Copyright | (c) Andrey Mokhov 2016-2021 |
---|---|
License | MIT (see the file LICENSE) |
Maintainer | andrey.mokhov@gmail.com |
Stability | experimental |
Safe Haskell | None |
Language | Haskell2010 |
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 AdjacencyIntMap
data type and associated functions.
See Algebra.Graph.AdjacencyIntMap.Algorithm for implementations of basic
graph algorithms. AdjacencyIntMap
is an instance of the Graph
type
class, which can be used for polymorphic graph construction and manipulation.
See Algebra.Graph.AdjacencyMap for graphs with non-Int
vertices.
Synopsis
- data AdjacencyIntMap
- adjacencyIntMap :: AdjacencyIntMap -> IntMap IntSet
- fromAdjacencyMap :: AdjacencyMap Int -> AdjacencyIntMap
- empty :: AdjacencyIntMap
- vertex :: Int -> AdjacencyIntMap
- edge :: Int -> Int -> AdjacencyIntMap
- overlay :: AdjacencyIntMap -> AdjacencyIntMap -> AdjacencyIntMap
- connect :: AdjacencyIntMap -> AdjacencyIntMap -> AdjacencyIntMap
- vertices :: [Int] -> AdjacencyIntMap
- edges :: [(Int, Int)] -> AdjacencyIntMap
- overlays :: [AdjacencyIntMap] -> AdjacencyIntMap
- connects :: [AdjacencyIntMap] -> AdjacencyIntMap
- isSubgraphOf :: AdjacencyIntMap -> AdjacencyIntMap -> Bool
- isEmpty :: AdjacencyIntMap -> Bool
- hasVertex :: Int -> AdjacencyIntMap -> Bool
- hasEdge :: Int -> Int -> AdjacencyIntMap -> Bool
- vertexCount :: AdjacencyIntMap -> Int
- edgeCount :: AdjacencyIntMap -> Int
- vertexList :: AdjacencyIntMap -> [Int]
- edgeList :: AdjacencyIntMap -> [(Int, Int)]
- adjacencyList :: AdjacencyIntMap -> [(Int, [Int])]
- vertexIntSet :: AdjacencyIntMap -> IntSet
- edgeSet :: AdjacencyIntMap -> Set (Int, Int)
- preIntSet :: Int -> AdjacencyIntMap -> IntSet
- postIntSet :: Int -> AdjacencyIntMap -> IntSet
- path :: [Int] -> AdjacencyIntMap
- circuit :: [Int] -> AdjacencyIntMap
- clique :: [Int] -> AdjacencyIntMap
- biclique :: [Int] -> [Int] -> AdjacencyIntMap
- star :: Int -> [Int] -> AdjacencyIntMap
- stars :: [(Int, [Int])] -> AdjacencyIntMap
- fromAdjacencyIntSets :: [(Int, IntSet)] -> AdjacencyIntMap
- tree :: Tree Int -> AdjacencyIntMap
- forest :: Forest Int -> AdjacencyIntMap
- removeVertex :: Int -> AdjacencyIntMap -> AdjacencyIntMap
- removeEdge :: Int -> Int -> AdjacencyIntMap -> AdjacencyIntMap
- replaceVertex :: Int -> Int -> AdjacencyIntMap -> AdjacencyIntMap
- mergeVertices :: (Int -> Bool) -> Int -> AdjacencyIntMap -> AdjacencyIntMap
- transpose :: AdjacencyIntMap -> AdjacencyIntMap
- gmap :: (Int -> Int) -> AdjacencyIntMap -> AdjacencyIntMap
- induce :: (Int -> Bool) -> AdjacencyIntMap -> AdjacencyIntMap
- compose :: AdjacencyIntMap -> AdjacencyIntMap -> AdjacencyIntMap
- closure :: AdjacencyIntMap -> AdjacencyIntMap
- reflexiveClosure :: AdjacencyIntMap -> AdjacencyIntMap
- symmetricClosure :: AdjacencyIntMap -> AdjacencyIntMap
- transitiveClosure :: AdjacencyIntMap -> AdjacencyIntMap
- consistent :: AdjacencyIntMap -> Bool
Data structure
data AdjacencyIntMap Source #
The AdjacencyIntMap
data type represents a graph by a map of vertices to
their adjacency sets. 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 :: AdjacencyIntMap Int) == "empty" show (1 :: AdjacencyIntMap Int) == "vertex 1" show (1 + 2 :: AdjacencyIntMap Int) == "vertices [1,2]" show (1 * 2 :: AdjacencyIntMap Int) == "edge 1 2" show (1 * 2 * 3 :: AdjacencyIntMap Int) == "edges [(1,2),(1,3),(2,3)]" show (1 * 2 + 3 :: AdjacencyIntMap 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 hasempty
as the identity:x * empty == x empty * x == x x * (y * z) == (x * y) * z
connect
distributes overoverlay
: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
hasempty
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
2vertex
3 <edge
1 2vertex
1 <edge
1 1edge
1 1 <edge
1 2edge
1 2 <edge
1 1 +edge
2 2edge
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
adjacencyIntMap :: AdjacencyIntMap -> IntMap IntSet Source #
The adjacency map of a graph: each vertex is associated with a set of its direct successors. Complexity: O(1) time and memory.
