Algebra.Graph.Relation

algebraic-graphs-0.7: A library for algebraic graph construction and transformation

Copyright | (c) Andrey Mokhov 2016-2022
License | MIT (see the file LICENSE)
Maintainer | andrey.mokhov@gmail.com
Stability | experimental
Safe Haskell | None
Language | Haskell2010

Algebra.Graph.Relation

Contents

- Data structure
- Basic graph construction primitives
- Relations on graphs
- Graph properties
- Standard families of graphs
- Graph transformation
- Relational operations
- Miscellaneous

Description

Alga is a library for algebraic construction and manipulation of graphs in Haskell. See this paper for the motivation behind the library, the underlying theory, and implementation details.

This module defines the `Relation` data type, as well as associated operations and algorithms. `Relation` is an instance of the `Graph` type class, which can be used for polymorphic graph construction and manipulation.

Synopsis

- data Relation a
- domain :: Relation a -> Set a
- relation :: Relation a -> Set (a, a)
- empty :: Relation a
- vertex :: a -> Relation a
- edge :: Ord a => a -> a -> Relation a
- overlay :: Ord a => Relation a -> Relation a -> Relation a
- connect :: Ord a => Relation a -> Relation a -> Relation a
- vertices :: Ord a => [a] -> Relation a
- edges :: Ord a => [(a, a)] -> Relation a
- overlays :: Ord a => [Relation a] -> Relation a
- connects :: Ord a => [Relation a] -> Relation a
- isSubgraphOf :: Ord a => Relation a -> Relation a -> Bool
- isEmpty :: Relation a -> Bool
- hasVertex :: Ord a => a -> Relation a -> Bool
- hasEdge :: Ord a => a -> a -> Relation a -> Bool
- vertexCount :: Relation a -> Int
- edgeCount :: Relation a -> Int
- vertexList :: Relation a -> [a]
- edgeList :: Relation a -> [(a, a)]
- adjacencyList :: Eq a => Relation a -> [(a, [a])]
- vertexSet :: Relation a -> Set a
- edgeSet :: Relation a -> Set (a, a)
- preSet :: Ord a => a -> Relation a -> Set a
- postSet :: Ord a => a -> Relation a -> Set a
- path :: Ord a => [a] -> Relation a
- circuit :: Ord a => [a] -> Relation a
- clique :: Ord a => [a] -> Relation a
- biclique :: Ord a => [a] -> [a] -> Relation a
- star :: Ord a => a -> [a] -> Relation a
- stars :: Ord a => [(a, [a])] -> Relation a
- tree :: Ord a => Tree a -> Relation a
- forest :: Ord a => Forest a -> Relation a
- removeVertex :: Ord a => a -> Relation a -> Relation a
- removeEdge :: Ord a => a -> a -> Relation a -> Relation a
- replaceVertex :: Ord a => a -> a -> Relation a -> Relation a
- mergeVertices :: Ord a => (a -> Bool) -> a -> Relation a -> Relation a
- transpose :: Ord a => Relation a -> Relation a
- gmap :: Ord b => (a -> b) -> Relation a -> Relation b
- induce :: (a -> Bool) -> Relation a -> Relation a
- induceJust :: Ord a => Relation (Maybe a) -> Relation a
- compose :: Ord a => Relation a -> Relation a -> Relation a
- closure :: Ord a => Relation a -> Relation a
- reflexiveClosure :: Ord a => Relation a -> Relation a
- symmetricClosure :: Ord a => Relation a -> Relation a
- transitiveClosure :: Ord a => Relation a -> Relation a
- consistent :: Ord a => Relation a -> Bool

# Data structure

data Relation a #

The `Relation` data type represents a graph as a binary relation. We define a `Num` instance as a convenient notation for working with graphs:

    0           == vertex 0
    1 + 2       == overlay (vertex 1) (vertex 2)
    1 * 2       == connect (vertex 1) (vertex 2)
    1 + 2 * 3   == overlay (vertex 1) (connect (vertex 2) (vertex 3))
    1 * (2 + 3) == connect (vertex 1) (overlay (vertex 2) (vertex 3))

Note: the `Num` instance does not satisfy several "customary laws" of `Num`, which dictate that `fromInteger` `0` and `fromInteger` `1` should act as additive and multiplicative identities, and `negate` as additive inverse. Nevertheless, overloading `fromInteger`, `+` and `*` is very convenient when working with algebraic graphs; we hope that in future Haskell's Prelude will provide a more fine-grained class hierarchy for algebraic structures, which we would be able to utilise without violating any laws.

