# `Graph` This module defines a graph data structure, which supports directed and undirected graphs, in both acyclic and cyclic forms. It also defines the API for creating, manipulating, and querying that structure. As far as memory usage is concerned, `Graph` should be fairly compact in memory, but if you want to do a rough comparison between the memory usage for a graph between `libgraph` and `digraph`, use `:digraph.info/1` and `Graph.info/1` on the two graphs, and both results will contain memory usage information. Keep in mind we don't have a precise way to measure the memory usage of a term in memory, whereas ETS is able to give a more precise answer, but we do have a fairly good way to estimate the usage of a term, and we use that method within `libgraph`. The Graph struct is structured like so: - A map of vertex ids to vertices (`vertices`) - A map of vertex ids to their out neighbors (`out_edges`), - A map of vertex ids to their in neighbors (`in_edges`), effectively the transposition of `out_edges` - A map of vertex ids to vertex labels (`vertex_labels`), (labels are only stored if a non-nil label was provided) - A map of edge ids (where an edge id is simply a tuple of `{vertex_id, vertex_id}`) to a map of edge metadata (`edges`) - Edge metadata is a map of `label => weight`, and each entry in that map represents a distinct edge. This allows us to support multiple edges in the same direction between the same pair of vertices, but for many purposes simply treat them as a single logical edge. This structure is designed to be as efficient as possible once a graph is built, but it turned out that it is also quite efficient for manipulating the graph as well. For example, splitting an edge and introducing a new vertex on that edge can be done with very little effort. We use vertex ids everywhere because we can generate them without any lookups, we don't incur any copies of the vertex structure, and they are very efficient as keys in a map. ## Multigraphs When `multigraph: true` is passed to `Graph.new/1`, an edge adjacency index (`edge_index`) is maintained alongside the standard graph structure. This index partitions edges by a key derived from a `partition_by` function (defaulting to `Graph.Utils.by_edge_label/1`, which partitions by edge label). The index structure is `%{partition_key => %{vertex_id => MapSet.t(edge_key)}}`, enabling O(1) map-access retrieval of edges by partition, avoiding O(E) scans over all edges. Query functions such as `edges/2`, `out_edges/3`, and `in_edges/3` accept `:by` and `:where` options to filter edges by partition or predicate. Traversal and pathfinding algorithms (`Graph.Reducers.Bfs`, `Graph.Reducers.Dfs`, `dijkstra/4`, `a_star/5`, `bellman_ford/3`) also accept a `:by` option to restrict traversal to edges in specific partitions. ## Edge Properties Edges support an arbitrary `properties` map (default `%{}`) for storing additional metadata beyond weight and label. Properties can be set via `add_edge/4` and are preserved through all graph operations. # `edge_index_key` ```elixir @type edge_index_key() :: label() | term() ``` # `edge_key` ```elixir @type edge_key() :: {vertex_id(), vertex_id()} ``` # `edge_value` ```elixir @type edge_value() :: %{required(label()) => edge_weight()} ``` # `edge_weight` ```elixir @type edge_weight() :: integer() | float() ``` # `graph_info` ```elixir @type graph_info() :: %{ num_edges: non_neg_integer(), num_vertices: non_neg_integer(), size_in_bytes: number(), type: :directed | :undirected } ``` # `graph_type` ```elixir @type graph_type() :: :directed | :undirected ``` # `label` ```elixir @type label() :: term() ``` # `t` ```elixir @type t() :: %Graph{ edge_index: %{required(edge_index_key()) => MapSet.t()}, edge_properties: term(), edges: %{required(edge_key()) => edge_value()}, in_edges: %{required(vertex_id()) => MapSet.t()}, multigraph: boolean(), out_edges: %{required(vertex_id()) => MapSet.t()}, partition_by: (Graph.Edge.t() -> [edge_index_key()]), type: graph_type(), vertex_identifier: (vertex() -> term()), vertex_labels: %{required(vertex_id()) => term()}, vertices: %{required(vertex_id()) => vertex()} } ``` # `vertex` ```elixir @type vertex() :: term() ``` # `vertex_id` ```elixir @type vertex_id() :: non_neg_integer() | term() ``` Identifier of a vertex. By default a non_neg_integer from `Graph.Utils.vertex_id/1` utilizing `:erlang.phash2`. # `vertices` ```elixir @type vertices() :: %{required(vertex_id()) => vertex()} ``` # `a_star` ```elixir @spec a_star(t(), vertex(), vertex(), (vertex(), vertex() -> integer())) :: [vertex()] ``` Gets the shortest path between `a` and `b`. The A* algorithm is very much like Dijkstra's algorithm, except in addition to edge weights, A* also considers a heuristic function for determining the lower bound of the cost to go from vertex `v` to `b`. The lower bound *must* be less than the cost of the shortest path from `v` to `b`, otherwise it will do more harm than good. Dijkstra's algorithm can be reframed as A* where `lower_bound(v)` is always 0. This function puts the heuristics in your hands, so you must provide the heuristic function, which should take a single parameter, `v`, which is the vertex being currently examined. Your heuristic should then determine what the lower bound for the cost to reach `b` from `v` is, and return that value. ## Example iex> g = Graph.new |> Graph.add_edges([{:a, :b}, {:b, :c}, {:c, :d}, {:b, :d}]) ...> Graph.a_star(g, :a, :d, fn _ -> 0 end) [:a, :b, :d] iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c, :d]) ...> g = Graph.add_edges(g, [{:a, :c}, {:b, :c}, {:b, :d}]) ...