-- Copyright (c) David Amos, 2008. All rights reserved. module Math.Combinatorics.Graph where import qualified Data.List as L import Data.Maybe (isJust) import qualified Data.Map as M import qualified Data.Set as S import Control.Arrow ( (&&&) ) import Math.Common.ListSet import Math.Algebra.Group.PermutationGroup import Math.Algebra.Group.SchreierSims as SS -- Main source: Godsil & Royle, Algebraic Graph Theory -- COMBINATORICS -- Some functions we'll use set xs = map head $ L.group $ L.sort xs -- subsets of a set (returned in "binary" order) powerset [] = [[]] powerset (x:xs) = let p = powerset xs in p ++ map (x:) p -- subsets of size k (returned in ascending order) combinationsOf 0 _ = [[]] combinationsOf _ [] = [] combinationsOf k (x:xs) = map (x:) (combinationsOf (k-1) xs) ++ combinationsOf k xs -- GRAPH data Graph a = G [a] [[a]] deriving (Eq,Ord,Show) -- we require that vs, es, and each individual e are sorted isSetSystem xs bs = isListSet xs && isListSet bs && all isListSet bs && all (`isSubset` xs) bs isGraph vs es = isSetSystem vs es && all ( (==2) . length) es graph (vs,es) | isGraph vs es = G vs es -- isValid g = g where g = G vs es toGraph (vs,es) | isGraph vs' es' = G vs' es' where vs' = L.sort vs es' = L.sort $ map L.sort es -- note that calling isListSet on a sorted list still checks that there are no duplicates vertices (G vs _) = vs edges (G _ es) = es -- OTHER REPRESENTATIONS -- incidence matrix of a graph -- (rows and columns indexed by edges and vertices respectively) -- (warning: in the literature it is often the other way round) incidenceMatrix (G vs es) = [ [if v `elem` e then 1 else 0 | v <- vs] | e <- es] fromIncidenceMatrix m = graph (vs,es) where n = L.genericLength $ head m vs = [1..n] es = L.sort $ map edge m edge row = [v | (1,v) <- zip row vs] adjacencyMatrix (G vs es) = [ [if L.sort [i,j] `S.member` es' then 1 else 0 | j <- vs] | i <- vs] where es' = S.fromList es fromAdjacencyMatrix m = graph (vs,es) where n = L.genericLength m vs = [1..n] es = es' 1 m es' i (r:rs) = [ [i,j] | (j,1) <- drop i (zip vs r)] ++ es' (i+1) rs es' _ [] = [] -- SOME SIMPLE FAMILIES OF GRAPHS nullGraph :: Graph Int -- type signature needed nullGraph = G [] [] -- cyclic graph c n = graph (vs,es) where vs = [1..n] es = L.insert [1,n] [[i,i+1] | i <- [1..n-1]] -- automorphism group is D2n -- complete graph k n = graph (vs,es) where vs = [1..n] es = [[i,j] | i <- [1..n-1], j <- [i+1..n]] -- == combinationsOf 2 [1..n] -- automorphism group is Sn -- complete bipartite graph kb m n = to1n $ kb' m n kb' m n = graph (vs,es) where vs = map Left [1..m] ++ map Right [1..n] es = [ [Left i, Right j] | i <- [1..m], j <- [1..n] ] -- automorphism group is Sm*Sn (plus a flip if m==n) -- k-cube q k = let vs = zip [0..] (powerset [1..k]) es = [ [i,j] | (i,iset) <- vs, (j,jset) <- vs, i < j, length (iset `symDiff` jset) == 1 ] in graph (map fst vs,es) q' k = let us = powerset $ map (2^) [0..k-1] vs = [0..2^k-1] -- == L.sort $ map sum us es = L.sort [ L.sort [sum u, sum v] | [u,v] <- combinationsOf 2 us, length (u `symDiff` v) == 1 ] in graph (vs, es) tetrahedron = k 4 cube = q 3 octahedron = graph (vs,es) where vs = [1..6] es = combinationsOf 2 vs L.