Compute the breadth-first search forest of a graph, such that adjacent vertices are explored in the increasing order. The search is seeded by a list of vertices that will become the roots of the resulting forest. Duplicates in the list will have their first occurrence explored and subsequent ones ignored. The seed vertices that do not belong to the graph are also ignored.

Complexity: O((L + m) * log n) time and O(n) space, where L is the number of seed vertices.

forest $ bfsForest (edge 1 2) [0]        == empty
forest $ bfsForest (edge 1 2) [1]        == edge 1 2
forest $ bfsForest (edge 1 2) [2]        == vertex 2
forest $ bfsForest (edge 1 2) [0,1,2]    == vertices [1,2]
forest $ bfsForest (edge 1 2) [2,1,0]    == vertices [1,2]
forest $ bfsForest (edge 1 1) [1]        == vertex 1
isSubgraphOf (forest $ bfsForest x vs) x == True
bfsForest x (vertexList x)               == map (\v -> Node v []) (nub $ vertexList x)
bfsForest x []                           == []
bfsForest empty vs                       == []
bfsForest (3 * (1 + 4) * (1 + 5)) [1,4]  == [ Node { rootLabel = 1
                                                   , subForest = [ Node { rootLabel = 5
                                                                        , subForest = [] }]}
                                            , Node { rootLabel = 4
                                                   , subForest = [] }]
forest $ bfsForest (circuit [1..5] + circuit [5,4..1]) [3] == path [3,2,1] + path [3,4,5]

A version of bfsForest where the resulting forest is converted to a level structure. Adjacent vertices are explored in the increasing order. Flattening the result via concat . bfs x gives an enumeration of reachable vertices in the breadth-first search order.

Complexity: O((L + m) * min(n,W)) time and O(n) space, where L is the number of seed vertices.

bfs (edge 1 2) [0]                == []
bfs (edge 1 2) [1]                == [[1], [2]]
bfs (edge 1 2) [2]                == [[2]]
bfs (edge 1 2) [1,2]              == [[1,2]]
bfs (edge 1 2) [2,1]              == [[2,1]]
bfs (edge 1 1) [1]                == [[1]]
bfs empty vs                      == []
bfs x []                          == []
bfs (1 * 2 + 3 * 4 + 5 * 6) [1,2] == [[1,2]]
bfs (1 * 2 + 3 * 4 + 5 * 6) [1,3] == [[1,3], [2,4]]
bfs (3 * (1 + 4) * (1 + 5)) [3]   == [[3], [1,4,5]]

bfs (circuit [1..5] + circuit [5,4..1]) [3]          == [[2], [1,3], [5,4]]
concat $ bfs (circuit [1..5] + circuit [5,4..1]) [3] == [3,2,4,1,5]
map concat . transpose . map levels . bfsForest x    == bfs x

Compute the depth-first search forest of a graph, where adjacent vertices are explored in the increasing order.

Complexity: O((n + m) * min(n,W)) time and O(n) space.

forest $ dfsForest empty              == empty
forest $ dfsForest (edge 1 1)         == vertex 1
forest $ dfsForest (edge 1 2)         == edge 1 2
forest $ dfsForest (edge 2 1)         == vertices [1,2]
isSubgraphOf (forest $ dfsForest x) x == True
isDfsForestOf (dfsForest x) x         == True
dfsForest . forest . dfsForest        == dfsForest
dfsForest (vertices vs)               == map (\v -> Node v []) (nub $ sort vs)
dfsForest $ 3 * (1 + 4) * (1 + 5)     == [ Node { rootLabel = 1
                                                , subForest = [ Node { rootLabel = 5
                                                                     , subForest = [] }]}
                                         , Node { rootLabel = 3
                                                , subForest = [ Node { rootLabel = 4
                                                                     , subForest = [] }]}]
forest (dfsForest $ circuit [1..5] + circuit [5,4..1]) == path [1,2,3,4,5]

Compute the depth-first search forest of a graph starting from the given seed vertices, where adjacent vertices are explored in the increasing order. Note that the resulting forest does not necessarily span the whole graph, as some vertices may be unreachable. The seed vertices which do not belong to the graph are ignored.

Complexity: O((L + m) * log n) time and O(n) space, where L is the number of seed vertices.

forest $ dfsForestFrom empty      vs             == empty
forest $ dfsForestFrom (edge 1 1) [1]            == vertex 1
forest $ dfsForestFrom (edge 1 2) [0]            == empty
forest $ dfsForestFrom (edge 1 2) [1]            == edge 1 2
forest $ dfsForestFrom (edge 1 2) [2]            == vertex 2
forest $ dfsForestFrom (edge 1 2) [1,2]          == edge 1 2
forest $ dfsForestFrom (edge 1 2) [2,1]          == vertices [1,2]
isSubgraphOf (forest $ dfsForestFrom x vs) x     == True
isDfsForestOf (dfsForestFrom x (vertexList x)) x == True
dfsForestFrom x (vertexList x)                   == dfsForest x
dfsForestFrom x []                               == []
dfsForestFrom (3 * (1 + 4) * (1 + 5)) [1,4]      == [ Node { rootLabel = 1
                                                           , subForest = [ Node { rootLabel = 5
                                                                                , subForest = [] }
                                                    , Node { rootLabel = 4
                                                           , subForest = [] }]
forest $ dfsForestFrom (circuit [1..5] + circuit [5,4..1]) [3] == path [3,2,1,5,4]

Return the list vertices visited by the depth-first search in a graph, starting from the given seed vertices. Adjacent vertices are explored in the increasing order.

