refactoring

ChangeLog.md

 Revision history for haskell-igraph
 ===================================
 
-v0.8.0 -- XXXX-XX-XX
+v0.8.1 -- 2020-02-XX
+--------------------
+
+Add more functions
+
+v0.8.0 -- 2020-02-22
 --------------------
 
 * Ship igraph C sources v0.8.0

haskell-igraph.cabal

 cabal-version:       2.2
 name:                haskell-igraph
-version:             0.8.0
+version:             0.8.1
 synopsis:            Bindings to the igraph C library (v0.8.0).
 description:         igraph<"http://igraph.org/c/"> is a library for creating
                      and manipulating large graphs. This package provides the Haskell
@@ -41,7 +41,7 @@ library
     IGraph.Algorithms.Structure
     IGraph.Algorithms.Community
     IGraph.Algorithms.Clique
-    --IGraph.Algorithms.Layout
+    IGraph.Algorithms.Layout
     IGraph.Algorithms.Motif
     IGraph.Algorithms.Generators
     IGraph.Algorithms.Isomorphism
@@ -51,7 +51,7 @@ library
     IGraph.Internal.C2HS
 
   build-depends:
-      base >= 4.0 && < 5.0
+      base >= 4.10 && < 5.0
     , bytestring >= 0.9
     , cereal
     , conduit >= 1.3.0

src/IGraph/Algorithms.hs

@@ -2,7 +2,7 @@ module IGraph.Algorithms
     ( module IGraph.Algorithms.Structure
     , module IGraph.Algorithms.Community
     , module IGraph.Algorithms.Clique
---    , module IGraph.Algorithms.Layout
+    , module IGraph.Algorithms.Layout
     , module IGraph.Algorithms.Motif
     , module IGraph.Algorithms.Generators
     , module IGraph.Algorithms.Isomorphism
@@ -12,7 +12,7 @@ module IGraph.Algorithms
 import IGraph.Algorithms.Structure
 import IGraph.Algorithms.Community
 import IGraph.Algorithms.Clique
---import IGraph.Algorithms.Layout
+import IGraph.Algorithms.Layout
 import IGraph.Algorithms.Motif
 import IGraph.Algorithms.Generators
 import IGraph.Algorithms.Isomorphism

src/IGraph/Algorithms/Clique.chs

@@ -6,10 +6,7 @@ module IGraph.Algorithms.Clique
     , cliqueNumber
     ) where
 
-import Control.Applicative ((<$>))
 import System.IO.Unsafe (unsafePerformIO)
-
-import qualified Foreign.Ptr as C2HSImp
 import Foreign
 
 import IGraph
@@ -18,6 +15,7 @@ import IGraph.Internal.C2HS
 
 #include "haskell_igraph.h"
 
+-- | Find all or some cliques in a graph.
 cliques :: Graph d v e
         -> (Int, Int)  -- ^ Minimum and maximum size of the cliques to be returned.
                        -- No bound will be used if negative or zero
@@ -27,12 +25,16 @@ cliques gr (lo, hi) = unsafePerformIO $ allocaVectorPtr $ \vptr -> do
     (map.map) truncate <$> toLists vptr
 {#fun igraph_cliques as ^ { `IGraph', castPtr `Ptr VectorPtr', `Int', `Int' } -> `CInt' void- #}
 
+-- | Finds the largest clique(s) in a graph.
+-- Time complexity: O(3^(|V|/3)) worst case.
 largestCliques :: Graph d v e -> [[Int]]
 largestCliques gr = unsafePerformIO $ allocaVectorPtr $ \vptr -> do
     igraphLargestCliques (_graph gr) vptr
     (map.map) truncate <$> toLists vptr
 {#fun igraph_largest_cliques as ^ { `IGraph', castPtr `Ptr VectorPtr' } -> `CInt' void- #}
 
