[DOC+first version] test is ko

clustering-louvain.cabal

@@ -4,7 +4,7 @@ cabal-version: 1.12
 --
 -- see: https://github.com/sol/hpack
 --
--- hash: 85a89537835f3546235aaf6eea2ef007ef1dd0406a6c9afc4bf6119d2f8239f0
+-- hash: f5efad8af0a555a91b2aa7b955eb00996a2391daa5222b63ae306ad40e22121c
 
 name:           clustering-louvain
 version:        0.1.0.0
@@ -37,6 +37,7 @@ library
       Data.Graph.Clustering.Louvain.IO.Gexf
       Data.Graph.Clustering.Louvain.CplusPlus
   other-modules:
+      Data.Graph.Clustering.Example
       Data.Graph.Clustering.HLouvain
       Paths_clustering_louvain
   default-language: Haskell2010

src/Data/Graph/Clustering/Example.hs

-module Data.Example where
+module Data.Graph.Clustering.Example where
 
 import Data.List (sort)
-import Data.Utils
+import Data.Graph.Clustering.Louvain.Utils
 import Data.Graph.Inductive
 
 

src/Data/Graph/Clustering/HLouvain.hs

 {-|
 Module      : Data.Graph.Clustering.HLouvain
 Description : Purely functional (Inductive) Louvain clustering
-Copyright   : (c) CNRS, 2020-Present
+Copyright   : (c) Alexandre Delanoë, CNRS, 2020-Present
 License     : AGPL + CECILL v3
 Maintainer  : alexandre.delanoe+louvain@iscpif.fr
 Stability   : experimental
 Portability : POSIX
 
-
-Main Reference:
- * Blondel, Vincent D; Guillaume, Jean-Loup; Lambiotte, Renaud; Lefebvre, Etienne (9 October 2008). "Fast unfolding of communities in large networks". Journal of Statistical Mechanics: Theory and Experiment. 2008 (10): P10008. arXiv:0803.0476 Freely accessible. doi:10.1088/1742-5468/2008/10/P10008.
- * Louvain_Modularity https://en.wikipedia.org/wiki/Louvain_Modularity
 -}
 
 
@@ -18,7 +14,7 @@ module Data.Graph.Clustering.HLouvain
   where
 
 import Data.Ord (Down(..))
-import Data.List (sortOn)
+import Data.List (sortOn, foldl')
 import Data.Graph.Inductive
 -- import Data.Graph.Clustering.Louvain
 
@@ -26,20 +22,28 @@ import Data.Graph.Inductive
 -- | Main Tools
 ------------------------------------------------------------------------
 
+-- | Glustering (instead of clustering) since we are working on the glue
+-- between nodes to create "glusters" of nodes
 glustering :: DynGraph gr
            => gr a b
            -> [Module]
-glustering g = undefined
+glustering g = foldl' (\a b -> modulare g b a) [] modules
+  where
+    modules :: [Module]
+    modules = map (\n -> Module n [] (modularity' g [n])) ns
 
+    ns :: [Node]
+    ns = sortOn (Down . (deg g)) $ nodes g
 
 ------------------------------------------------------------------------
 -- | Definitions
 ------------------------------------------------------------------------
 
-data Module = Module { node    :: Node
-                     , modules :: [Module]
-                     , score   :: Double
+data Module = Module { node    :: !Node
+                     , modules :: ![Module]
+                     , score   :: !Double
                      }
+  deriving (Show)
 
 -- | Divide en conquer method
 modulare :: DynGraph gr
@@ -64,9 +68,9 @@ flat (Module n ns _) = [n] ++ (concat $ map flat ns)
 -- | TODO Sum or Union -> QuickCheck
 modularity :: DynGraph gr => gr a b -> Module -> Double
 modularity g m = modularity' g (flat m)
-  where
-    modularity' :: DynGraph gr => gr a b -> [Node] -> Double
-    modularity' gr ns = coverage - edgeDensity
+
+modularity' :: DynGraph gr => gr a b -> [Node] -> Double
+modularity' gr ns = coverage - edgeDensity
     where
         coverage :: Double
         coverage  = sizeSubGraph / sizeAllGraph