adjacencyIntMapempty
== IntMap.empty
adjacencyIntMap (vertex
x) == IntMap.singleton
x IntSet.empty
adjacencyIntMap (edge
1 1) == IntMap.singleton
1 (IntSet.singleton
1) adjacencyIntMap (edge
1 2) == IntMap.fromList
[(1,IntSet.singleton
2), (2,IntSet.empty
)]
fromAdjacencyMap :: AdjacencyMap Int -> AdjacencyIntMap Source #
Construct an AdjacencyIntMap
from an AdjacencyMap
with vertices of
type Int
.
Complexity: O(n + m) time and memory.
fromAdjacencyMap ==stars
. AdjacencyMap.adjacencyList
Basic graph construction primitives
empty :: AdjacencyIntMap Source #
Construct the empty graph.
isEmpty
empty == TruehasVertex
x empty == FalsevertexCount
empty == 0edgeCount
empty == 0
vertex :: Int -> AdjacencyIntMap Source #
Construct the graph comprising a single isolated vertex.
isEmpty
(vertex x) == FalsehasVertex
x (vertex y) == (x == y)vertexCount
(vertex x) == 1edgeCount
(vertex x) == 0
edge :: Int -> Int -> AdjacencyIntMap Source #
Construct the graph comprising a single edge.
edge x y ==connect
(vertex
x) (vertex
y)hasEdge
x y (edge x y) == TrueedgeCount
(edge x y) == 1vertexCount
(edge 1 1) == 1vertexCount
(edge 1 2) == 2
overlay :: AdjacencyIntMap -> AdjacencyIntMap -> AdjacencyIntMap Source #
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
yhasVertex
z (overlay x y) ==hasVertex
z x ||hasVertex
z yvertexCount
(overlay x y) >=vertexCount
xvertexCount
(overlay x y) <=vertexCount
x +vertexCount
yedgeCount
(overlay x y) >=edgeCount
xedgeCount
(overlay x y) <=edgeCount
x +edgeCount
yvertexCount
(overlay 1 2) == 2edgeCount
(overlay 1 2) == 0
connect :: AdjacencyIntMap -> AdjacencyIntMap -> AdjacencyIntMap Source #
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
yhasVertex
z (connect x y) ==hasVertex
z x ||hasVertex
z yvertexCount
(connect x y) >=vertexCount
xvertexCount
(connect x y) <=vertexCount
x +vertexCount
yedgeCount
(connect x y) >=edgeCount
xedgeCount
(connect x y) >=edgeCount
yedgeCount
(connect x y) >=vertexCount
x *vertexCount
yedgeCount
(connect x y) <=vertexCount
x *vertexCount
y +edgeCount
x +edgeCount
yvertexCount
(connect 1 2) == 2edgeCount
(connect 1 2) == 1
vertices :: [Int] -> AdjacencyIntMap Source #
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
. mapvertex
hasVertex
x . vertices ==elem
xvertexCount
. vertices ==length
.nub
vertexIntSet
. vertices == IntSet.fromList
overlays :: [AdjacencyIntMap] -> AdjacencyIntMap Source #
connects :: [AdjacencyIntMap] -> AdjacencyIntMap Source #
Relations on graphs
isSubgraphOf :: AdjacencyIntMap -> AdjacencyIntMap -> Bool Source #
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.
isSubgraphOfempty
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 :: AdjacencyIntMap -> Bool Source #
Check if a graph is empty. Complexity: O(1) time.
isEmptyempty
== 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 :: Int -> AdjacencyIntMap -> Bool Source #
Check if a graph contains a given vertex. Complexity: O(log(n)) time.