> Graph.a_star(g, :a, :d, fn _ -> 0 end) nil # `a_star` ```elixir @spec a_star(t(), vertex(), vertex(), (vertex(), vertex() -> integer()), keyword()) :: [vertex()] ``` Like `a_star/4`, but accepts options for multigraph partition filtering. ## Options - `:by` - a partition key or list of partition keys to restrict edge traversal ## Example iex> g = Graph.new(multigraph: true) |> Graph.add_edges([ ...> {:a, :b, label: :fast, weight: 1}, ...> {:a, :c, label: :slow, weight: 10}, ...> {:b, :d, label: :fast, weight: 1}, ...> {:c, :d, label: :slow, weight: 1} ...> ]) ...> Graph.a_star(g, :a, :d, fn _ -> 0 end, by: :fast) [:a, :b, :d] # `add_edge` ```elixir @spec add_edge(t(), Graph.Edge.t()) :: t() ``` Like `add_edge/3` or `add_edge/4`, but takes a `Graph.Edge` struct created with `Graph.Edge.new/2` or `Graph.Edge.new/3`. ## Example iex> g = Graph.new |> Graph.add_edge(Graph.Edge.new(:a, :b)) ...> [:a, :b] = Graph.vertices(g) ...> Graph.edges(g) [%Graph.Edge{v1: :a, v2: :b}] # `add_edge` ```elixir @spec add_edge(t(), vertex(), vertex(), Graph.Edge.edge_opts()) :: t() | no_return() ``` Adds an edge connecting `v1` to `v2`. If either `v1` or `v2` do not exist in the graph, they are automatically added. Adding the same edge more than once does not create multiple edges, each edge is only ever stored once. Edges have a default weight of 1, and an empty (nil) label. You can change this by passing options to this function, as shown below. ## Example iex> g = Graph.new |> Graph.add_edge(:a, :b) ...> [:a, :b] = Graph.vertices(g) ...> Graph.edges(g) [%Graph.Edge{v1: :a, v2: :b, label: nil, weight: 1}] iex> g = Graph.new |> Graph.add_edge(:a, :b, label: :foo, weight: 2) ...> [:a, :b] = Graph.vertices(g) ...> Graph.edges(g) [%Graph.Edge{v1: :a, v2: :b, label: :foo, weight: 2}] # `add_edges` ```elixir @spec add_edges(t(), [Graph.Edge.t()] | Enumerable.t()) :: t() | no_return() ``` This function is like `add_edge/3`, but for multiple edges at once, it also accepts edge specifications in a few different ways to make it easy to generate graphs succinctly. Edges must be provided as a list of `Edge` structs, `{vertex, vertex}` pairs, or `{vertex, vertex, edge_opts :: [label: term, weight: integer, properties: map]}`. See the docs for `Graph.Edge.new/2` or `Graph.Edge.new/3` for more info on creating Edge structs, and `add_edge/3` for information on edge options. If an invalid edge specification is provided, raises `Graph.EdgeSpecificationError`. ## Examples iex> alias Graph.Edge ...> edges = [Edge.new(:a, :b), Edge.new(:b, :c, weight: 2)] ...> g = Graph.new |> Graph.add_vertices([:a, :b, :c]) |> Graph.add_edges(edges) ...> Graph.edges(g) |> Enum.sort_by(& {&1.v1, &1.v2, &1.label}) [%Graph.Edge{v1: :a, v2: :b}, %Graph.Edge{v1: :b, v2: :c, weight: 2}] iex> g = Graph.new |> Graph.add_edges([{:a, :b}, {:a, :b, label: :foo}, {:a, :b, label: :foo, weight: 2}]) ...> Graph.edges(g) |> Enum.sort_by(& {&1.v1, &1.v2, &1.label}) [%Graph.Edge{v1: :a, v2: :b, label: :foo, weight: 2}, %Graph.Edge{v1: :a, v2: :b}] iex> Graph.new |> Graph.add_vertices([:a, :b, :c]) |> Graph.add_edges([:a, :b]) ** (Graph.EdgeSpecificationError) Expected a valid edge specification, but got: :a # `add_vertex` ```elixir @spec add_vertex(t(), vertex(), label()) :: t() ``` Adds a new vertex to the graph. If the vertex is already present in the graph, the add is a no-op. You can provide optional labels for the vertex, aside from the variety of uses this has for working with graphs, labels will also be used when exporting a graph in DOT format. ## Example iex> g = Graph.new |> Graph.add_vertex(:a, :mylabel) |> Graph.add_vertex(:a) ...> [:a] = Graph.vertices(g) ...> Graph.vertex_labels(g, :a) [:mylabel] iex> g = Graph.new |> Graph.add_vertex(:a, [:mylabel, :other]) ...> Graph.vertex_labels(g, :a) [:mylabel, :other] # `add_vertices` ```elixir @spec add_vertices(t(), [vertex()]) :: t() ``` Like `add_vertex/2`, but takes a list of vertices to add to the graph. ## Example iex> g = Graph.new |> Graph.add_vertices([:a, :b, :a]) ...> Graph.vertices(g) [:a, :b] # `arborescence_root` ```elixir @spec arborescence_root(t()) :: vertex() | nil ``` Returns the root vertex of the arborescence, if one exists, otherwise nil. # `batch_topsort` ```elixir @spec batch_topsort(t()) :: [vertex()] | false ``` Returns a batch topological ordering of the vertices of graph `g`, if such an ordering exists, otherwise it returns false. For each vertex in the returned list, no out-neighbors occur earlier in the list. This differs from `topsort/1` in that this function returns a list of lists where each sublist can be concurrently evaluated without worrying about elements in the sublist depending on eachother. Multiple edges between two vertices are considered a single edge for purposes of this sort. ## Example iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c]) ...> g = Graph.add_edges(g, [{:a, :b}, {:a, :c}]) ...> Graph.batch_topsort(g) [[:a], [:b, :c]] iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c, :d]) ...> g = Graph.add_edges(g, [{:a, :b}, {:a, :c}, {:b, :c}, {:c, :d}]) ...> Graph.batch_topsort(g) [[:a], [:b], [:c], [:d]] iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c, :d, :x, :y, :z]) ...> g = Graph.add_edges(g, [{:a, :b}, {:a, :c}, {:c, :d}, {:x, :y}, {:x, :z}]) ...> Graph.batch_topsort(g) [[:a, :x], [:b, :c, :y, :z], [:d]] iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c, :d, :x, :y, :z]) ...> g = Graph.add_edges(g, [{:a, :b}, {:a, :c}, {:b, :c}, {:c, :d}, {:c, :a}]) ...> Graph.batch_topsort(g) false # `bellman_ford` ```elixir @spec bellman_ford(t(), vertex()) :: [vertex()] ``` ## Example iex> g = Graph.new |> Graph.add_edges([ ...> {:b, :c, weight: -2}, {:a, :b, weight: 1}, ...