The `Show` instance is defined using basic graph construction primitives:

    show (empty     :: Relation Int) == "empty"
    show (1         :: Relation Int) == "vertex 1"
    show (1 + 2     :: Relation Int) == "vertices [1,2]"
    show (1 * 2     :: Relation Int) == "edge 1 2"
    show (1 * 2 * 3 :: Relation Int) == "edges [(1,2),(1,3),(2,3)]"
    show (1 * 2 + 3 :: Relation Int) == "overlay (vertex 3) (edge 1 2)"

The `Eq` instance satisfies all axioms of algebraic graphs:

- `overlay` is commutative and associative:

            x + y == y + x
      x + (y + z) == (x + y) + z

- `connect` is associative and has `empty` as the identity:

        x * empty == x
        empty * x == x
      x * (y * z) == (x * y) * z

- `connect` distributes over `overlay`:

      x * (y + z) == x * y + x * z
      (x + y) * z == x * z + y * z

- `connect` can be decomposed:

      x * y * z == x * y + x * z + y * z

The following useful theorems can be proved from the above set of axioms.

- `overlay` has `empty` as the identity and is idempotent:

        x + empty == x
        empty + x == x
            x + x == x

- Absorption and saturation of `connect`:

      x * y + x + y == x * y
          x * x * x == x * x

When specifying the time and memory complexity of graph algorithms, n and m will denote the number of vertices and edges in the graph, respectively.

The total order on graphs is defined using size-lexicographic comparison:

- Compare the number of vertices. In case of a tie, continue.
- Compare the sets of vertices. In case of a tie, continue.
- Compare the number of edges. In case of a tie, continue.
- Compare the sets of edges.

Here are a few examples:

    vertex 1 < vertex 2
    vertex 3 < edge 1 2
    vertex 1 < edge 1 1
    edge 1 1 < edge 1 2
    edge 1 2 < edge 1 1 + edge 2 2
    edge 1 2 < edge 1 3

Note that the resulting order refines the `isSubgraphOf` relation and is compatible with `overlay` and `connect` operations:

    isSubgraphOf x y ==> x <= y

    empty <= x
    x     <= x + y
    x + y <= x * y

#### Instances

Instances details

Ord a => Graph (Relation a) # |
Instance details Defined in Algebra.Graph.Class Associated Types
type Vertex (Relation a) |
Instance details Defined in Algebra.Graph.Class type Vertex (Relation a) = a
Methods empty :: Relation a # vertex :: Vertex (Relation a) -> Relation a # overlay :: Relation a -> Relation a -> Relation a # connect :: Relation a -> Relation a -> Relation a #
Ord a => ToGraph (Relation a) # |
Instance details Defined in Algebra.Graph.Relation Associated Types
type ToVertex (Relation a) |
Instance details Defined in Algebra.Graph.Relation type ToVertex (Relation a) = a
Methods toGraph :: Relation a -> Graph (ToVertex (Relation a)) # foldg :: r -> (ToVertex (Relation a) -> r) -> (r -> r -> r) -> (r -> r -> r) -> Relation a -> r # isEmpty :: Relation a -> Bool # hasVertex :: ToVertex (Relation a) -> Relation a -> Bool # hasEdge :: ToVertex (Relation a) -> ToVertex (Relation a) -> Relation a -> Bool # vertexCount :: Relation a -> Int # edgeCount :: Relation a -> Int # vertexList :: Relation a -> [ToVertex (Relation a)] # edgeList :: Relation a -> [(ToVertex (Relation a), ToVertex (Relation a))] # vertexSet :: Relation a -> Set (ToVertex (Relation a)) # vertexIntSet :: Relation a -> IntSet # edgeSet :: Relation a -> Set (ToVertex (Relation a), ToVertex (Relation a)) # preSet :: ToVertex (Relation a) -> Relation a -> Set (ToVertex (Relation a)) # preIntSet :: Int -> Relation a -> IntSet # postSet :: ToVertex (Relation a) -> Relation a -> Set (ToVertex (Relation a)) # postIntSet :: Int -> Relation a -> IntSet # adjacencyList :: Relation a -> [(ToVertex (Relation a), [ToVertex (Relation a)])] # dfsForest :: Relation a -> Forest (ToVertex (Relation a)) # dfsForestFrom :: Relation a -> [ToVertex (Relation a)] -> Forest (ToVertex (Relation a)) # dfs :: Relation a -> [ToVertex (Relation a)] -> [ToVertex (Relation a)] # reachable :: Relation a -> ToVertex (Relation a) -> [ToVertex (Relation a)] # topSort :: Relation a -> Either (Cycle (ToVertex (Relation a))) [ToVertex (Relation a)] # isAcyclic :: Relation a -> Bool # toAdjacencyMap :: Relation a -> AdjacencyMap (ToVertex (Relation a)) # toAdjacencyMapTranspose :: Relation a -> AdjacencyMap (ToVertex (Relation a)) # toAdjacencyIntMap :: Relation a -> AdjacencyIntMap # toAdjacencyIntMapTranspose :: Relation a -> AdjacencyIntMap # isDfsForestOf :: Forest (ToVertex (Relation a)) -> Relation a -> Bool # isTopSortOf :: [ToVertex (Relation a)] -> Relation a -> Bool #
NFData a => NFData (Relation a) # |
Instance details Defined in Algebra.Graph.Relation Methods rnf :: Relation a -> () #
Ord a => Monoid (Relation a) # | Defined via `overlay` and `empty`.
Instance details Defined in Algebra.Graph.Relation Methods mempty :: Relation a # mappend :: Relation a -> Relation a -> Relation a # mconcat :: [Relation a] -> Relation a #
Ord a => Semigroup (Relation a) # | Defined via `overlay`.
Instance details Defined in Algebra.Graph.Relation Methods (<>) :: Relation a -> Relation a -> Relation a # sconcat :: NonEmpty (Relation a) -> Relation a # stimes :: Integral b => b -> Relation a -> Relation a #
IsString a => IsString (Relation a) # |
Instance details Defined in Algebra.Graph.Relation Methods fromString :: String -> Relation a #
(Ord a, Num a) => Num (Relation a) # | Note: this does not satisfy the usual ring laws; see `Relation` for more details.
Instance details Defined in Algebra.Graph.Relation Methods (+) :: Relation a -> Relation a -> Relation a # (-) :: Relation a -> Relation a -> Relation a # (*) :: Relation a -> Relation a -> Relation a # negate :: Relation a -> Relation a # abs :: Relation a -> Relation a # signum :: Relation a -> Relation a # fromInteger :: Integer -> Relation a #
(Ord a, Show a) => Show (Relation a) # |
Instance details Defined in Algebra.Graph.Relation Methods showsPrec :: Int -> Relation a -> ShowS # show :: Relation a -> String # showList :: [Relation a] -> ShowS #
Eq a => Eq (Relation a) # |
Instance details Defined in Algebra.Graph.Relation Methods (==) :: Relation a -> Relation a -> Bool # (/=) :: Relation a -> Relation a -> Bool #
Ord a => Ord (Relation a) # |
Instance details Defined in Algebra.Graph.Relation Methods compare :: Relation a -> Relation a -> Ordering # (<) :: Relation a -> Relation a -> Bool # (<=) :: Relation a -> Relation a -> Bool # (>) :: Relation a -> Relation a -> Bool # (>=) :: Relation a -> Relation a -> Bool # max :: Relation a -> Relation a -> Relation a # min :: Relation a -> Relation a -> Relation a #
type Vertex (Relation a) # |
Instance details Defined in Algebra.Graph.Class type Vertex (Relation a) = a
type ToVertex (Relation a) # |
Instance details Defined in Algebra.Graph.Relation type ToVertex (Relation a) = a

domain :: Relation a -> Set a #

The domain of the relation. Complexity: O(1) time and memory.