Complexity: O((L + m) * log n) time and O(n) space, where L is the number of seed vertices.

dfs empty      vs    == []
dfs (edge 1 1) [1]   == [1]
dfs (edge 1 2) [0]   == []
dfs (edge 1 2) [1]   == [1,2]
dfs (edge 1 2) [2]   == [2]
dfs (edge 1 2) [1,2] == [1,2]
dfs (edge 1 2) [2,1] == [2,1]
dfs x          []    == []

and [ hasVertex v x | v <- dfs x vs ]       == True
dfs (3 * (1 + 4) * (1 + 5)) [1,4]           == [1,5,4]
dfs (circuit [1..5] + circuit [5,4..1]) [3] == [3,2,1,5,4]

Return the list of vertices reachable from a source vertex in a graph. The vertices in the resulting list appear in the depth-first search order.

Complexity: O(m * log n) time and O(n) space.

reachable empty              x == []
reachable (vertex 1)         1 == [1]
reachable (edge 1 1)         1 == [1]
reachable (edge 1 2)         0 == []
reachable (edge 1 2)         1 == [1,2]
reachable (edge 1 2)         2 == [2]
reachable (path    [1..8]  ) 4 == [4..8]
reachable (circuit [1..8]  ) 4 == [4..8] ++ [1..3]
reachable (clique  [8,7..1]) 8 == [8] ++ [1..7]

and [ hasVertex v x | v <- reachable x y ] == True

Compute a topological sort of a graph or discover a cycle.

Vertices are explored in the decreasing order according to their Ord instance. This gives the lexicographically smallest topological ordering in the case of success. In the case of failure, the cycle is characterized by being the lexicographically smallest up to rotation with respect to Ord (Dual Int) in the first connected component of the graph containing a cycle, where the connected components are ordered by their largest vertex with respect to Ord a.

Complexity: O((n + m) * min(n,W)) time and O(n) space.

topSort (1 * 2 + 3 * 1)                    == Right [3,1,2]
topSort (path [1..5])                      == Right [1..5]
topSort (3 * (1 * 4 + 2 * 5))              == Right [3,1,2,4,5]
topSort (1 * 2 + 2 * 1)                    == Left (2 :| [1])
topSort (path [5,4..1] + edge 2 4)         == Left (4 :| [3,2])
topSort (circuit [1..3])                   == Left (3 :| [1,2])
topSort (circuit [1..3] + circuit [3,2,1]) == Left (3 :| [2])
topSort (1 * 2 + (5 + 2) * 1 + 3 * 4 * 3)  == Left (1 :| [2])
fmap (flip isTopSortOf x) (topSort x)      /= Right False
topSort . vertices                         == Right . nub . sort

Check if a given graph is acyclic.

Complexity: O((n + m) * min(n,W)) time and O(n) space.

isAcyclic (1 * 2 + 3 * 1) == True
isAcyclic (1 * 2 + 2 * 1) == False
isAcyclic . circuit       == null
isAcyclic                 == isRight . topSort

Check if a given forest is a correct depth-first search forest of a graph. The implementation is based on the paper "Depth-First Search and Strong Connectivity in Coq" by François Pottier.

isDfsForestOf []                              empty            == True
isDfsForestOf []                              (vertex 1)       == False
isDfsForestOf [Node 1 []]                     (vertex 1)       == True
isDfsForestOf [Node 1 []]                     (vertex 2)       == False
isDfsForestOf [Node 1 [], Node 1 []]          (vertex 1)       == False
isDfsForestOf [Node 1 []]                     (edge 1 1)       == True
isDfsForestOf [Node 1 []]                     (edge 1 2)       == False
isDfsForestOf [Node 1 [], Node 2 []]          (edge 1 2)       == False
isDfsForestOf [Node 2 [], Node 1 []]          (edge 1 2)       == True
isDfsForestOf [Node 1 [Node 2 []]]            (edge 1 2)       == True
isDfsForestOf [Node 1 [], Node 2 []]          (vertices [1,2]) == True
isDfsForestOf [Node 2 [], Node 1 []]          (vertices [1,2]) == True
isDfsForestOf [Node 1 [Node 2 []]]            (vertices [1,2]) == False
isDfsForestOf [Node 1 [Node 2 [Node 3 []]]]   (path [1,2,3])   == True
isDfsForestOf [Node 1 [Node 3 [Node 2 []]]]   (path [1,2,3])   == False
isDfsForestOf [Node 3 [], Node 1 [Node 2 []]] (path [1,2,3])   == True
isDfsForestOf [Node 2 [Node 3 []], Node 1 []] (path [1,2,3])   == True
isDfsForestOf [Node 1 [], Node 2 [Node 3 []]] (path [1,2,3])   == False

Check if a given list of vertices is a correct topological sort of a graph.

isTopSortOf [3,1,2] (1 * 2 + 3 * 1) == True
isTopSortOf [1,2,3] (1 * 2 + 3 * 1) == False
isTopSortOf []      (1 * 2 + 3 * 1) == False
isTopSortOf []      empty           == True
isTopSortOf [x]     (vertex x)      == True
isTopSortOf [x]     (edge x x)      == False