> {:c, :d, weight: 3}, {:b, :d, weight: 4}]) ...> Graph.bellman_ford(g, :a) %{a: 0, b: 1, c: -1, d: 2} iex> g = Graph.new |> Graph.add_edges([ ...> {:b, :c, weight: -2}, {:a, :b, weight: -1}, ...> {:c, :d, weight: -3}, {:d, :a, weight: -5}]) ...> Graph.bellman_ford(g, :a) nil # `bellman_ford` ```elixir @spec bellman_ford(t(), vertex(), keyword()) :: [vertex()] ``` Like `bellman_ford/2`, but accepts options for multigraph partition filtering. ## Options - `:by` - a partition key or list of partition keys to restrict edge relaxation ## Example iex> g = Graph.new(multigraph: true) |> Graph.add_edges([ ...> {:a, :b, label: :fast, weight: 1}, ...> {:b, :c, label: :fast, weight: 2}, ...> {:a, :c, label: :slow, weight: 100} ...> ]) ...> distances = Graph.bellman_ford(g, :a, by: :fast) ...> distances[:c] 3 # `cliques` ```elixir @spec cliques(t()) :: [[vertex()]] ``` Detects all maximal cliques in the provided graph. Returns a list of cliques, where each clique is a list of vertices in the clique. A clique is a subset `vs` of the vertices in the given graph, which together form a complete graph; or put another way, every vertex in `vs` is connected to all other vertices in `vs`. # `components` ```elixir @spec components(t()) :: [[vertex()]] ``` Returns a list of connected components, where each component is a list of vertices. A *connected component* is a maximal subgraph such that there is a path between each pair of vertices, considering all edges undirected. A *subgraph* is a graph whose vertices and edges are a subset of the vertices and edges of the source graph. A *maximal subgraph* is a subgraph with property `P` where all other subgraphs which contain the same vertices do not have that same property `P`. ## Example iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c, :d]) ...> g = Graph.add_edges(g, [{:a, :b}, {:a, :c}, {:b, :c}, {:c, :d}, {:c, :a}]) ...> Graph.components(g) [[:d, :b, :c, :a]] # `coreness` ```elixir @spec coreness(t(), vertex()) :: non_neg_integer() ``` Calculates the k-coreness of vertex `v` in graph `g`. The k-coreness of a vertex is defined as the maximum value of `k` for which `v` is found in the corresponding k-core of graph `g`. NOTE: This function decomposes all k-core components to determine the coreness of a vertex - if you will be trying to determine the coreness of many vertices, it is recommended to use `k_core_components/1` and then lookup the coreness of a vertex by querying the resulting map. # `degeneracy` ```elixir @spec degeneracy(t()) :: non_neg_integer() ``` Determines the k-degeneracy of the given graph. The degeneracy of graph `g` is the maximum value of `k` for which a k-core exists in graph `g`. # `degeneracy_core` ```elixir @spec degeneracy_core(t()) :: t() ``` Calculates the degeneracy core of a given graph. The degeneracy core of a graph is the k-core of the graph where the value of `k` is the degeneracy of the graph. The degeneracy of a graph is the highest value of `k` which has a non-empty k-core in the graph. # `degree` ```elixir @spec degree(t(), vertex()) :: non_neg_integer() ``` Returns the degree of vertex `v` of graph `g`. The degree of a vertex is the total number of edges containing that vertex. For directed graphs this is the same as the sum of the in-degree and out-degree of the given vertex. For undirected graphs, the in-degree and out-degree are always the same. ## Example iex> g = Graph.new(type: :undirected) |> Graph.add_vertices([:a, :b, :c]) |> Graph.add_edge(:a, :b) ...> Graph.degree(g, :b) 1 iex> g = Graph.new() |> Graph.add_vertices([:a, :b, :c]) |> Graph.add_edge(:a, :b) ...> Graph.degree(g, :b) 1 # `delete_edge` ```elixir @spec delete_edge(t(), vertex(), vertex()) :: t() ``` Removes all edges connecting `v1` to `v2`, regardless of label. If no such edge exists, the graph is returned unmodified. ## Example iex> g = Graph.new |> Graph.add_edges([{:a, :b}, {:a, :b, label: :foo}]) ...> g = Graph.delete_edge(g, :a, :b) ...> [:a, :b] = Graph.vertices(g) ...> Graph.edges(g) [] iex> g = Graph.new(type: :undirected) |> Graph.add_edges([{:a, :b}, {:a, :b, label: :foo}]) ...> g = Graph.delete_edge(g, :a, :b) ...> [:a, :b] = Graph.vertices(g) ...> Graph.edges(g) [] # `delete_edge` ```elixir @spec delete_edge(t(), vertex(), vertex(), label()) :: t() ``` Removes an edge connecting `v1` to `v2`. A label can be specified to disambiguate the specific edge you wish to delete, if not provided, the unlabelled edge, if one exists, will be removed. If no such edge exists, the graph is returned unmodified. ## Example iex> g = Graph.new |> Graph.add_edges([{:a, :b}, {:a, :b, label: :foo}]) ...> g = Graph.delete_edge(g, :a, :b, nil) ...> [:a, :b] = Graph.vertices(g) ...> Graph.edges(g) [%Graph.Edge{v1: :a, v2: :b, label: :foo}] iex> g = Graph.new |> Graph.add_edges([{:a, :b}, {:a, :b, label: :foo}]) ...> g = Graph.delete_edge(g, :a, :b, :foo) ...> [:a, :b] = Graph.vertices(g) ...> Graph.edges(g) [%Graph.Edge{v1: :a, v2: :b, label: nil}] iex> g = Graph.new(type: :undirected) |> Graph.add_edges([{:a, :b}, {:a, :b, label: :foo}]) ...> g = Graph.delete_edge(g, :a, :b, :foo) ...> [:a, :b] = Graph.vertices(g) ...> Graph.edges(g) [%Graph.Edge{v1: :a, v2: :b, label: nil}] # `delete_edges` ```elixir @spec delete_edges(t(), [{vertex(), vertex()}]) :: t() | no_return() ``` Like `delete_edge/3`, but takes a list of edge specifications, and deletes the corresponding edges from the graph, if they exist. Edge specifications can be `Edge` structs, `{vertex, vertex}` pairs, or `{vertex, vertex, label: label}` triplets. An invalid specification will cause `Graph.EdgeSpecificationError` to be raised. ## Examples iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c]) |> Graph.add_edge(:a, :b) ...