relation :: Relation a -> Set (a, a) #

The set of pairs of elements that are related. It is guaranteed that each element belongs to the domain. Complexity: O(1) time and memory.

# Basic graph construction primitives

empty :: Relation a #

Construct the empty graph.

    isEmpty     empty == True
    hasVertex x empty == False
    vertexCount empty == 0
    edgeCount   empty == 0

vertex :: a -> Relation a #

Construct the graph comprising a single isolated vertex.

    isEmpty     (vertex x) == False
    hasVertex x (vertex y) == (x == y)
    vertexCount (vertex x) == 1
    edgeCount   (vertex x) == 0

edge :: Ord a => a -> a -> Relation a #

Construct the graph comprising a single edge.

    edge x y               == connect (vertex x) (vertex y)
    hasEdge x y (edge x y) == True
    edgeCount   (edge x y) == 1
    vertexCount (edge 1 1) == 1
    vertexCount (edge 1 2) == 2

overlay :: Ord a => Relation a -> Relation a -> Relation a #

Overlay two graphs. This is a commutative, associative and idempotent operation with the identity `empty`. Complexity: O((n + m) * log(n)) time and O(n + m) memory.

    isEmpty     (overlay x y) == isEmpty   x   && isEmpty   y
    hasVertex z (overlay x y) == hasVertex z x || hasVertex z y
    vertexCount (overlay x y) >= vertexCount x
    vertexCount (overlay x y) <= vertexCount x + vertexCount y
    edgeCount   (overlay x y) >= edgeCount x
    edgeCount   (overlay x y) <= edgeCount x   + edgeCount y
    vertexCount (overlay 1 2) == 2
    edgeCount   (overlay 1 2) == 0

connect :: Ord a => Relation a -> Relation a -> Relation a #

Connect two graphs. This is an associative operation with the identity `empty`, which distributes over `overlay` and obeys the decomposition axiom. Complexity: O((n + m) * log(n)) time and O(n + m) memory. Note that the number of edges in the resulting graph is quadratic with respect to the number of vertices of the arguments: m = O(m1 + m2 + n1 * n2).

    isEmpty     (connect x y) == isEmpty   x   && isEmpty   y
    hasVertex z (connect x y) == hasVertex z x || hasVertex z y
    vertexCount (connect x y) >= vertexCount x
    vertexCount (connect x y) <= vertexCount x + vertexCount y
    edgeCount   (connect x y) >= edgeCount x
    edgeCount   (connect x y) >= edgeCount y
    edgeCount   (connect x y) >= vertexCount x * vertexCount y
    edgeCount   (connect x y) <= vertexCount x * vertexCount y + edgeCount x + edgeCount y
    vertexCount (connect 1 2) == 2
    edgeCount   (connect 1 2) == 1

vertices :: Ord a => [a] -> Relation a #

Construct the graph comprising a given list of isolated vertices. Complexity: O(L * log(L)) time and O(L) memory, where L is the length of the given list.

    vertices []            == empty
    vertices [x]           == vertex x
    vertices               == overlays . map vertex
    hasVertex x . vertices == elem x
    vertexCount . vertices == length . nub
    vertexSet   . vertices == Set.fromList

edges :: Ord a => [(a, a)] -> Relation a #

Construct the graph from a list of edges. Complexity: O((n + m) * log(n)) time and O(n + m) memory.

    edges []          == empty
    edges [(x,y)]     == edge x y
    edges             == overlays . map (uncurry edge)
    edgeCount . edges == length . nub

overlays :: Ord a => [Relation a] -> Relation a #

Overlay a given list of graphs. Complexity: O((n + m) * log(n)) time and O(n + m) memory.

