def __init__(self, size, noOfEdges):
""" Constructs an empty graph
@param size Number of vertices to be considered for generation
@param noOfEdges Number of edges to generate
"""
NumberedEdgeUndirectedGraph.__init__(self)
## Number of vertices to be considered for generation
self.graphSize = size
## Number of edges to generate
self.noOfEdges = noOfEdges
## Parameters of the RMAT algorithm. Decide the probability with which quadrants in an adjacency matrix are chosen
## \todo Add description about choosing these probabilities
## Probability of choosing quadrant A
self.probA = 0.45
## Probability of choosing quadrant B
self.probB = 0.15
## Probability of choosing quadrant C
self.probC = 0.15
## Probability of choosing quadrant D
self.probD = 0.25
## Temporary storage of edges. Maintained for achieving performance
self.serialEdgeList = []
## Debug flag
self.debug = 0
self.startVertX = 1
self.endVertX = size
self.startVertY = 1
self.endVertY = size
## Logger instance
self.logger = PyGelLogging().getLogger()
def __init__(self, size, noOfEdges):
""" Constructs an empty graph
@param size Number of vertices to be considered for generation
@param noOfEdges Number of edges to generate
"""
NumberedEdgeUndirectedGraph.__init__(self)
## Number of vertices to be considered for generation
self.graphSize = size
## Number of edges to generate
self.noOfEdges = noOfEdges
## Parameters of the RMAT algorithm. Decide the probability with which quadrants in an adjacency matrix are chosen
## \todo Add description about choosing these probabilities
## Probability of choosing quadrant A
self.probA = 0.45
## Probability of choosing quadrant B
self.probB = 0.15
## Probability of choosing quadrant C
self.probC = 0.15
## Probability of choosing quadrant D
self.probD = 0.25
## Temporary storage of edges. Maintained for achieving performance
self.serialEdgeList = []
## Debug flag
self.debug = 0
self.startVertX = 1
self.endVertX = size
self.startVertY = 1
self.endVertY = size
## Logger instance
self.logger = PyGelLogging().getLogger()
def __init__(self):
""" Constructs a numbered edge graph
"""
## Dictionary of edges, indexed by edge number
self.edgeIndex = {}
## Dictionary of vertices, indexed by vertex number
self.vertexIndex = {}
## Dictionary of vertices, indexed by parent
self.parentIndex = {}
## Dictionary of vertices and edge numbers, indexed by parent
self.parentEdgeIndex = {}
## Last edge number assigned
self.__lastEdgeNumber = -1
## Dictionary of degree counts. Used for efficiently computing degree distribution
self.__degreeCount = {}
## Logger instance
self.logger = PyGelLogging().getLogger()
class UndirectedPowerLawRandomGraph(NumberedEdgeUndirectedGraph):
""" Generates a synthetic Web graph or Power Law graph using an RMAT algorithm
\ingroup RandomGraphs
"""
def __init__(self, size, noOfEdges):
""" Constructs an empty graph
@param size Number of vertices to be considered for generation
@param noOfEdges Number of edges to generate
"""
NumberedEdgeUndirectedGraph.__init__(self)
## Number of vertices to be considered for generation
self.graphSize = size
## Number of edges to generate
self.noOfEdges = noOfEdges
## Parameters of the RMAT algorithm. Decide the probability with which quadrants in an adjacency matrix are chosen
## \todo Add description about choosing these probabilities
## Probability of choosing quadrant A
self.probA = 0.45
## Probability of choosing quadrant B
self.probB = 0.15
## Probability of choosing quadrant C
self.probC = 0.15
## Probability of choosing quadrant D
self.probD = 0.25
## Temporary storage of edges. Maintained for achieving performance
self.serialEdgeList = []
## Debug flag
self.debug = 0
self.startVertX = 1
self.endVertX = size
self.startVertY = 1
self.endVertY = size
## Logger instance
self.logger = PyGelLogging().getLogger()
def setProbs(self, probA, probB, probC, probD):
""" Sets the probability with which quadrants in an adjacency matrix are chosen
@param probA Probability of choosing quadrant A
@param probB Probability of choosing quadrant B
@param probC Probability of choosing quadrant C
@param probD Probability of choosing quadrant D
@throws PackageExceptions::DistError
"""
if probA + probB + probC + probD != 1:
raise DistError(ErrorMessages.distAddOne)
self.probA = probA
self.probB = probB
self.probC = probC
self.probD = probD
return
def generate(self, noOfThreads, noSelfLoops):
""" Generates a the graph. Heart of web graph generation algorithm. Each thread gets an equal number of nodes to generate.
@param noOfThreads Number of threads to spawn for the graph generation. More threads does not correspond to fast generation
@param noSelfLoops If true (set to 1) self loops are discarded in the resulting graph.