+-- | Find all maximal cliques of a graph. Time complexity: O(d(n-d)3^(d/3))
+-- worst case, d is the degeneracy of the graph.
 maximalCliques :: Graph d v e
                -> (Int, Int)  -- ^ Minimum and maximum size of the cliques to be returned.
                               -- No bound will be used if negative or zero
@@ -42,9 +44,12 @@ maximalCliques gr (lo, hi) = unsafePerformIO $ allocaVectorPtr $ \vpptr -> do
     (map.map) truncate <$> toLists vpptr
 {#fun igraph_maximal_cliques as ^ { `IGraph', castPtr `Ptr VectorPtr', `Int', `Int' } -> `CInt' void- #}
 
+-- | Find the clique number of the graph. The clique number of a graph is
+-- the size of the largest clique.
+-- Time complexity: O(3^(|V|/3)) worst case.
 cliqueNumber :: Graph d v e -> Int
 cliqueNumber gr = unsafePerformIO $ igraphCliqueNumber $ _graph gr
 {#fun igraph_clique_number as ^
     { `IGraph'
     , alloca- `Int' peekIntConv*
-    } -> `CInt' void- #}
+    } -> `CInt' void- #}
\ No newline at end of file

src/IGraph/Algorithms/Community.chs

@@ -2,11 +2,12 @@
 {-# LANGUAGE RecordWildCards #-}
 {-# LANGUAGE DataKinds #-}
 module IGraph.Algorithms.Community
-    ( modularity
-    , findCommunity
+    ( findCommunity
     , CommunityMethod(..)
-    , defaultLeadingEigenvector
-    , defaultSpinglass
+    , leadingEigenvector
+    , spinglass
+    , leiden
+    , modularity
     ) where
 
 import           Data.Function             (on)
@@ -14,37 +15,53 @@ import           Data.List (sortBy, groupBy)
 import Data.List.Ordered (nubSortBy)
 import           Data.Ord (comparing)
 import           System.IO.Unsafe          (unsafePerformIO)
+import           Data.Serialize            (Serialize)
 
 import           Foreign
 import           Foreign.C.Types
 
 import           IGraph
+import           IGraph.Random
 import IGraph.Internal.C2HS
 {#import IGraph.Internal #}
 {#import IGraph.Internal.Constants #}
 
 #include "haskell_igraph.h"
 
-modularity :: Graph d v e
-           -> [[Int]]   -- ^ Communities.
-           -> Maybe [Double] -- ^ Weights
-           -> Double
-modularity gr clusters ws
-    | length nds /= length (concat clusters) = error "Duplicated nodes"
-    | nds /= nodes gr = error "Some nodes were not given community assignments"
-    | otherwise = unsafePerformIO $ withList membership $ \membership' ->
-        withListMaybe ws (igraphModularity (_graph gr) membership')
+-- | Detecting community structure.
+findCommunity :: (Serialize v, Serialize e)
+              => Graph 'U v e
+              -> Maybe (v -> Double)  -- ^ Function to assign node weights
+              -> Maybe (e -> Double)  -- ^ Function to assign edge weights
+              -> CommunityMethod  -- ^ Community finding algorithms
+              -> Gen
+              -> [[Int]]
+findCommunity gr getNodeW getEdgeW method _ = unsafePerformIO $ allocaVector $ \result ->
+    withListMaybe ew $ \ew' -> do
+        case method of
+            LeadingEigenvector n -> allocaArpackOpt $ \arpack ->
+                igraphCommunityLeadingEigenvector (_graph gr) ew' nullPtr result
+                                                  n arpack nullPtr False
+                                                  nullPtr nullPtr nullPtr
+                                                  nullFunPtr nullPtr
+            Spinglass{..} -> igraphCommunitySpinglass (_graph gr) ew' nullPtr nullPtr result
+                                     nullPtr _nSpins False _startTemp
+                                     _stopTemp _coolFact
+                                     IgraphSpincommUpdateConfig _gamma
+                                     IgraphSpincommImpOrig 1.0
+            Leiden{..} -> do
+                _ <- withListMaybe nw $ \nw' -> igraphCommunityLeiden
+                    (_graph gr) ew' nw' _resolution _beta False result nullPtr
+                return ()
+        fmap ( map (fst . unzip) . groupBy ((==) `on` snd)
+              . sortBy (comparing snd) . zip [0..] ) $ toList result
   where
-    (membership, nds) = unzip $ nubSortBy (comparing snd) $ concat $
-        zipWith f [0 :: Int ..] clusters
-      where
-        f i xs = zip (repeat i) xs
-{#fun igraph_modularity as ^
-    { `IGraph'
-    , castPtr `Ptr Vector'
-	, alloca- `Double' peekFloatConv*
-	, castPtr `Ptr Vector'
-    } -> `CInt' void- #}
+    ew = case getEdgeW of
+        Nothing -> Nothing
+        Just f -> Just $ map (f . snd) $ labEdges gr
+    nw = case getNodeW of
+        Nothing -> Nothing
+        Just f -> Just $ map (f . snd) $ labNodes gr
 