    overlays []        == empty
    overlays [x]       == x
    overlays [x,y]     == overlay x y
    overlays           == foldr overlay empty
    isEmpty . overlays == all isEmpty

connects :: Ord a => [Relation a] -> Relation a #

Connect a given list of graphs. Complexity: O((n + m) * log(n)) time and O(n + m) memory.

    connects []        == empty
    connects [x]       == x
    connects [x,y]     == connect x y
    connects           == foldr connect empty
    isEmpty . connects == all isEmpty

# Relations on graphs

isSubgraphOf :: Ord a => Relation a -> Relation a -> Bool #

The `isSubgraphOf` function takes two graphs and returns `True` if the first graph is a subgraph of the second. Complexity: O((n + m) * log(n)) time.

    isSubgraphOf empty         x             ==  True
    isSubgraphOf (vertex x)    empty         ==  False
    isSubgraphOf x             (overlay x y) ==  True
    isSubgraphOf (overlay x y) (connect x y) ==  True
    isSubgraphOf (path xs)     (circuit xs)  ==  True
    isSubgraphOf x y                         ==> x <= y

# Graph properties

isEmpty :: Relation a -> Bool #

Check if a relation is empty. Complexity: O(1) time.

    isEmpty empty                       == True
    isEmpty (overlay empty empty)       == True
    isEmpty (vertex x)                  == False
    isEmpty (removeVertex x $ vertex x) == True
    isEmpty (removeEdge x y $ edge x y) == False

hasVertex :: Ord a => a -> Relation a -> Bool #

Check if a graph contains a given vertex. Complexity: O(log(n)) time.

    hasVertex x empty            == False
    hasVertex x (vertex y)       == (x == y)
    hasVertex x . removeVertex x == const False

hasEdge :: Ord a => a -> a -> Relation a -> Bool #

Check if a graph contains a given edge. Complexity: O(log(n)) time.

    hasEdge x y empty            == False
    hasEdge x y (vertex z)       == False
    hasEdge x y (edge x y)       == True
    hasEdge x y . removeEdge x y == const False
    hasEdge x y                  == elem (x,y) . edgeList

vertexCount :: Relation a -> Int #

The number of vertices in a graph. Complexity: O(1) time.

    vertexCount empty             ==  0
    vertexCount (vertex x)        ==  1
    vertexCount                   ==  length . vertexList
    vertexCount x < vertexCount y ==> x < y

edgeCount :: Relation a -> Int #

The number of edges in a graph. Complexity: O(1) time.

    edgeCount empty      == 0
    edgeCount (vertex x) == 0
    edgeCount (edge x y) == 1
    edgeCount            == length . edgeList

vertexList :: Relation a -> [a] #

The sorted list of vertices of a given graph. Complexity: O(n) time and memory.

    vertexList empty      == []
    vertexList (vertex x) == [x]
    vertexList . vertices == nub . sort

edgeList :: Relation a -> [(a, a)] #

The sorted list of edges of a graph. Complexity: O(n + m) time and O(m) memory.

    edgeList empty          == []
    edgeList (vertex x)     == []
    edgeList (edge x y)     == [(x,y)]
    edgeList (star 2 [3,1]) == [(2,1), (2,3)]
    edgeList . edges        == nub . sort
    edgeList . transpose    == sort . map swap . edgeList

adjacencyList :: Eq a => Relation a -> [(a, [a])] #

The sorted adjacency list of a graph. Complexity: O(n + m) time and memory.

    adjacencyList empty          == []
    adjacencyList (vertex x)     == [(x, [])]
    adjacencyList (edge 1 2)     == [(1, [2]), (2, [])]
    adjacencyList (star 2 [3,1]) == [(1, []), (2, [1,3]), (3, [])]
    stars . adjacencyList        == id

vertexSet :: Relation a -> Set a #

The set of vertices of a given graph. Complexity: O(1) time.

