{-| Implementations of binomially random graphs, as described by Erdős and Rényi. Graphs generated using this method have a constant edge probability between two nodes. See Erdős and A. Rényi, On Random Graphs, Publ. Math. 6, 290 (1959). graph-generators copyright: Copyright (C) 2014 Uli Köhler NetworkX copyright: Copyright (C) 2004-2010 by Aric Hagberg <hagberg@lanl.gov> Dan Schult <dschult@colgate.edu> Pieter Swart <swart@lanl.gov> All rights reserved. BSD license. -} module Data.Graph.Generators.Random.ErdosRenyi ( -- ** Graph generators erdosRenyiGraph, erdosRenyiGraph', -- ** Graph component generators erdosRenyiContext, -- ** Utility functions selectWithProbability ) where import System.Random.MWC import Control.Monad import Data.Graph.Generators import Control.Applicative ((<$>)) {-| Generate a unlabelled context using the Erdős and Rényi method. See 'erdosRenyiGraph' for a detailed algorithm description. Example usage, using a truly random generator: > import System.Random.MWC > gen <- withSystemRandom . asGenIO $ return > -} erdosRenyiContext :: GenIO -- ^ The random number generator to use -> Int -- ^ Identifier of the context's central node -> [Int] -- ^ The algorithm will generate random edges to those nodes -- from or to the given node -> Double -- ^ The probability for any pair of nodes to be connected -> IO GraphContext -- ^ The resulting graph (IO required for randomness) erdosRenyiContext gen n allNodes p = do let endpoints = selectWithProbability gen p allNodes inEdges <- endpoints outEdges <- endpoints return $ GraphContext inEdges n outEdges {-| Generate a unlabelled directed random graph using the Algorithm introduced by Erdős and Rényi, also called a binomial random graph generator. Note that self-loops with also be generated with probability p. This algorithm runs in O(n²) and is best suited for non-sparse networks. The generated nodes are identified by [0..n-1]. Example usage, using a truly random generator: > import System.Random.MWC > gen <- withSystemRandom . asGenIO $ return > erdosRenyiGraph 10 0.1 ... Modelled after NetworkX 1.8.1 erdos_renyi_graph(). -} erdosRenyiGraph :: GenIO -- ^ The random number generator to use -> Int -- ^ The number of nodes -> Double -- ^ The probability for any pair of nodes to be connected -> IO GraphInfo -- ^ The resulting graph (IO required for randomness) erdosRenyiGraph gen n p = do let allNodes = [0..n-1] -- Outgoing edge targets for any node let outgoingEdgeTargets = selectWithProbability gen p allNodes -- Outgoing edge tuples for a single nodes let singleNodeEdges node = zip (repeat node) <$> outgoingEdgeTargets allEdges <- concat <$> mapM singleNodeEdges allNodes return $ GraphInfo n allEdges {-| Like 'erdosRenyiGraph', but uses a newly initialized random number generator. See 'System.Random.MWC.withSystemRandom' for details on how the generator is initialized. By using this function, you don't have to initialize the generator by yourself, however generator initialization is slow, so reusing the generator is recommended. Usage example: > erdosRenyiGraph' 10 0.1 -} erdosRenyiGraph' :: Int -- ^ The number of nodes -> Double -- ^ The probability for any pair of nodes to be connected -> IO GraphInfo -- ^ The resulting graph (IO required for randomness) erdosRenyiGraph' n p = withSystemRandom . asGenIO $ \gen -> erdosRenyiGraph gen n p {-| Filter a list by selecting each list element uniformly with a given probability p Although this is mainly used internally, it can be used as general utility function -} selectWithProbability :: GenIO -- ^ The random generator state -> Double -- ^ The probability to select each list element -> [a] -- ^ The list to filter -> IO [a] -- ^ The filtered list selectWithProbability _ _ [] = return [] selectWithProbability gen p (x:xs) = do r <- uniform gen :: IO Double let v = [x | r <= p] liftM2 (++) (return v) $ selectWithProbability gen p xs