class MetaPathSimilarityStrategy(SimilarityStrategy):
"""
Generic class for similarity strategies that use meta paths
"""
def __init__(self, graph, metaPath = None, symmetric = False, conserveMemory = False):
"""
Constructs a meta path similarity strategy, storing the meta path data for this strategy.
@param metaPath Meta path object where the list of classes contains classes of nodes in the graph,
and weights in [0,1] containing the importance of the meta path
@param symmetric Whether or not to enforce that meta paths must be symmetric
For example, if 'symmetric' and 'evenLength' are both 'true', for meta path 'ABC', we will only count meta
path 'ABCCBA', depending on, and if 'symmetric' is 'true' while 'evenLength' is 'false', we will only count
meta paths 'ABCBA'
"""
super(MetaPathSimilarityStrategy, self).__init__(graph)
self.metaPath = metaPath
self.symmetric = symmetric
self.conserveMemory = conserveMemory
self.metaPathUtility = EdgeBasedMetaPathUtility()
def findMostSimilarNodes(self, source, number=None, conserveMemory=False):
"""
Simple find the similarity scores between this node and all reachable nodes on this meta path. Note that if
there are fewer reachable nodes than "number", the number of reachable nodes will be returned.
"""
# If no number is provided, default to the number of nodes in the graph
if number is None:
number = self.n
# Get similarity scores for all entries
reachableNodes = self.metaPathUtility.findMetaPathNeighbors(self.graph, source, self.metaPath)
for reachableNode in reachableNodes:
self.similarityScores[source][reachableNode] = self.findSimilarityScore(source, reachableNode)
# Sort by increasing score
mostSimilarNodes = sorted(self.similarityScores[source].iteritems(), key=operator.itemgetter(1))
# Remove source, nodes of different types, and reverse
newMostSimilarNodes = []
for node, score in mostSimilarNodes:
if node != source and node.__class__ == source.__class__:
newMostSimilarNodes.append(node)
newMostSimilarNodes.reverse()
number = min([number, len(newMostSimilarNodes)])
mostSimilarNodes = newMostSimilarNodes[:number]
return mostSimilarNodes
class MetaPathSimilarityStrategy(SimilarityStrategy):
"""
Generic class for similarity strategies that use meta paths
"""
def __init__(self,
graph,
metaPath=None,
symmetric=False,
conserveMemory=False):
"""
Constructs a meta path similarity strategy, storing the meta path data for this strategy.
@param metaPath Meta path object where the list of classes contains classes of nodes in the graph,
and weights in [0,1] containing the importance of the meta path
@param symmetric Whether or not to enforce that meta paths must be symmetric
For example, if 'symmetric' and 'evenLength' are both 'true', for meta path 'ABC', we will only count meta
path 'ABCCBA', depending on, and if 'symmetric' is 'true' while 'evenLength' is 'false', we will only count
meta paths 'ABCBA'
"""
super(MetaPathSimilarityStrategy, self).__init__(graph)
self.metaPath = metaPath
self.symmetric = symmetric
self.conserveMemory = conserveMemory
self.metaPathUtility = EdgeBasedMetaPathUtility()
def findMostSimilarNodes(self, source, number=None, conserveMemory=False):
"""
Simple find the similarity scores between this node and all reachable nodes on this meta path. Note that if
there are fewer reachable nodes than "number", the number of reachable nodes will be returned.
"""
# If no number is provided, default to the number of nodes in the graph
if number is None:
number = self.n
# Get similarity scores for all entries
reachableNodes = self.metaPathUtility.findMetaPathNeighbors(
self.graph, source, self.metaPath)
for reachableNode in reachableNodes:
self.similarityScores[source][
reachableNode] = self.findSimilarityScore(
source, reachableNode)
# Sort by increasing score
mostSimilarNodes = sorted(self.similarityScores[source].iteritems(),
key=operator.itemgetter(1))
# Remove source, nodes of different types, and reverse
newMostSimilarNodes = []
for node, score in mostSimilarNodes:
if node != source and node.__class__ == source.__class__:
newMostSimilarNodes.append(node)
newMostSimilarNodes.reverse()
number = min([number, len(newMostSimilarNodes)])
mostSimilarNodes = newMostSimilarNodes[:number]
return mostSimilarNodes