> g = Graph.delete_edges(g, [{:a, :b}]) ...> Graph.edges(g) [] iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c]) |> Graph.add_edge(:a, :b, label: :foo) ...> g = Graph.delete_edges(g, [{:a, :b}]) ...> Graph.edges(g) [] iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c]) |> Graph.add_edge(:a, :b, label: :foo) ...> g = Graph.delete_edges(g, [{:a, :b, label: :bar}]) ...> Graph.edges(g) [%Graph.Edge{v1: :a, v2: :b, label: :foo}] iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c]) |> Graph.add_edge(:a, :b, label: :foo) ...> g = Graph.delete_edges(g, [{:a, :b, label: :foo}]) ...> Graph.edges(g) [] iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c]) |> Graph.add_edge(:a, :b) ...> Graph.delete_edges(g, [:a]) ** (Graph.EdgeSpecificationError) Expected a valid edge specification, but got: :a # `delete_edges` ```elixir @spec delete_edges(t(), vertex(), vertex()) :: t() ``` This function can be used to remove all edges between `v1` and `v2`. This is useful if you are defining multiple edges between vertices to represent different relationships, but want to remove them all as if they are a single unit. ## Examples iex> g = Graph.new |> Graph.add_edges([{:a, :b}, {:a, :b, label: :foo}, {:b, :a}]) ...> g = Graph.delete_edges(g, :a, :b) ...> Graph.edges(g) [%Graph.Edge{v1: :b, v2: :a}] iex> g = Graph.new(type: :undirected) |> Graph.add_edges([{:a, :b}, {:a, :b, label: :foo}, {:b, :a}]) ...> g = Graph.delete_edges(g, :a, :b) ...> Graph.edges(g) [] # `delete_vertex` ```elixir @spec delete_vertex(t(), vertex()) :: t() ``` Removes a vertex from the graph, as well as any edges which refer to that vertex. If the vertex does not exist in the graph, it is a no-op. ## Example iex> g = Graph.new |> Graph.add_vertex(:a) |> Graph.add_vertex(:b) |> Graph.add_edge(:a, :b) ...> [:a, :b] = Graph.vertices(g) ...> [%Graph.Edge{v1: :a, v2: :b}] = Graph.edges(g) ...> g = Graph.delete_vertex(g, :b) ...> [:a] = Graph.vertices(g) ...> Graph.edges(g) [] # `delete_vertices` ```elixir @spec delete_vertices(t(), [vertex()]) :: t() ``` Like `delete_vertex/2`, but takes a list of vertices to delete from the graph. ## Example iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c]) |> Graph.delete_vertices([:a, :b]) ...> Graph.vertices(g) [:c] # `dijkstra` ```elixir @spec dijkstra(t(), vertex(), vertex()) :: [vertex()] | nil ``` Gets the shortest path between `a` and `b`. As indicated by the name, this uses Dijkstra's algorithm for locating the shortest path, which means that edge weights are taken into account when determining which vertices to search next. By default, all edges have a weight of 1, so vertices are inspected at random; which causes this algorithm to perform a naive depth-first search of the graph until a path is found. If your edges are weighted however, this will allow the algorithm to more intelligently navigate the graph. ## Example iex> g = Graph.new |> Graph.add_edges([{:a, :b}, {:b, :c}, {:c, :d}, {:b, :d}]) ...> Graph.dijkstra(g, :a, :d) [:a, :b, :d] iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c, :d]) ...> g = Graph.add_edges(g, [{:a, :c}, {:b, :c}, {:b, :d}]) ...> Graph.dijkstra(g, :a, :d) nil # `dijkstra` ```elixir @spec dijkstra(t(), vertex(), vertex(), keyword()) :: [vertex()] | nil ``` Like `dijkstra/3`, but accepts options for multigraph partition filtering. ## Options - `:by` - a partition key or list of partition keys to restrict edge traversal ## Example iex> g = Graph.new(multigraph: true) |> Graph.add_edges([ ...> {:a, :b, label: :fast, weight: 1}, ...> {:a, :c, label: :slow, weight: 10}, ...> {:b, :d, label: :fast, weight: 1}, ...> {:c, :d, label: :slow, weight: 1} ...> ]) ...> Graph.dijkstra(g, :a, :d, by: :fast) [:a, :b, :d] # `edge` ```elixir @spec edge(t(), vertex(), vertex()) :: Graph.Edge.t() | nil ``` Get an Edge struct for a specific vertex pair, or vertex pair + label. ## Example iex> g = Graph.new |> Graph.add_edges([{:a, :b}, {:a, :b, label: :contains}, {:a, :b, label: :uses}]) ...> Graph.edge(g, :b, :a) nil iex> g = Graph.new |> Graph.add_edges([{:a, :b}, {:a, :b, label: :contains}, {:a, :b, label: :uses}]) ...> Graph.edge(g, :a, :b) %Graph.Edge{v1: :a, v2: :b} iex> g = Graph.new |> Graph.add_edges([{:a, :b}, {:a, :b, label: :contains}, {:a, :b, label: :uses}]) ...> Graph.edge(g, :a, :b, :contains) %Graph.Edge{v1: :a, v2: :b, label: :contains} iex> g = Graph.new(type: :undirected) |> Graph.add_edges([{:a, :b}, {:a, :b, label: :contains}, {:a, :b, label: :uses}]) ...> Graph.edge(g, :a, :b, :contains) %Graph.Edge{v1: :a, v2: :b, label: :contains} # `edge` ```elixir @spec edge(t(), vertex(), vertex(), label()) :: Graph.Edge.t() | nil ``` # `edges` ```elixir @spec edges(t()) :: [Graph.Edge.t()] ``` Return a list of all the edges, where each edge is expressed as a tuple of `{A, B}`, where the elements are the vertices involved, and implying the direction of the edge to be from `A` to `B`. NOTE: You should be careful when using this on dense graphs, as it produces lists with whatever you've provided as vertices, with likely many copies of each. I'm not sure if those copies are shared in-memory as they are unchanged, so it *should* be fairly compact in memory, but I have not verified that to be sure. ## Example iex> g = Graph.new |> Graph.add_vertex(:a) |> Graph.add_vertex(:b) |> Graph.add_vertex(:c) ...> g = g |> Graph.add_edge(:a, :c) |> Graph.add_edge(:b, :c) ...