Graphs

julia> g=SimpleDiGraph(3)
{3, 0} directed simple Int64 graph

julia> g = SimpleDiGraph([0 1 0 ; 0 0 1; 0 0 0])
{3, 2} directed simple Int64 graph

julia> strongly_connected_components_kosaraju(g)
3-element Vector{Vector{Int64}}:




julia> g=SimpleDiGraph(11)
{11, 0} directed simple Int64 graph

julia> edge_list=[(1,2),(2,3),(3,4),(4,1),(3,5),(5,6),(6,7),(7,5),(5,8),(8,9),(9,8),(10,11),(11,10)]
13-element Vector{Tuple{Int64, Int64}}:
(1, 2)
(2, 3)
(3, 4)
(4, 1)
(3, 5)
(5, 6)
(6, 7)
(7, 5)
(5, 8)
(8, 9)
(9, 8)
(10, 11)
(11, 10)

julia> g = SimpleDiGraph(Edge.(edge_list))
{11, 13} directed simple Int64 graph

julia> strongly_connected_components_kosaraju(g)
4-element Vector{Vector{Int64}}:
[11, 10]
[2, 3, 4, 1]
[6, 7, 5]
[9, 8]

source
Graphs.strongly_connected_components_tarjanFunction
strongly_connected_components_tarjan(g)

Compute the strongly connected components of a directed graph g using Tarjan's algorithm.

Return an array of arrays, each of which is the entire connected component.

Implementation Notes

The returned components will be ordered reverse topologically.

Examples

julia> using Graphs

julia> g = SimpleDiGraph([0 1 0; 1 0 1; 0 0 0]);

julia> strongly_connected_components_tarjan(g)
2-element Vector{Vector{Int64}}:

[1, 2]

julia> g = SimpleDiGraph(11)
{11, 0} directed simple Int64 graph

julia> edge_list=[(1,2),(2,3),(3,4),(4,1),(3,5),(5,6),(6,7),(7,5),(5,8),(8,9),(9,8),(10,11),(11,10)];

julia> g = SimpleDiGraph(Edge.(edge_list))
{11, 13} directed simple Int64 graph

julia> strongly_connected_components_tarjan(g)
4-element Vector{Vector{Int64}}:
[8, 9]
[5, 6, 7]
[1, 2, 3, 4]
[10, 11]
source
Graphs.weakly_connected_componentsMethod
weakly_connected_components(g)

Return the weakly connected components of the graph g. This is equivalent to the connected components of the undirected equivalent of g. For undirected graphs this is equivalent to the connected_components of g.

Examples

julia> using Graphs

julia> g = SimpleDiGraph([0 1 0; 1 0 1; 0 0 0]);

julia> weakly_connected_components(g)
1-element Vector{Vector{Int64}}:
[1, 2, 3]
source