 data CommunityMethod =
       LeadingEigenvector
@@ -57,38 +74,29 @@ data CommunityMethod =
         , _coolFact  :: Double  -- ^ the cooling factor for the simulated annealing
         , _gamma     :: Double  -- ^ the gamma parameter of the algorithm.
         }
+    | Leiden
+        { _resolution :: Double
+        , _beta :: Double
+        }
 
-defaultLeadingEigenvector :: CommunityMethod
-defaultLeadingEigenvector = LeadingEigenvector 10000
+-- | Default parameters for the leading eigenvector algorithm.
+leadingEigenvector :: CommunityMethod
+leadingEigenvector = LeadingEigenvector 10000
 
-defaultSpinglass :: CommunityMethod
-defaultSpinglass = Spinglass
+-- | Default parameters for the spin-glass algorithm.
+spinglass :: CommunityMethod
+spinglass = Spinglass
     { _nSpins = 25
     , _startTemp = 1.0
     , _stopTemp = 0.01
     , _coolFact = 0.99
     , _gamma = 1.0 }
 
-findCommunity :: Graph 'U v e
-              -> Maybe [Double]   -- ^ node weights
-              -> CommunityMethod  -- ^ Community finding algorithms
-              -> [[Int]]
-findCommunity gr ws method = unsafePerformIO $ allocaVector $ \result ->
-    withListMaybe ws $ \ws' -> do
-        case method of
-            LeadingEigenvector n -> allocaArpackOpt $ \arpack ->
-                igraphCommunityLeadingEigenvector (_graph gr) ws' nullPtr result
-                                                  n arpack nullPtr False
-                                                  nullPtr nullPtr nullPtr
-                                                  nullFunPtr nullPtr
-            Spinglass{..} -> igraphCommunitySpinglass (_graph gr) ws' nullPtr nullPtr result
-                                     nullPtr _nSpins False _startTemp
-                                     _stopTemp _coolFact
-                                     IgraphSpincommUpdateConfig _gamma
-                                     IgraphSpincommImpOrig 1.0
-
-        fmap ( map (fst . unzip) . groupBy ((==) `on` snd)
-              . sortBy (comparing snd) . zip [0..] ) $ toList result
+-- | Default parameters for the leiden algorithm.
+leiden :: CommunityMethod
+leiden = Leiden
+    { _resolution = 1
+    , _beta = 0 }
 
 {#fun igraph_community_spinglass as ^
     { `IGraph'
@@ -124,6 +132,18 @@ findCommunity gr ws method = unsafePerformIO $ allocaVector $ \result ->
     , id `Ptr ()'
     } -> `CInt' void- #}
 