> Graph.edges(g) |> Enum.sort_by(& {&1.v1, &1.v2, &1.label}) [%Graph.Edge{v1: :a, v2: :c}, %Graph.Edge{v1: :b, v2: :c}] # `edges` ```elixir @spec edges(t(), vertex() | keyword()) :: [Graph.Edge.t()] ``` Returns a list of all edges inbound or outbound from vertex `v` or by multigraph traversal options. ## Options when `multigraph: true` - `:where` - a function that accepts an edge and must return a boolean to include the edge. - `:by` - a keyword list of partitions to traverse. If not provided, all edges are traversed. ## Example iex> g = Graph.new |> Graph.add_edges([{:a, :b}, {:b, :c}]) ...> Graph.edges(g, :b) |> Enum.sort_by(& {&1.v1, &1.v2, &1.label}) [%Graph.Edge{v1: :a, v2: :b}, %Graph.Edge{v1: :b, v2: :c}] iex> g = Graph.new |> Graph.add_edges([{:a, :b}, {:b, :c}]) ...> Graph.edges(g, :d) [] iex> g = Graph.new(multigraph: true) |> Graph.add_edges([{:a, :b}, {:b, :c}]) ...> g = Graph.add_edge(g, :a, :b, label: :contains) ...> Graph.edges(g, :a, by: [:contains]) [%Graph.Edge{v1: :a, v2: :b, label: :contains}] iex> g = Graph.new(multigraph: true) |> Graph.add_edges([{:a, :b}, {:b, :c}]) ...> g = Graph.add_edge(g, :a, :b, label: :contains, weight: 2) ...> Graph.edges(g, :a, where: fn edge -> edge.weight == 2 end) [%Graph.Edge{v1: :a, v2: :b, label: :contains, weight: 2}] # `edges` ```elixir @spec edges(t(), vertex(), vertex() | keyword()) :: [Graph.Edge.t()] ``` Returns a list of all edges between `v1` and `v2` or connected to `v1` given multigraph options. ## Options when `multigraph: true` - `:where` - a function that accepts an edge and must return a boolean to include the edge. - `:by` - a single partition or list of partitions to traverse. If not provided, all edges are traversed. ## Example iex> g = Graph.new |> Graph.add_edge(:a, :b, label: :uses) ...> g = Graph.add_edge(g, :a, :b, label: :contains) ...> Graph.edges(g, :a, :b) |> Enum.sort_by(& &1.label) [%Graph.Edge{v1: :a, v2: :b, label: :contains}, %Graph.Edge{v1: :a, v2: :b, label: :uses}] iex> g = Graph.new(type: :undirected) |> Graph.add_edge(:a, :b, label: :uses) ...> g = Graph.add_edge(g, :a, :b, label: :contains) ...> Graph.edges(g, :a, :b) |> Enum.sort_by(& &1.label) [%Graph.Edge{v1: :a, v2: :b, label: :contains}, %Graph.Edge{v1: :a, v2: :b, label: :uses}] iex> g = Graph.new(multigraph: true) |> Graph.add_edges([{:a, :b}, {:b, :c}]) ...> g = Graph.add_edge(g, :a, :b, label: :contains) ...> Graph.edges(g, :a, by: :contains) [%Graph.Edge{v1: :a, v2: :b, label: :contains}] iex> g = Graph.new(multigraph: true) |> Graph.add_edges([{:a, :b}, {:b, :c}]) ...> g = Graph.add_edge(g, :a, :b, label: :contains, weight: 2) ...> Graph.edges(g, :a, by: :contains, where: fn edge -> edge.weight == 2 end) [%Graph.Edge{v1: :a, v2: :b, label: :contains, weight: 2}] # `get_paths` ```elixir @spec get_paths(t(), vertex(), vertex()) :: [[vertex()]] ``` Builds a list of paths between vertex `a` and vertex `b`. The algorithm used here is a depth-first search, which evaluates the whole graph until all paths are found. Order is guaranteed to be deterministic, but not guaranteed to be in any meaningful order (i.e. shortest to longest). ## Example iex> g = Graph.new |> Graph.add_edges([{:a, :b}, {:b, :c}, {:c, :d}, {:b, :d}, {:c, :a}]) ...> Graph.get_paths(g, :a, :d) [[:a, :b, :c, :d], [:a, :b, :d]] iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c, :d]) ...> g = Graph.add_edges(g, [{:a, :c}, {:b, :c}, {:b, :d}]) ...> Graph.get_paths(g, :a, :d) [] # `get_shortest_path` ```elixir @spec get_shortest_path(t(), vertex(), vertex()) :: [vertex()] | nil ``` See `dijkstra/3`. # `has_vertex?` ```elixir @spec has_vertex?(t(), vertex()) :: boolean() ``` Returns true if the given vertex exists in the graph. Otherwise false. ## Example iex> g = Graph.new |> Graph.add_vertices([:a, :b]) ...> Graph.has_vertex?(g, :a) true iex> g = Graph.new |> Graph.add_vertices([:a, :b]) ...> Graph.has_vertex?(g, :c) false # `in_degree` Returns the in-degree of vertex `v` of graph `g`. The *in-degree* of a vertex is the number of edges directed inbound towards that vertex. For undirected graphs, the in-degree and out-degree are always the same - the sum total of all edges inbound or outbound from the vertex. ## Example iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c]) |> Graph.add_edge(:a, :b) ...> Graph.in_degree(g, :b) 1 # `in_edges` ```elixir @spec in_edges(t(), vertex()) :: Graph.Edge.t() ``` Returns a list of `Graph.Edge` structs representing the in edges to vertex `v`. In the case of undirected graphs, it delegates to `edges/2`. ## Example iex> g = Graph.new |> Graph.add_edges([{:a, :b}, {:a, :b, label: :foo}, {:b, :c}]) ...> Graph.in_edges(g, :b) |> Enum.sort_by(& &1.label) [%Graph.Edge{v1: :a, v2: :b, label: :foo}, %Graph.Edge{v1: :a, v2: :b}] # `in_edges` ```elixir @spec in_edges(t(), vertex(), [{:by, term()}]) :: [Graph.Edge.t()] ``` Returns a list of `Graph.Edge` structs representing the in edges to vertex `v`, filtered by the given partition. Only available when `multigraph: true`. ## Example iex> g = Graph.new(multigraph: true) |> Graph.add_edges([{:a, :b, label: :foo}, {:a, :b, label: :bar}]) ...> Graph.in_edges(g, :b, by: :foo) [%Graph.Edge{v1: :a, v2: :b, label: :foo}] # `in_neighbors` ```elixir @spec in_neighbors(t(), vertex()) :: [vertex()] ``` Returns a list of vertices which all have edges coming in to the given vertex `v`. In the case of undirected graphs, it delegates to `neighbors/2`. ## Example iex> g = Graph.new |> Graph.add_edges([{:a, :b}, {:a, :b, label: :foo}, {:b, :c}]) ...> Graph.in_neighbors(g, :b) [:a] # `info` ```elixir @spec info(t()) :: graph_info() ``` Returns a map of summary information about this graph. NOTE: The `size_in_bytes` value is an estimate, not a perfectly precise value, but should be close enough to be useful. ## Example iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c, :d]) ...