\\ [[1,6],[2,5],[3,4]] dodecahedron = toGraph (vs,es) where vs = [1..20] es = [ [1,2],[2,3],[3,4],[4,5],[5,1], [6,7],[7,8],[8,9],[9,10],[10,11],[11,12],[12,13],[13,14],[14,15],[15,6], [16,17],[17,18],[18,19],[19,20],[20,16], [1,6],[2,8],[3,10],[4,12],[5,14], [7,16],[9,17],[11,18],[13,19],[15,20] ] icosahedron = toGraph (vs,es) where vs = [1..12] es = [ [1,2],[1,3],[1,4],[1,5],[1,6], [2,3],[3,4],[4,5],[5,6],[6,2], [7,12],[8,12],[9,12],[10,12],[11,12], [7,8],[8,9],[9,10],[10,11],[11,7], [2,7],[7,3],[3,8],[8,4],[4,9],[9,5],[5,10],[10,6],[6,11],[11,2] ] -- convert a graph to have [1..n] as vertices to1n (G vs es) = graph (vs',es') where mapping = M.fromList $ zip vs [1..] -- the mapping from vs to [1..n] vs' = M.elems mapping es' = [map (mapping M.!) e | e <- es] -- the edges will already be sorted correctly by construction -- NEW GRAPHS FROM OLD complement (G vs es) = graph (vs,es') where es' = combinationsOf 2 vs \\ es -- es' = [e | e <- combinationsOf 2 vs, e `notElem` es] lineGraph g = to1n $ lineGraph' g lineGraph' (G vs es) = graph (es, [ [ei,ej] | ei <- es, ej <- dropWhile (<= ei) es, ei `intersect` ej /= [] ]) petersen = complement $ lineGraph $ k 5 -- SIMPLE PROPERTIES OF GRAPHS order g = length (vertices g) size g = length (edges g) -- also called degree valency (G vs es) v = length $ filter (v `elem`) es valencies g@(G vs es) = map (head &&& length) $ L.group $ L.sort $ map (valency g) vs regularParam g = case valencies g of [(v,_)] -> Just v _ -> Nothing isRegular g = isJust $ regularParam g isCubic g = regularParam g == Just 3 nbrs (G vs es) v = [u | [u,v'] <- es, v == v'] ++ [w | [v',w] <- es, v == v'] -- if the graph is valid, then the neighbours will be returned in ascending order -- find paths from x to y using bfs -- by definition, a path is a subgraph isomorphic to a "line" - it can't have self-crossings -- (a walk allows self-crossings, a trail allows self-crossings but no edge reuse) findPaths g@(G vs es) x y = map reverse $ bfs [ [x] ] where bfs ((z:zs) : nodes) | z == y = (z:zs) : bfs nodes | otherwise = bfs (nodes ++ [(w:z:zs) | w <- nbrs g z, w `notElem` zs]) bfs [] = [] -- length of the shortest path from x to y distance g x y = case findPaths g x y of [] -> -1 -- infinite p:ps -> length p - 1 -- diameter of a graph is maximum distance between two distinct vertices diameter g@(G vs es) | isConnected g = maximum $ map maxDistance vs | otherwise = -1 where maxDistance v = length (distancePartition g v) - 1 -- find cycles starting at x -- by definition, a cycle is a subgraph isomorphic to a cyclic graph - it can't have self-crossings -- (a circuit allows self-crossings but not edge reuse) findCycles g@(G vs es) x = [reverse (x:z:zs) | z:zs <- bfs [ [x] ], z `elem` nbrsx, length zs > 1] where nbrsx = nbrs g x bfs ((z:zs) : nodes) = (z:zs) : bfs (nodes ++ [ w:z:zs | w <- nbrs g z, w `notElem` zs]) bfs [] = [] -- girth of a graph is the size of the smallest cycle it contains -- Note: If graph contains no cycles, we return -1, representing infinity girth g@(G vs es) = minimum' $ map minCycle vs where minimum' xs = let (zs,nzs) = L.partition (==0) xs in if null nzs then -1 else minimum nzs minCycle v = case findCycles g v of [] -> 0 c:cs -> length c - 1 -- because v occurs twice in c, as startpoint and endpoint -- circumference = max cycle - Bollobas p104 distancePartition g v = distancePartition' S.empty (S.singleton v) where distancePartition' interior boundary | S.null boundary = [] | otherwise = let interior' = S.union interior boundary boundary' = foldl S.union S.empty [S.fromList (nbrs g x) | x <- S.toList boundary] S.\\ interior' in S.toList boundary : distancePartition' interior' boundary' -- the connected component to which v belongs component g v = concat $ distancePartition g v isConnected g@(G (v:vs) es) = length (component g v) == length (v:vs) isConnected (G [] []) = True -- MORE GRAPHS -- Generalized Johnson graph, Godsil & Royle p9 j v k i | v >= k && k >= i = graph (vs,es) where vs = combinationsOf k [1..v] es = [ [v1,v2] | [v1,v2] <- combinationsOf 2 vs, length (v1 `intersect` v2) == i ] -- j v k i is isomorphic to j v (v-k) (v-2k+i), so may as well have v >= 2k kneser v k | v >= 2*k = j v k 0 johnson v k | v >= 2*k = j v k (k-1) petersen1 = to1n $ j 5 2 0