+{#fun igraph_community_leiden as ^
+    { `IGraph'
+    , castPtr `Ptr Vector'
+    , castPtr `Ptr Vector'
+    , `Double'
+    , `Double'
+    , `Bool'
+    , castPtr `Ptr Vector'
+    , alloca- `Int' peekIntConv*
+    , id `Ptr CDouble'
+    } -> `CInt' void- #}
+
 type T = FunPtr ( Ptr ()
                 -> CLong
                 -> CDouble
@@ -132,3 +152,30 @@ type T = FunPtr ( Ptr ()
                 -> Ptr ()
                 -> Ptr ()
                 -> IO CInt)
+
+-- | Calculate the modularity of a graph with respect to some vertex types.
+modularity :: Serialize e
+           => Graph d v e
+           -> Maybe (e -> Double)  -- ^ Function to assign edge weights
+           -> [[Int]]   -- ^ Communities.
+           -> Double
+modularity gr getEdgeW clusters
+    | length nds /= length (concat clusters) = error "Duplicated nodes"
+    | nds /= nodes gr = error "Some nodes were not given community assignments"
+    | otherwise = unsafePerformIO $ withList membership $ \membership' ->
+        withListMaybe ws (igraphModularity (_graph gr) membership')
+  where
+    (membership, nds) = unzip $ nubSortBy (comparing snd) $ concat $
+        zipWith f [0 :: Int ..] clusters
+      where
+        f i xs = zip (repeat i) xs
+    ws = case getEdgeW of
+        Nothing -> Nothing
+        Just f -> Just $ map (f . snd) $ labEdges gr
+{#fun igraph_modularity as ^
+    { `IGraph'
+    , castPtr `Ptr Vector'
+	, alloca- `Double' peekFloatConv*
+	, castPtr `Ptr Vector'
+    } -> `CInt' void- #}
+

src/IGraph/Algorithms/Generators.chs

@@ -5,6 +5,7 @@ module IGraph.Algorithms.Generators
     ( full
     , star
     , ring
+    , zacharyKarate
     , ErdosRenyiModel(..)
     , erdosRenyiGame
     , degreeSequenceGame
@@ -78,6 +79,20 @@ ring n = unsafePerformIO $ do
     , `Bool'
     } -> `CInt' void- #}
 
+-- | Zachary's karate club
+zacharyKarate :: Graph 'U () ()
+zacharyKarate = mkGraph (replicate 34 ()) $ map (\(a, b) -> ((a-1,b-1),())) es
+  where
+    es = [ (2,1),(3,1),(3,2),(4,1),(4,2),(4,3),(5,1),(6,1),(7,1),(7,5),(7,6)
+         , (8,1),(8,2),(8,3),(8,4),(9,1),(9,3),(10,3),(11,1),(11,5),(11,6)
+         , (12,1),(13,1),(13,4),(14,1),(14,2),(14,3),(14,4),(17,6),(17,7)
+         , (18,1),(18,2),(20,1),(20,2),(22,1),(22,2),(26,24),(26,25)
+         , (28,3),(28,24),(28,25),(29,3),(30,24),(30,27),(31,2),(31,9)
+         , (32,1),(32,25),(32,26),(32,29),(33,3),(33,9),(33,15),(33,16)
+         , (33,19),(33,21),(33,23),(33,24),(33,30),(33,31),(33,32)
+         , (34,9),(34,10),(34,14),(34,15),(34,16),(34,19),(34,20),(34,21)
+         , (34,23),(34,24),(34,27),(34,28),(34,29),(34,30),(34,31),(34,32),(34,33) ]
+
 data ErdosRenyiModel = GNP Int Double  -- ^ G(n,p) graph, every possible edge is
                                        -- included in the graph with probability p.
                      | GNM Int Int   -- ^ G(n,m) graph, m edges are selected

src/IGraph/Algorithms/Isomorphism.chs

 {-# LANGUAGE ForeignFunctionInterface #-}
 {-# LANGUAGE ScopedTypeVariables #-}
 module IGraph.Algorithms.Isomorphism
-    ( getSubisomorphisms
-    , isomorphic
+    ( isomorphic
+    , getSubisomorphisms
     , isoclassCreate
     , isoclass3
     , isoclass4
@@ -20,6 +20,17 @@ import           IGraph.Internal.Initialization (igraphInit)
 
 #include "haskell_igraph.h"
 