> g = g |> Graph.add_edges([{:a, :b}, {:b, :c}]) ...> match?(%{type: :directed, num_vertices: 4, num_edges: 2}, Graph.info(g)) true # `is_acyclic?` ```elixir @spec is_acyclic?(t()) :: boolean() ``` Returns true if and only if the graph `g` is acyclic. # `is_arborescence?` ```elixir @spec is_arborescence?(t()) :: boolean() ``` Returns true if the graph is an aborescence, a directed acyclic graph, where the *root*, a vertex, of the arborescence has a unique path from itself to every other vertex in the graph. # `is_cyclic?` ```elixir @spec is_cyclic?(t()) :: boolean() ``` Returns true if the graph `g` is not acyclic. # `is_subgraph?` ```elixir @spec is_subgraph?(t(), t()) :: boolean() ``` Returns true if graph `g1` is a subgraph of `g2`. A graph is a subgraph of another graph if it's vertices and edges are a subset of that graph's vertices and edges. ## Example iex> g1 = Graph.new |> Graph.add_vertices([:a, :b, :c, :d]) |> Graph.add_edge(:a, :b) |> Graph.add_edge(:b, :c) ...> g2 = Graph.new |> Graph.add_vertices([:b, :c]) |> Graph.add_edge(:b, :c) ...> Graph.is_subgraph?(g2, g1) true iex> g1 = Graph.new |> Graph.add_vertices([:a, :b, :c, :d]) |> Graph.add_edges([{:a, :b}, {:b, :c}]) ...> g2 = Graph.new |> Graph.add_vertices([:b, :c, :e]) |> Graph.add_edges([{:b, :c}, {:c, :e}]) ...> Graph.is_subgraph?(g2, g1) false # `is_tree?` ```elixir @spec is_tree?(t()) :: boolean() ``` Returns true if and only if the graph `g` is a tree. This function always returns false for undirected graphs. NOTE: Multiple edges between the same pair of vertices in the same direction are considered a single edge when determining if the provided graph is a tree. # `k_cliques` ```elixir @spec k_cliques(t(), non_neg_integer()) :: [[vertex()]] ``` Detects all maximal cliques of degree `k`. Returns a list of cliques, where each clique is a list of vertices in the clique. # `k_core` ```elixir @spec k_core(t(), k :: non_neg_integer()) :: t() ``` Calculates the k-core for a given graph and value of `k`. A k-core of the graph is a maximal subgraph of `g` which contains vertices of which all have a degree of at least `k`. This function returns a new `Graph` which is a subgraph of `g` containing all vertices which have a coreness >= the desired value of `k`. If there is no k-core in the graph for the provided value of `k`, an empty `Graph` is returned. If a negative integer is provided for `k`, a RuntimeError will be raised. NOTE: For performance reasons, k-core calculations make use of ETS. If you are sensitive to the number of concurrent ETS tables running in your system, you should be aware of it's usage here. 2 tables are used, and they are automatically cleaned up when this function returns. # `k_core_components` ```elixir @spec k_core_components(t()) :: %{required(k :: non_neg_integer()) => [vertex()]} ``` Groups all vertices by their k-coreness into a single map. More commonly you will want a specific k-core, in particular the degeneracy core, for which there are other functions in the API you can use. However if you have a need to determine which k-core each vertex belongs to, this function can be used to do just that. As an example, you can construct the k-core for a given graph like so: k_core_vertices = g |> Graph.k_core_components() |> Stream.filter(fn {k, _} -> k >= desired_k end) |> Enum.flat_map(fn {_, vs} -> vs end) Graph.subgraph(g, k_core_vertices) # `label_vertex` ```elixir @spec label_vertex(t(), vertex(), term()) :: t() | {:error, {:invalid_vertex, vertex()}} ``` Updates the labels for the given vertex. If no such vertex exists in the graph, `{:error, {:invalid_vertex, v}}` is returned. ## Example iex> g = Graph.new |> Graph.add_vertex(:a, :foo) ...> [:foo] = Graph.vertex_labels(g, :a) ...> g = Graph.label_vertex(g, :a, :bar) ...> Graph.vertex_labels(g, :a) [:foo, :bar] iex> g = Graph.new |> Graph.add_vertex(:a) ...> g = Graph.label_vertex(g, :a, [:foo, :bar]) ...> Graph.vertex_labels(g, :a) [:foo, :bar] # `loop_vertices` ```elixir @spec loop_vertices(t()) :: [vertex()] ``` Returns a list of vertices from graph `g` which are included in a loop, where a loop is a cycle of length 1. ## Example iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c]) |> Graph.add_edge(:a, :a) ...> Graph.loop_vertices(g) [:a] # `neighbors` ```elixir @spec neighbors(t(), vertex()) :: [vertex()] ``` Return all neighboring vertices of the given vertex. ## Example iex> g = Graph.new |> Graph.add_edges([{:a, :b}, {:b, :a}, {:b, :c}, {:c, :a}]) ...> Graph.neighbors(g, :a) [:b, :c] iex> g = Graph.new |> Graph.add_edges([{:a, :b}, {:b, :a}, {:b, :c}, {:c, :a}]) ...> Graph.neighbors(g, :d) [] # `new` Creates a new graph using the provided options. ## Options - `type: :directed | :undirected`, specifies what type of graph this is. Defaults to a `:directed` graph. - `vertex_identifier`: a function which accepts a vertex and returns a unique identifier of said vertex. Defaults to `Graph.Utils.vertex_id/1`, a hash of the whole vertex utilizing `:erlang.phash2/2`. - `multigraph: true | false`, enables edge indexing for efficient partition-based edge retrieval. - When `true`, an `edge_index` is maintained that maps partition keys to sets of edge keys. - When `false` (default), no additional memory is used for the index. - `partition_by`: a function which accepts an `%Edge{}` and returns a list of unique identifiers used as the partition keys. Defaults to `Graph.Utils.by_edge_label/1`, which partitions edges by the label when multigraphs are enabled. ### Multigraph Edge Indexing When `multigraph: true` is enabled the `partition_by` function maintains sets of edges for the partition. This option enables a space for time trade-off for Map access retrieval partitioned edges of a kind i.e. [multigraph](https://en.wikipedia.org/wiki/Multigraph) capabilities. This edge adjacency index can be useful for graphs where many different kinds of edges exist between the same vertices and iteration over all edges is prohibitive. ## Example iex> Graph.new() #Graph iex> g = Graph.new(type: :undirected) |> Graph.add_edges([{:a, :b}, {:b, :a}]) ...