    vertexSet empty      == Set.empty
    vertexSet . vertex   == Set.singleton
    vertexSet . vertices == Set.fromList

edgeSet :: Relation a -> Set (a, a) #

The set of edges of a given graph. Complexity: O(1) time.

    edgeSet empty      == Set.empty
    edgeSet (vertex x) == Set.empty
    edgeSet (edge x y) == Set.singleton (x,y)
    edgeSet . edges    == Set.fromList

preSet :: Ord a => a -> Relation a -> Set a #

The preset of an element `x` is the set of elements that are related to it on the left, i.e. `preSet x == { a | aRx }`. In the context of directed graphs, this corresponds to the set of direct predecessors of vertex `x`. Complexity: O(n + m) time and O(n) memory.

    preSet x empty      == Set.empty
    preSet x (vertex x) == Set.empty
    preSet 1 (edge 1 2) == Set.empty
    preSet y (edge x y) == Set.fromList [x]

postSet :: Ord a => a -> Relation a -> Set a #

The postset of an element `x` is the set of elements that are related to it on the right, i.e. `postSet x == { a | xRa }`. In the context of directed graphs, this corresponds to the set of direct successors of vertex `x`. Complexity: O(n + m) time and O(n) memory.

    postSet x empty      == Set.empty
    postSet x (vertex x) == Set.empty
    postSet x (edge x y) == Set.fromList [y]
    postSet 2 (edge 1 2) == Set.empty

# Standard families of graphs

path :: Ord a => [a] -> Relation a #

The path on a list of vertices. Complexity: O((n + m) * log(n)) time and O(n + m) memory.

    path []        == empty
    path [x]       == vertex x
    path [x,y]     == edge x y
    path . reverse == transpose . path

circuit :: Ord a => [a] -> Relation a #

The circuit on a list of vertices. Complexity: O((n + m) * log(n)) time and O(n + m) memory.

    circuit []        == empty
    circuit [x]       == edge x x
    circuit [x,y]     == edges [(x,y), (y,x)]
    circuit . reverse == transpose . circuit

clique :: Ord a => [a] -> Relation a #

The clique on a list of vertices. Complexity: O((n + m) * log(n)) time and O(n + m) memory.

    clique []         == empty
    clique [x]        == vertex x
    clique [x,y]      == edge x y
    clique [x,y,z]    == edges [(x,y), (x,z), (y,z)]
    clique (xs ++ ys) == connect (clique xs) (clique ys)
    clique . reverse  == transpose . clique

biclique :: Ord a => [a] -> [a] -> Relation a #

The biclique on two lists of vertices. Complexity: O(n * log(n) + m) time and O(n + m) memory.

    biclique []      []      == empty
    biclique [x]     []      == vertex x
    biclique []      [y]     == vertex y
    biclique [x1,x2] [y1,y2] == edges [(x1,y1), (x1,y2), (x2,y1), (x2,y2)]
    biclique xs      ys      == connect (vertices xs) (vertices ys)

star :: Ord a => a -> [a] -> Relation a #

The star formed by a centre vertex connected to a list of leaves. Complexity: O((n + m) * log(n)) time and O(n + m) memory.

    star x []    == vertex x
    star x [y]   == edge x y
    star x [y,z] == edges [(x,y), (x,z)]
    star x ys    == connect (vertex x) (vertices ys)

stars :: Ord a => [(a, [a])] -> Relation a #

The stars formed by overlaying a list of `star`s. An inverse of `adjacencyList`. Complexity: O(L * log(n)) time, memory and size, where L is the total size of the input.

    stars []                      == empty
    stars [(x, [])]               == vertex x
    stars [(x, [y])]              == edge x y
    stars [(x, ys)]               == star x ys
    stars                         == overlays . map (uncurry star)
    stars . adjacencyList         == id
    overlay (stars xs) (stars ys) == stars (xs ++ ys)

tree :: Ord a => Tree a -> Relation a #

The tree graph constructed from a given `Tree` data structure. Complexity: O((n + m) * log(n)) time and O(n + m) memory.