+-- | Determine whether two graphs are isomorphic.
+isomorphic :: Graph d v1 e1
+           -> Graph d v2 e2
+           -> Bool
+isomorphic g1 g2 = unsafePerformIO $ alloca $ \ptr -> do
+    _ <- igraphIsomorphic (_graph g1) (_graph g2) ptr
+    x <- peek ptr
+    return (x /= 0)
+{-# INLINE isomorphic #-}
+{#fun igraph_isomorphic as ^ { `IGraph', `IGraph', id `Ptr CInt' } -> `CInt' void- #}
+
 getSubisomorphisms :: Graph d v1 e1  -- ^ graph to be searched in
                    -> Graph d v2 e2   -- ^ smaller graph
                    -> [[Int]]
@@ -44,16 +55,6 @@ getSubisomorphisms g1 g2 = unsafePerformIO $ allocaVectorPtr $ \vpptr -> do
     , id `Ptr ()'
     } -> `CInt' void- #}
 
--- | Determine whether two graphs are isomorphic.
-isomorphic :: Graph d v1 e1
-           -> Graph d v2 e2
-           -> Bool
-isomorphic g1 g2 = unsafePerformIO $ alloca $ \ptr -> do
-    _ <- igraphIsomorphic (_graph g1) (_graph g2) ptr
-    x <- peek ptr
-    return (x /= 0)
-{#fun igraph_isomorphic as ^ { `IGraph', `IGraph', id `Ptr CInt' } -> `CInt' void- #}
-
 -- | Creates a graph from the given isomorphism class.
 -- This function is implemented only for graphs with three or four vertices.
 isoclassCreate :: forall d. SingI d

src/IGraph/Algorithms/Layout.chs

 {-# LANGUAGE ForeignFunctionInterface #-}
 module IGraph.Algorithms.Layout
-    ( getLayout
+    ( layout
     , LayoutMethod(..)
-    , defaultKamadaKawai
-    , defaultLGL
+    , kamadaKawai
+    , lgl
     ) where
 
-import           Data.Maybe             (isJust)
+import           Data.Maybe             (isJust, fromMaybe)
 import           Foreign                (nullPtr)
+import           System.IO.Unsafe          (unsafePerformIO)
 
 import Foreign
 
 import           IGraph
+import IGraph.Random
 {#import IGraph.Internal #}
 
 #include "haskell_igraph.h"
 
+layout :: Graph d v e -> LayoutMethod -> Gen -> [(Double, Double)]
+layout gr method _ = unsafePerformIO $ case method of
+    Random -> allocaMatrix $ \mat -> do
+        igraphLayoutRandom gptr mat
+        getResult mat
+
+    KamadaKawai seed niter kkconst epsilon -> do
+        let f mat = igraphLayoutKamadaKawai gptr mat (isJust seed) niter
+                epsilon (fromMaybe (fromIntegral $ nNodes gr) kkconst) nullPtr
+                nullPtr nullPtr nullPtr nullPtr
+        case seed of
+            Nothing -> allocaMatrix $ \mat -> do
+                f mat
+                getResult mat
+            Just s -> withRowLists ((\(x,y) -> [x,y]) $ unzip s) $ \mat -> do
+                f mat
+                getResult mat
+
+    LGL niter delta area coolexp repulserad cellsize -> allocaMatrix $ \mat -> do
+        igraphLayoutLgl gptr mat niter (delta n) (area n) coolexp
+            (repulserad n) (cellsize n) (-1)
+        getResult mat
+  where
+    n = nNodes gr
+    gptr = _graph gr
+    getResult mat = (\[x, y] -> zip x y) <$> toColumnLists mat
+
 data LayoutMethod =
-    KamadaKawai { kk_seed      :: !(Maybe [(Double, Double)])
-                , kk_nIter     :: !Int
-                , kk_sigma     :: (Int -> Double) -- ^ The base standard deviation of
-                -- position change proposals
-                , kk_startTemp :: !Double  -- ^ The initial temperature for the annealing
-                , kk_coolFact  :: !Double  -- ^ The cooling factor for the simulated annealing
-                , kk_const     :: (Int -> Double)  -- ^ The Kamada-Kawai vertex attraction constant
-                }
+    Random 
+  | KamadaKawai { kk_seed      :: Maybe [(Double, Double)]
+                , kk_nIter     :: Int
+                , kk_const     :: Maybe Double  -- ^ The Kamada-Kawai vertex attraction constant
+                , kk_epsilon   :: Double
+                }   -- ^ The Kamada-Kawai algorithm. Time complexity: O(|V|)
+                    -- for each iteration, after an O(|V|^2 log|V|)
+                    -- initialization step. 
   | LGL { lgl_nIter      :: !Int
         , lgl_maxdelta   :: (Int -> Double)  -- ^ The maximum length of the move allowed
         -- for a vertex in a single iteration. A reasonable default is the number of vertices.
@@ -38,18 +67,17 @@ data LayoutMethod =
         , lgl_cellsize   :: (Int -> Double)
         }
 