hasVertex xempty
== False hasVertex x (vertex
y) == (x == y) hasVertex x .removeVertex
x ==const
False
vertexCount :: AdjacencyIntMap -> Int Source #
The number of vertices in a graph. Complexity: O(1) time.
vertexCountempty
== 0 vertexCount (vertex
x) == 1 vertexCount ==length
.vertexList
vertexCount x < vertexCount y ==> x < y
edgeCount :: AdjacencyIntMap -> Int Source #
vertexList :: AdjacencyIntMap -> [Int] Source #
adjacencyList :: AdjacencyIntMap -> [(Int, [Int])] Source #
vertexIntSet :: AdjacencyIntMap -> IntSet Source #
postIntSet :: Int -> AdjacencyIntMap -> IntSet Source #
Standard families of graphs
path :: [Int] -> AdjacencyIntMap Source #
circuit :: [Int] -> AdjacencyIntMap Source #
clique :: [Int] -> AdjacencyIntMap Source #
stars :: [(Int, [Int])] -> AdjacencyIntMap Source #
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
== idoverlay
(stars xs) (stars ys) == stars (xs ++ ys)
fromAdjacencyIntSets :: [(Int, IntSet)] -> AdjacencyIntMap Source #
Construct a graph from a list of adjacency sets; a variation of stars
.
Complexity: O((n + m) * log(n)) time and O(n + m) memory.
fromAdjacencyIntSets [] ==empty
fromAdjacencyIntSets [(x, IntSet.empty
)] ==vertex
x fromAdjacencyIntSets [(x, IntSet.singleton
y)] ==edge
x y fromAdjacencyIntSets .map
(fmap
IntSet.fromList
) ==stars
overlay
(fromAdjacencyIntSets xs) (fromAdjacencyIntSets ys) == fromAdjacencyIntSets (xs ++ ys)
tree :: Tree Int -> AdjacencyIntMap Source #
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)]
Graph transformation
removeVertex :: Int -> AdjacencyIntMap -> AdjacencyIntMap Source #
removeEdge :: Int -> Int -> AdjacencyIntMap -> AdjacencyIntMap Source #
Remove an edge from a given graph. Complexity: O(log(n)) 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 :: Int -> Int -> AdjacencyIntMap -> AdjacencyIntMap Source #
The function
replaces vertex replaceVertex
x yx
with vertex y
in a
given AdjacencyIntMap
. 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 :: (Int -> Bool) -> Int -> AdjacencyIntMap -> AdjacencyIntMap Source #
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 mergeVerticeseven
1 (0 * 2) == 1 * 1 mergeVerticesodd
1 (3 + 4 * 5) == 4 * 1
gmap :: (Int -> Int) -> AdjacencyIntMap -> AdjacencyIntMap Source #
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
AdjacencyIntMap
.
Complexity: O((n + m) * log(n)) time.
gmap fempty
==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 :: (Int -> Bool) -> AdjacencyIntMap -> AdjacencyIntMap Source #
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
Relational operations
compose :: AdjacencyIntMap -> AdjacencyIntMap -> AdjacencyIntMap Source #
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(n)) time and O(n + m) memory.
composeempty
x ==empty
compose xempty
==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 :: AdjacencyIntMap -> AdjacencyIntMap Source #
Compute the reflexive and transitive closure of a graph. Complexity: O(n * m * log(n)^2) time.
closureempty
==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 == closurepostIntSet
x (closure y) == IntSet.fromList
(reachable
x y)
reflexiveClosure :: AdjacencyIntMap -> AdjacencyIntMap Source #
Compute the reflexive closure of a graph by adding a self-loop to every vertex. Complexity: O(n * log(n)) time.
reflexiveClosureempty
==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 :: AdjacencyIntMap -> AdjacencyIntMap Source #
Compute the symmetric closure of a graph by overlaying it with its own transpose. Complexity: O((n + m) * log(n)) time.
symmetricClosureempty
==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 :: AdjacencyIntMap -> AdjacencyIntMap Source #
Compute the transitive closure of a graph. Complexity: O(n * m * log(n)^2) time.
transitiveClosureempty
==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 :: AdjacencyIntMap -> Bool Source #
Check that the internal graph representation is consistent, i.e. that all edges refer to existing vertices. It should be impossible to create an inconsistent adjacency map, and we use this function in testing.
consistentempty
== 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