    tree (Node x [])                                         == vertex x
    tree (Node x [Node y [Node z []]])                       == path [x,y,z]
    tree (Node x [Node y [], Node z []])                     == star x [y,z]
    tree (Node 1 [Node 2 [], Node 3 [Node 4 [], Node 5 []]]) == edges [(1,2), (1,3), (3,4), (3,5)]

forest :: Ord a => Forest a -> Relation a #

The forest graph constructed from a given `Forest` data structure. Complexity: O((n + m) * log(n)) time and O(n + m) memory.

    forest []                                                  == empty
    forest [x]                                                 == tree x
    forest [Node 1 [Node 2 [], Node 3 []], Node 4 [Node 5 []]] == edges [(1,2), (1,3), (4,5)]
    forest                                                     == overlays . map tree

# Graph transformation

removeVertex :: Ord a => a -> Relation a -> Relation a #

Remove a vertex from a given graph. Complexity: O(n + m) time.

    removeVertex x (vertex x)       == empty
    removeVertex 1 (vertex 2)       == vertex 2
    removeVertex x (edge x x)       == empty
    removeVertex 1 (edge 1 2)       == vertex 2
    removeVertex x . removeVertex x == removeVertex x

removeEdge :: Ord a => a -> a -> Relation a -> Relation a #

Remove an edge from a given graph. Complexity: O(log(m)) time.

    removeEdge x y (edge x y)       == vertices [x,y]
    removeEdge x y . removeEdge x y == removeEdge x y
    removeEdge x y . removeVertex x == removeVertex x
    removeEdge 1 1 (1 * 1 * 2 * 2)  == 1 * 2 * 2
    removeEdge 1 2 (1 * 1 * 2 * 2)  == 1 * 1 + 2 * 2

replaceVertex :: Ord a => a -> a -> Relation a -> Relation a #

The function ``replaceVertex` x y` replaces vertex `x` with vertex `y` in a given `AdjacencyMap`. If `y` already exists, `x` and `y` will be merged. Complexity: O((n + m) * log(n)) time.

    replaceVertex x x            == id
    replaceVertex x y (vertex x) == vertex y
    replaceVertex x y            == mergeVertices (== x) y

mergeVertices :: Ord a => (a -> Bool) -> a -> Relation a -> Relation a #

Merge vertices satisfying a given predicate into a given vertex. Complexity: O((n + m) * log(n)) time, assuming that the predicate takes constant time.

    mergeVertices (const False) x    == id
    mergeVertices (== x) y           == replaceVertex x y
    mergeVertices even 1 (0 * 2)     == 1 * 1
    mergeVertices odd  1 (3 + 4 * 5) == 4 * 1

transpose :: Ord a => Relation a -> Relation a #

Transpose a given graph. Complexity: O(m * log(m)) time.

    transpose empty       == empty
    transpose (vertex x)  == vertex x
    transpose (edge x y)  == edge y x
    transpose . transpose == id
    edgeList . transpose  == sort . map swap . edgeList

gmap :: Ord b => (a -> b) -> Relation a -> Relation b #

Transform a graph by applying a function to each of its vertices. This is similar to `Functor`'s `fmap` but can be used with non-fully-parametric `Relation`. Complexity: O((n + m) * log(n)) time.

    gmap f empty      == empty
    gmap f (vertex x) == vertex (f x)
    gmap f (edge x y) == edge (f x) (f y)
    gmap id           == id
    gmap f . gmap g   == gmap (f . g)

induce :: (a -> Bool) -> Relation a -> Relation a #

Construct the induced subgraph of a given graph by removing the vertices that do not satisfy a given predicate. Complexity: O(n + m) time, assuming that the predicate takes constant time.

    induce (const True ) x      == x
    induce (const False) x      == empty
    induce (/= x)               == removeVertex x
    induce p . induce q         == induce (\x -> p x && q x)
    isSubgraphOf (induce p x) x == True

induceJust :: Ord a => Relation (Maybe a) -> Relation a #

Construct the induced subgraph of a given graph by removing the vertices that are `Nothing`. Complexity: O(n + m) time.