-defaultKamadaKawai :: LayoutMethod
-defaultKamadaKawai = KamadaKawai
+-- | Default parameters for the Kamada-Kawai algorithm.
+kamadaKawai :: LayoutMethod
+kamadaKawai = KamadaKawai
     { kk_seed = Nothing
     , kk_nIter = 10
-    , kk_sigma = \x -> fromIntegral x / 4
-    , kk_startTemp = 10
-    , kk_coolFact = 0.99
-    , kk_const = \x -> fromIntegral $ x^2
-    }
+    , kk_const = Nothing
+    , kk_epsilon = 0 }
 
-defaultLGL :: LayoutMethod
-defaultLGL = LGL
+-- | Default parameters for the LGL algorithm.
+lgl :: LayoutMethod
+lgl = LGL
     { lgl_nIter = 100
     , lgl_maxdelta = \x -> fromIntegral x
     , lgl_area = area
@@ -60,40 +88,20 @@ defaultLGL = LGL
   where
     area x = fromIntegral $ x^2
 
-getLayout :: Graph d v e -> LayoutMethod -> IO [(Double, Double)]
-getLayout gr method = case method of
-    KamadaKawai seed niter sigma initemp coolexp kkconst -> case seed of
-        Nothing -> allocaMatrix $ \mat -> do
-            igraphLayoutKamadaKawai gptr mat niter (sigma n) initemp coolexp
-                (kkconst n) (isJust seed) nullPtr nullPtr nullPtr nullPtr
-            [x, y] <- toColumnLists mat
-            return $ zip x y
-        Just xs -> if length xs /= nNodes gr
-            then error "Seed error: incorrect size"
-            else withRowLists ((\(x,y) -> [x,y]) (unzip xs)) $ \mat -> do
-                igraphLayoutKamadaKawai gptr mat niter (sigma n) initemp coolexp
-                    (kkconst n) (isJust seed) nullPtr nullPtr nullPtr nullPtr
-                [x, y] <- toColumnLists mat
-                return $ zip x y
-
-    LGL niter delta area coolexp repulserad cellsize -> allocaMatrix $ \mat -> do
-        igraphLayoutLgl gptr mat niter (delta n) (area n) coolexp
-            (repulserad n) (cellsize n) (-1)
-        [x, y] <- toColumnLists mat
-        return $ zip x y
-  where
-    n = nNodes gr
-    gptr = _graph gr
-
-{#fun igraph_layout_kamada_kawai as ^
+-- | Places the vertices uniform randomly on a plane.
+{#fun igraph_layout_random as ^
     { `IGraph'
     , castPtr `Ptr Matrix'
-    , `Int'
-    , `Double'
-    , `Double'
-    , `Double'
-    , `Double'
-    , `Bool'
+    } -> `CInt' void- #}
+
+{#fun igraph_layout_kamada_kawai as ^
+    { `IGraph'                    -- ^ Graph
+    , castPtr `Ptr Matrix'        -- ^ Pointer to the result matrix
+    , `Bool'                      -- ^ Whether to use the seed
+    , `Int'                       -- ^ The maximum number of iterations to perform
+    , `Double'                    -- ^ epsilon
+    , `Double'                    -- ^ kkconst
+    , castPtr `Ptr Vector'        -- ^ edges weights
     , castPtr `Ptr Vector'
     , castPtr `Ptr Vector'
     , castPtr `Ptr Vector'
@@ -110,4 +118,4 @@ getLayout gr method = case method of
     , `Double'
     , `Double'
     , `Int'
-    } -> `CInt' void- #}
+    } -> `CInt' void- #}
\ No newline at end of file

