cosmetics

src/Data/Graph/Clustering/FLouvain.hs

@@ -7,6 +7,26 @@ Maintainer  : alexandre.delanoe+louvain@iscpif.fr
 Stability   : experimental
 Portability : POSIX
 
+# Louvain Algorithm
+
+Our algorithm is divided into two phases that are repeated iteratively.
+Assume that we start with a weighted network of N nodes. First, we
+assign a different community to each node of the network. So, in this
+initial partition there are as many communities as there are nodes.
+Then, for each node i we consider the neighbours j of i and we evaluate
+the gain of modularity that would take place by removing i from its
+community and by placing it in the community of j. The node i is then
+placed in the community for which this gain is maximum (in the case of
+a tie we use a breaking rule), but only if this gain is positive. If
+no positive gain is possible, i stays in its original community. This
+process is applied repeatedly and sequentially for all nodes until
+no further improvement can be achieved and the first phase is then
+complete.
+
+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.
+ 
+
 -}
 
 {-# LANGUAGE ConstrainedClassMethods #-}
@@ -20,24 +40,10 @@ module Data.Graph.Clustering.FLouvain
 import Protolude
 import Data.Graph.Inductive
 
-
-
--- Our algorithm is divided into two phases that are repeated
--- iteratively. Assume that we start with a weighted network of N nodes. First,
--- we assign a different community to each node of the network. So, in this
--- initial partition there are as many communities as there are nodes. Then, for
--- each node i we consider the neighbours j of i and we evaluate the gain of
--- modularity that would take place by removing i from its community and by
--- placing it in the community of j. The node i is then placed in the community
--- for which this gain is maximum (in the case of a tie we use a breaking rule),
--- but only if this gain is positive. If no positive gain is possible, i stays
--- in its original community. This process is applied repeatedly and
--- sequentially for all nodes until no further improvement can be achieved and
--- the first phase is then complete.
-
 --  "glue" : function to gather/merge communities
 --  "klue" : function to split communities
 data ClusteringMethod = Glue | Klue
+  deriving (Eq)
 
 
 -- | Find LNode of a node (i.e. a node with label)
@@ -48,11 +54,11 @@ lnode cgr n = case lab cgr n of
 
 -- We need to implement a fold over graph
 
-type CFoldFun a b c = c -> Context a b -> c
+type CFunFold a b c = Context a b -> c -> c
 
 xdfsFoldWith :: (Graph gr)
-    => CFun a b [Node]
-    -> CFoldFun a b c
+    => CFun     a b [Node]
+    -> CFunFold a b c
     -> c
     -> [Node]
     -> gr a b
@@ -60,7 +66,7 @@ xdfsFoldWith :: (Graph gr)
 xdfsFoldWith _ _ acc [] _            = acc
 xdfsFoldWith _ _ acc _ g | isEmpty g = acc
 xdfsFoldWith d f acc (v:vs) g = case match v g of
-                                 (Just c, g')  -> xdfsFoldWith d f (f acc c) (d c++vs) g'
+                                 (Just c, g')  -> xdfsFoldWith d f (f c acc) (d c++vs) g'
                                  (Nothing, g') -> xdfsFoldWith d f acc vs g'
 
 
@@ -82,10 +88,10 @@ iteration :: (Graph gr) => gr a b -> CGr -> CGr
 iteration gr cs = xdfsFoldWith suc' step cs (nodes gr) gr
 
 -- TODO Remember to filter out empty Communities
-step :: CFoldFun a b CGr
-step cgr (p, v, l, s) = cgr
+step :: CFunFold a b CGr
+step (p, v, l, s) cgr = cgr
   where
-    nc = nodeCommunity v cgr
+    nc  = nodeCommunity v cgr
     ncs = nodeNeighbours v cgr
     -- TODO Compute \Delta Q (gain of moving node v into Community C) which consists of:
     -- - Community WeightSum