    induceJust (vertex Nothing)                               == empty
    induceJust (edge (Just x) Nothing)                        == vertex x
    induceJust . gmap Just                                    == id
    induceJust . gmap (\x -> if p x then Just x else Nothing) == induce p

# Relational operations

compose :: Ord a => Relation a -> Relation a -> Relation a #

Left-to-right relational composition of graphs: vertices `x` and `z` are connected in the resulting graph if there is a vertex `y`, such that `x` is connected to `y` in the first graph, and `y` is connected to `z` in the second graph. There are no isolated vertices in the result. This operation is associative, has `empty` and single-`vertex` graphs as annihilating zeroes, and distributes over `overlay`. Complexity: O(n * m * log(m)) time and O(n + m) memory.

    compose empty            x                == empty
    compose x                empty            == empty
    compose (vertex x)       y                == empty
    compose x                (vertex y)       == empty
    compose x                (compose y z)    == compose (compose x y) z
    compose x                (overlay y z)    == overlay (compose x y) (compose x z)
    compose (overlay x y)    z                == overlay (compose x z) (compose y z)
    compose (edge x y)       (edge y z)       == edge x z
    compose (path    [1..5]) (path    [1..5]) == edges [(1,3), (2,4), (3,5)]
    compose (circuit [1..5]) (circuit [1..5]) == circuit [1,3,5,2,4]

closure :: Ord a => Relation a -> Relation a #

Compute the reflexive and transitive closure of a graph. Complexity: O(n * m * log(n) * log(m)) time.

    closure empty           == empty
    closure (vertex x)      == edge x x
    closure (edge x x)      == edge x x
    closure (edge x y)      == edges [(x,x), (x,y), (y,y)]
    closure (path $ nub xs) == reflexiveClosure (clique $ nub xs)
    closure                 == reflexiveClosure . transitiveClosure
    closure                 == transitiveClosure . reflexiveClosure
    closure . closure       == closure
    postSet x (closure y)   == Set.fromList (reachable y x)

reflexiveClosure :: Ord a => Relation a -> Relation a #

Compute the reflexive closure of a graph. Complexity: O(n * log(m)) time.

    reflexiveClosure empty              == empty
    reflexiveClosure (vertex x)         == edge x x
    reflexiveClosure (edge x x)         == edge x x
    reflexiveClosure (edge x y)         == edges [(x,x), (x,y), (y,y)]
    reflexiveClosure . reflexiveClosure == reflexiveClosure

symmetricClosure :: Ord a => Relation a -> Relation a #

Compute the symmetric closure of a graph. Complexity: O(m * log(m)) time.

    symmetricClosure empty              == empty
    symmetricClosure (vertex x)         == vertex x
    symmetricClosure (edge x y)         == edges [(x,y), (y,x)]
    symmetricClosure x                  == overlay x (transpose x)
    symmetricClosure . symmetricClosure == symmetricClosure

transitiveClosure :: Ord a => Relation a -> Relation a #

Compute the transitive closure of a graph. Complexity: O(n * m * log(n) * log(m)) time.

    transitiveClosure empty               == empty
    transitiveClosure (vertex x)          == vertex x
    transitiveClosure (edge x y)          == edge x y
    transitiveClosure (path $ nub xs)     == clique (nub xs)
    transitiveClosure . transitiveClosure == transitiveClosure

# Miscellaneous

consistent :: Ord a => Relation a -> Bool #

Check that the internal representation of a relation is consistent, i.e. if all pairs of elements in the `relation` refer to existing elements in the `domain`. It should be impossible to create an inconsistent `Relation`, and we use this function in testing.

    consistent empty         == True
    consistent (vertex x)    == True
    consistent (overlay x y) == True
    consistent (connect x y) == True
    consistent (edge x y)    == True
    consistent (edges xs)    == True
    consistent (stars xs)    == True