"""
chooserThreads = []
for i in range(noOfThreads):
chooser = ChooseEdges(self.noOfEdges / noOfThreads, noSelfLoops,
self.startVertX, self.endVertX,
self.startVertY, self.endVertY, self.probA,
self.probB, self.probC, self.probD)
chooserThreads.append(chooser)
chooser.start()
for t in chooserThreads:
t.join()
self.serialEdgeList = ChooseEdges.serialEdgeList
del chooserThreads
del ChooseEdges.serialEdgeList
return
def populate(self):
""" Populate graph with edges generated after a call to DirectedPowerLawRandomGraph::generate. You should call this method before you can use any of the non-overridden method in Graph::NumberedEdgeDirectedGraph
"""
serialEdgeList = self.serialEdgeList
for i in xrange(0, len(serialEdgeList) - 1, 2):
newEdge = Edge(Vertex(serialEdgeList[i]),
Vertex(serialEdgeList[i + 1]))
try:
## In an NumberedEdgeUndirectedGraph you cannot have two edges between a pair of vertices. Thus, here we just log this error
## and move on to the next iteration. Perhaps this is the only difference between the undirected and the directed Power Law random
## graph.
self.addEdge(newEdge)
except EdgeError, e:
self.logger.info(e.message)
class UndirectedPowerLawRandomGraph(NumberedEdgeUndirectedGraph):
""" Generates a synthetic Web graph or Power Law graph using an RMAT algorithm
\ingroup RandomGraphs
"""
def __init__(self, size, noOfEdges):
""" Constructs an empty graph
@param size Number of vertices to be considered for generation
@param noOfEdges Number of edges to generate
"""
NumberedEdgeUndirectedGraph.__init__(self)
## Number of vertices to be considered for generation
self.graphSize = size
## Number of edges to generate
self.noOfEdges = noOfEdges
## Parameters of the RMAT algorithm. Decide the probability with which quadrants in an adjacency matrix are chosen
## \todo Add description about choosing these probabilities
## Probability of choosing quadrant A
self.probA = 0.45
## Probability of choosing quadrant B
self.probB = 0.15
## Probability of choosing quadrant C
self.probC = 0.15
## Probability of choosing quadrant D
self.probD = 0.25
## Temporary storage of edges. Maintained for achieving performance
self.serialEdgeList = []
## Debug flag
self.debug = 0
self.startVertX = 1
self.endVertX = size
self.startVertY = 1
self.endVertY = size
## Logger instance
self.logger = PyGelLogging().getLogger()
def setProbs(self, probA, probB, probC, probD):
""" Sets the probability with which quadrants in an adjacency matrix are chosen
@param probA Probability of choosing quadrant A
@param probB Probability of choosing quadrant B
@param probC Probability of choosing quadrant C
@param probD Probability of choosing quadrant D
@throws PackageExceptions::DistError
"""
if probA + probB + probC + probD != 1:
raise DistError(ErrorMessages.distAddOne)
self.probA = probA
self.probB = probB
self.probC = probC
self.probD = probD
return
def generate(self, noOfThreads, noSelfLoops):
""" Generates a the graph. Heart of web graph generation algorithm. Each thread gets an equal number of nodes to generate.
@param noOfThreads Number of threads to spawn for the graph generation. More threads does not correspond to fast generation
@param noSelfLoops If true (set to 1) self loops are discarded in the resulting graph.
"""
chooserThreads = []
for i in range(noOfThreads):
chooser = ChooseEdges(
self.noOfEdges / noOfThreads,
noSelfLoops,
self.startVertX,
self.endVertX,
self.startVertY,
self.endVertY,
self.probA,
self.probB,
self.probC,
self.probD,
)
chooserThreads.append(chooser)
chooser.start()
for t in chooserThreads:
t.join()
self.serialEdgeList = ChooseEdges.serialEdgeList
del chooserThreads
del ChooseEdges.serialEdgeList
return
def populate(self):
""" Populate graph with edges generated after a call to DirectedPowerLawRandomGraph::generate. You should call this method before you can use any of the non-overridden method in Graph::NumberedEdgeDirectedGraph
"""
serialEdgeList = self.serialEdgeList
for i in xrange(0, len(serialEdgeList) - 1, 2):
newEdge = Edge(Vertex(serialEdgeList[i]), Vertex(serialEdgeList[i + 1]))
try:
## In an NumberedEdgeUndirectedGraph you cannot have two edges between a pair of vertices. Thus, here we just log this error
## and move on to the next iteration. Perhaps this is the only difference between the undirected and the directed Power Law random
## graph.
self.addEdge(newEdge)
except EdgeError, e:
self.logger.info(e.message)