> Graph.edges(g) [%Graph.Edge{v1: :a, v2: :b}] iex> g = Graph.new(type: :directed) |> Graph.add_edges([{:a, :b}, {:b, :a}]) ...> Graph.edges(g) [%Graph.Edge{v1: :a, v2: :b}, %Graph.Edge{v1: :b, v2: :a}] iex> g = Graph.new(vertex_identifier: fn v -> :erlang.phash2(v) end) |> Graph.add_edges([{:a, :b}, {:b, :a}]) ...> Graph.edges(g) [%Graph.Edge{v1: :a, v2: :b}, %Graph.Edge{v1: :b, v2: :a}] iex> g = Graph.new(multigraph: true, partition_by: fn edge -> [edge.weight] end) |> Graph.add_edges([{:a, :b, weight: 1}, {:b, :a, weight: 2}]) ...> Graph.edges(g, by: 1) [%Graph.Edge{v1: :a, v2: :b, weight: 1}] # `num_edges` ```elixir @spec num_edges(t()) :: non_neg_integer() ``` Returns the number of edges in the graph. Pseudo-edges (label/weight pairs applied to an edge) are not counted, only distinct vertex pairs where an edge exists between them are counted. ## Example iex> g = Graph.add_edges(Graph.new, [{:a, :b}, {:b, :c}, {:a, :a}]) ...> Graph.num_edges(g) 3 # `num_vertices` ```elixir @spec num_vertices(t()) :: non_neg_integer() ``` Returns the number of vertices in the graph ## Example iex> g = Graph.add_vertices(Graph.new, [:a, :b, :c]) ...> Graph.num_vertices(g) 3 # `out_degree` ```elixir @spec out_degree(t(), vertex()) :: non_neg_integer() ``` Returns the out-degree of vertex `v` of graph `g`. The *out-degree* of a vertex is the number of edges directed outbound from that vertex. For undirected graphs, the in-degree and out-degree are always the same - the sum total of all edges inbound or outbound from the vertex. ## Example iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c]) |> Graph.add_edge(:a, :b) ...> Graph.out_degree(g, :a) 1 # `out_edges` ```elixir @spec out_edges(t(), vertex()) :: Graph.Edge.t() ``` Returns a list of `Graph.Edge` structs representing the out edges from vertex `v`. In the case of undirected graphs, it delegates to `edges/2`. ## Example iex> g = Graph.new |> Graph.add_edges([{:a, :b}, {:a, :b, label: :foo}, {:b, :c}]) ...> Graph.out_edges(g, :a) |> Enum.sort_by(& &1.label) [%Graph.Edge{v1: :a, v2: :b, label: :foo}, %Graph.Edge{v1: :a, v2: :b}] # `out_edges` ```elixir @spec out_edges(t(), any(), [{:by, any()}, ...]) :: list() ``` Returns a list of `Graph.Edge` structs representing the out edges from vertex `v`, filtered by multigraph options. Only available when `multigraph: true`. ## Options - `:by` - a single partition key or list of partition keys to filter edges by - `:where` - a predicate function that receives an edge and returns a boolean ## Example iex> g = Graph.new(multigraph: true) |> Graph.add_edges([{:a, :b, label: :foo}, {:a, :b, label: :bar}, {:a, :c}]) ...> Graph.out_edges(g, :a, by: :foo) [%Graph.Edge{v1: :a, v2: :b, label: :foo}] iex> g = Graph.new(multigraph: true) |> Graph.add_edges([{:a, :b, label: :foo, weight: 5}, {:a, :b, label: :bar}]) ...> Graph.out_edges(g, :a, by: :foo, where: fn e -> e.weight > 1 end) [%Graph.Edge{v1: :a, v2: :b, label: :foo, weight: 5}] # `out_neighbors` ```elixir @spec out_neighbors(t(), vertex()) :: [vertex()] ``` Returns a list of vertices which the given vertex `v` has edges going to. In the case of undirected graphs, it delegates to `neighbors/2`. ## Example iex> g = Graph.new |> Graph.add_edges([{:a, :b}, {:a, :b, label: :foo}, {:b, :c}]) ...> Graph.out_neighbors(g, :a) [:b] # `postorder` ```elixir @spec postorder(t()) :: [vertex()] ``` Returns all vertices of graph `g`. The order is given by a depth-first traversal of the graph, collecting visited vertices in postorder. More precisely, the vertices visited while searching from an arbitrarily chosen vertex are collected in postorder, and all those collected vertices are placed before the subsequently visited vertices. ## Example Our example code constructs a graph which looks like so: :a :b / :c :d / :e iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c, :d, :e]) ...> g = Graph.add_edges(g, [{:a, :b}, {:b, :c}, {:b, :d}, {:c, :e}]) ...> Graph.postorder(g) [:e, :c, :d, :b, :a] # `preorder` ```elixir @spec preorder(t()) :: [vertex()] ``` Returns all vertices of graph `g`. The order is given by a depth-first traversal of the graph, collecting visited vertices in preorder. ## Example Our example code constructs a graph which looks like so: :a :b / :c :d / :e iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c, :d, :e]) ...> g = Graph.add_edges(g, [{:a, :b}, {:b, :c}, {:b, :d}, {:c, :e}]) ...> Graph.preorder(g) [:a, :b, :c, :e, :d] # `reachable` ```elixir @spec reachable(t(), [vertex()]) :: [[vertex()]] ``` Returns an unsorted list of vertices from the graph, such that for each vertex in the list (call it `v`), there is a path in the graph from some vertex of `vs` to `v`. As paths of length zero are allowed, the vertices of `vs` are also included in the returned list. ## Example iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c, :d]) ...> g = Graph.add_edges(g, [{:a, :b}, {:a, :c}, {:b, :c}, {:c, :d}]) ...> Graph.reachable(g, [:a]) [:d, :c, :b, :a] # `reachable_neighbors` ```elixir @spec reachable_neighbors(t(), [vertex()]) :: [[vertex()]] ``` Returns an unsorted list of vertices from the graph, such that for each vertex in the list (call it `v`), there is a path in the graph of length one or more from some vertex of `vs` to `v`. As a consequence, only those vertices of `vs` that are included in some cycle are returned. ## Example iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c, :d]) ...