src/IGraph/Algorithms/Motif.chs

 {-# LANGUAGE ForeignFunctionInterface #-}
 {-# LANGUAGE DataKinds #-}
 module IGraph.Algorithms.Motif
-    ( triad
+    ( dyadCensus
+    , triad
     , triadCensus
     ) where
 
@@ -10,10 +11,26 @@ import System.IO.Unsafe (unsafePerformIO)
 import Foreign
 
 import IGraph
+import IGraph.Internal.C2HS
 {#import IGraph.Internal #}
 
 #include "haskell_igraph.h"
 
+-- | Dyad census means classifying each pair of vertices of a directed graph
+-- into three categories: mutual, there is an edge from a to b and also
+-- from b to a; asymmetric, there is an edge either from a to b or
+-- from b to a but not the other way; null, no edges between a and b.
+dyadCensus :: Graph D v e -> (Int, Int, Int)
+dyadCensus = unsafePerformIO . igraphDyadCensus . _graph
+{-# INLINE dyadCensus #-}
+
+{#fun igraph_dyad_census as ^
+    { `IGraph'
+    , alloca- `Int' peekIntConv*
+    , alloca- `Int' peekIntConv*
+    , alloca- `Int' peekIntConv*
+    } -> `CInt' void- #}
+
 -- | Every triple of vertices in a directed graph
 -- 003: A, B, C, the empty graph.
 -- 012: A->B, C, a graph with a single directed edge.
@@ -54,16 +71,18 @@ triad = map make edgeList
          ]
     make :: [(Int, Int)] -> Graph 'D () ()
     make xs = mkGraph (replicate 3 ()) $ zip xs $ repeat ()
+{-# INLINE triad #-}
 
-triadCensus :: (Ord v, Read v) => Graph d v e -> [Int]
+-- | Calculating the triad census means classifying every triple of vertices
+-- in a directed graph. A triple can be in one of 16 states listed in `triad`.
+triadCensus :: (Ord v, Read v) => Graph D v e -> [Int]
 triadCensus gr = unsafePerformIO $ allocaVector $ \result -> do
     igraphTriadCensus (_graph gr) result
     map truncate <$> toList result
-
--- motifsRandesu
-
+{-# INLINE triadCensus #-}
 {#fun igraph_triad_census as ^ { `IGraph'
                                , castPtr `Ptr Vector' } -> `CInt' void- #}
 
+-- motifsRandesu
 {#fun igraph_motifs_randesu as ^ { `IGraph', castPtr `Ptr Vector', `Int'
                                  , castPtr `Ptr Vector' } -> `CInt' void- #}

src/IGraph/Algorithms/Structure.chs

@@ -29,14 +29,6 @@ import IGraph.Internal.C2HS
 
 #include "haskell_igraph.h"
 
-{#fun igraph_shortest_paths as ^
-    { `IGraph'
-    , castPtr `Ptr Matrix'
-    , castPtr %`Ptr VertexSelector'
-    , castPtr %`Ptr VertexSelector'
-    , `Neimode'
-    } -> `CInt' void- #}
-
 -- Calculates and returns a single unweighted shortest path from a given vertex
 -- to another one. If there are more than one shortest paths between the two
 -- vertices, then an arbitrary one is returned.
@@ -53,6 +45,7 @@ shortestPath gr s t getEdgeW = unsafePerformIO $ allocaVector $ \path -> do
         Just f -> withList (map (f . snd) $ labEdges gr) $ \ws ->
             igraphGetShortestPathDijkstra (_graph gr) path nullPtr s t ws IgraphOut
     map truncate <$> toList path
+{-# INLINE shortestPath #-}
 {#fun igraph_get_shortest_path as ^
     { `IGraph'
     , castPtr `Ptr Vector'
@@ -71,13 +64,16 @@ shortestPath gr s t getEdgeW = unsafePerformIO $ allocaVector $ \path -> do
     , `Neimode'
     } -> `CInt' void- #}
 