> g = Graph.add_edges(g, [{:a, :b}, {:a, :c}, {:b, :c}, {:c, :d}]) ...> Graph.reachable_neighbors(g, [:a]) [:d, :c, :b] # `reaching` ```elixir @spec reaching(t(), [vertex()]) :: [[vertex()]] ``` Returns an unsorted list of vertices from the graph, such that for each vertex in the list (call it `v`), there is a path from `v` to some vertex of `vs`. As paths of length zero are allowed, the vertices of `vs` are also included in the returned list. ## Example iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c, :d]) ...> g = Graph.add_edges(g, [{:a, :b}, {:a, :c}, {:b, :c}, {:c, :d}]) ...> Graph.reaching(g, [:d]) [:b, :a, :c, :d] # `reaching_neighbors` ```elixir @spec reaching_neighbors(t(), [vertex()]) :: [[vertex()]] ``` Returns an unsorted list of vertices from the graph, such that for each vertex in the list (call it `v`), there is a path of length one or more from `v` to some vertex of `vs`. As a consequence, only those vertices of `vs` that are included in some cycle are returned. ## Example iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c, :d]) ...> g = Graph.add_edges(g, [{:a, :b}, {:a, :c}, {:b, :c}, {:c, :a}, {:b, :d}]) ...> Graph.reaching_neighbors(g, [:b]) [:b, :c, :a] # `remove_vertex_labels` ```elixir @spec remove_vertex_labels(t(), vertex()) :: t() | {:error, {:invalid_vertex, vertex()}} ``` iex> graph = Graph.new |> Graph.add_vertex(:a, [:foo, :bar]) ...> [:foo, :bar] = Graph.vertex_labels(graph, :a) ...> graph = Graph.remove_vertex_labels(graph, :a) ...> Graph.vertex_labels(graph, :a) [] iex> graph = Graph.new |> Graph.add_vertex(:a, [:foo, :bar]) ...> [:foo, :bar] = Graph.vertex_labels(graph, :a) ...> Graph.remove_vertex_labels(graph, :b) {:error, {:invalid_vertex, :b}} # `replace_vertex` ```elixir @spec replace_vertex(t(), vertex(), vertex()) :: t() | {:error, :no_such_vertex} ``` Replaces `vertex` with `new_vertex` in the graph. ## Example iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c, :d]) ...> g = Graph.add_edges(g, [{:a, :b}, {:b, :c}, {:c, :a}, {:c, :d}]) ...> Graph.vertices(g) |> Enum.sort() [:a, :b, :c, :d] iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c, :d]) ...> g = Graph.add_edges(g, [{:a, :b}, {:b, :c}, {:c, :a}, {:c, :d}]) ...> g = Graph.replace_vertex(g, :a, :e) ...> Graph.vertices(g) |> Enum.sort() [:b, :c, :d, :e] iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c, :d]) ...> g = Graph.add_edges(g, [{:a, :b}, {:b, :c}, {:c, :a}, {:c, :d}]) ...> g = Graph.replace_vertex(g, :a, :e) ...> Graph.edges(g) |> Enum.sort_by(& {&1.v1, &1.v2, &1.label}) [%Graph.Edge{v1: :b, v2: :c}, %Graph.Edge{v1: :c, v2: :d}, %Graph.Edge{v1: :c, v2: :e}, %Graph.Edge{v1: :e, v2: :b}] # `split_edge` ```elixir @spec split_edge(t(), vertex(), vertex(), vertex()) :: t() | {:error, :no_such_edge} ``` Splits the edges between `v1` and `v2` by inserting a new vertex, `v3`, deleting the edges between `v1` and `v2`, and inserting new edges from `v1` to `v3` and from `v3` to `v2`. The resulting edges from the split will share the same weight and label as the old edges. ## Example iex> g = Graph.new |> Graph.add_vertices([:a, :c]) |> Graph.add_edge(:a, :c, weight: 2) ...> g = Graph.split_edge(g, :a, :c, :b) ...> Graph.edges(g) [%Graph.Edge{v1: :a, v2: :b, weight: 2}, %Graph.Edge{v1: :b, v2: :c, weight: 2}] iex> g = Graph.new(type: :undirected) |> Graph.add_vertices([:a, :c]) |> Graph.add_edge(:a, :c, weight: 2) ...> g = Graph.split_edge(g, :a, :c, :b) ...> Graph.edges(g) [%Graph.Edge{v1: :a, v2: :b, weight: 2}, %Graph.Edge{v1: :b, v2: :c, weight: 2}] # `strong_components` ```elixir @spec strong_components(t()) :: [[vertex()]] ``` Returns a list of strongly connected components, where each component is a list of vertices. A *strongly connected component* is a maximal subgraph such that there is a path between each pair of vertices. See `components/1` for the definitions of *subgraph* and *maximal subgraph* if you are unfamiliar with the terminology. ## Example iex> g = Graph.new |> Graph.add_vertices([:a, :b, :c, :d]) ...> g = Graph.add_edges(g, [{:a, :b}, {:a, :c}, {:b, :c}, {:c, :d}, {:c, :a}]) ...> Graph.strong_components(g) [[:d], [:b, :c, :a]] # `subgraph` ```elixir @spec subgraph(t(), [vertex()]) :: t() ``` Builds a maximal subgraph of `g` which includes all of the vertices in `vs` and the edges which connect them. See the test suite for example usage. # `to_dot` ```elixir @spec to_dot(t()) :: {:ok, binary()} | {:error, term()} ``` Converts the given Graph to DOT format, which can then be converted to a number of other formats via Graphviz, e.g. `dot -Tpng out.dot > out.png`. If labels are set on a vertex, then those labels are used in the DOT output in place of the vertex itself. If no labels were set, then the vertex is stringified if it's a primitive type and inspected if it's not, in which case the inspect output will be quoted and used as the vertex label in the DOT file. Edge labels and weights will be shown as attributes on the edge definitions, otherwise they use the same labelling scheme for the involved vertices as described above. NOTE: Currently this function assumes graphs are directed graphs, but in the future it will support undirected graphs as well. NOTE: Currently this function assumes graphs are directed graphs, but in the future it will support undirected graphs as well. NOTE 2: To avoid to overwrite vertices with the same label, output is generated using the internal numeric ID as vertex label. Original label is expressed as `id[label="