+-- | Creates a subgraph induced by the specified vertices. This function collects
+-- the specified vertices and all edges between them to a new graph.
 inducedSubgraph :: (Ord v, Serialize v)
                 => Graph d v e
-                -> [Int]
+                -> [Node]
                 -> Graph d v e
 inducedSubgraph gr nds = unsafePerformIO $ withVerticesList nds $ \vs ->
     igraphInducedSubgraph (_graph gr) vs IgraphSubgraphCreateFromScratch >>=
         (\g -> return $ Graph g $ mkLabelToId g)
+{-# INLINE inducedSubgraph #-}
 {#fun igraph_induced_subgraph as ^
     { `IGraph'
     , allocaIGraph- `IGraph' addIGraphFinalizer*
@@ -88,10 +84,11 @@ inducedSubgraph gr nds = unsafePerformIO $ withVerticesList nds $ \vs ->
 -- | Decides whether the graph is weakly connected.
 isConnected :: Graph d v e -> Bool
 isConnected gr = igraphIsConnected (_graph gr) IgraphWeak
+{-# INLINE isConnected #-}
 
 isStronglyConnected :: Graph 'D v e -> Bool
 isStronglyConnected gr = igraphIsConnected (_graph gr) IgraphStrong
-
+{-# INLINE isStronglyConnected #-}
 {#fun pure igraph_is_connected as ^
     { `IGraph'
     , alloca- `Bool' peekBool*
@@ -124,11 +121,14 @@ isDag = igraphIsDag . _graph
     { `IGraph'
     , alloca- `Bool' peekBool*
     } -> `CInt' void- #}
+{-# INLINE isDag #-}
 
--- | Calculate a possible topological sorting of the graph.
+-- | Calculate a possible topological sorting of the graph. Raise error if the
+-- graph is not acyclic.
 topSort :: Graph d v e -> [Node]
 topSort gr | isDag gr = topSortUnsafe gr
            | otherwise = error "the graph is not acyclic"
+{-# INLINE topSort #-}
 
 -- | Calculate a possible topological sorting of the graph. If the graph is not
 -- acyclic (it has at least one cycle), a partial topological sort is returned.
@@ -138,6 +138,7 @@ topSortUnsafe gr = unsafePerformIO $ allocaVectorN n $ \res -> do
     map truncate <$> toList res
   where
     n = nNodes gr
+{-# INLINE topSortUnsafe #-}
 {#fun igraph_topological_sorting as ^
     { `IGraph'
     , castPtr `Ptr Vector'

tests/Test/Algorithms.hs

@@ -87,10 +87,25 @@ decomposeTest = testGroup "Decompose"
 		 , (8,9), (9,10) ]
     gr = mkGraph (replicate 11 ()) $ zip es $ repeat () :: Graph 'U () ()
 
+{-
+communityTest :: TestTree
+communityTest = testGroup "Community"
+    [ consistency ]
+  where
+    consistency = testCase "Consistency" $ do
+        r1 <- withSeed 134 $ return . findCommunity zacharyKarate Nothing spinglass
+        r2 <- withSeed 14 $ return . findCommunity zacharyKarate Nothing spinglass 
+        r1 @=? r2
+        -}
+
 pagerankTest :: TestTree
 pagerankTest = testGroup "PageRank"
-    [ testCase "case 1" $ ranks @=? ranks' ]
+    [ consistency
+    , testCase "case 1" $ ranks @=? ranks' ]
   where
+    consistency = testCase "Consistency" $ 
+        pagerank gr 0.85 Nothing Nothing @=?
+        pagerank gr 0.85 Nothing Nothing
     gr = star 11
     ranks = [0.47,0.05,0.05,0.05,0.05,0.05,0.05,0.05,0.05,0.05,0.05]
     ranks' = map ((/100) . fromIntegral . round. (*100)) $