def run(self):
self.graph, authorMap, conference, citationsPublications = SampleGraphUtility.constructSkewedCitationPublicationExample(
introduceRandomness=False
)
# Get the nodes we care about
authors = [
authorMap["Alice"],
authorMap["Bob"],
authorMap["Carol"],
authorMap["Dave"],
authorMap["Ed"],
authorMap["Frank"],
]
metaPathUtility = EdgeBasedMetaPathUtility()
# Output adjacency matrices
self.output("\nCPA Adjacency Matrix:")
cpaadjMatrix, nodesIndex = metaPathUtility.getAdjacencyMatrixFromGraph(
self.graph, [Conference, Paper, Author], project=True
)
adjMatrixTable = texttable.Texttable()
rows = [["Conference"] + [author.name for author in authors]]
rows += [[conference.name] + [cpaadjMatrix[nodesIndex[conference]][nodesIndex[author]] for author in authors]]
adjMatrixTable.add_rows(rows)
self.output(adjMatrixTable.draw())
self.output("\nCPPA Adjacency Matrix:")
cpaadjMatrix, nodesIndex = metaPathUtility.getAdjacencyMatrixFromGraph(
self.graph, [Conference, Paper, Paper, Author], project=True
)
adjMatrixTable = texttable.Texttable()
rows = [["Conference"] + [author.name for author in authors]]
rows += [[conference.name] + [cpaadjMatrix[nodesIndex[conference]][nodesIndex[author]] for author in authors]]
adjMatrixTable.add_rows(rows)
self.output(adjMatrixTable.draw())
# Total citation & publication counts
self.output("\nCitation & Publication Counts")
adjMatrixTable = texttable.Texttable()
rows = [["Measure"] + [author.name for author in authors]]
rows += [["Citations"] + [citationsPublications[author][0] for author in authors]]
rows += [["Publications"] + [citationsPublications[author][1] for author in authors]]
adjMatrixTable.add_rows(rows)
self.output(adjMatrixTable.draw())
# Output NeighborSim & PathSim similarity scores
neighborSimStrategy = NeighborSimStrategy(self.graph, [Conference, Paper, Author], symmetric=True)
self.outputSimilarityScores(authorMap, authors, neighborSimStrategy, "APCPA PathSim")
neighborSimStrategy = NeighborSimStrategy(self.graph, [Conference, Paper, Paper, Author])
self.outputSimilarityScores(authorMap, authors, neighborSimStrategy, "APPCPPA PathSim")
# Omit extra duplicate entry in path, and weight at different levels of 'relative'
for strategy, generalStrategyTitle in [
(FlattenedMatrixStrategy, "FlatMat"),
(VectorProductStrategy, "VectorProduct"),
]:
for w in [1.0, 0.5, 0]:
neighborPathShapeStrategy = strategy(
self.graph, weight=w, omit=[], metaPath=[Conference, Paper, Paper, Author], symmetric=True
)
strategyTitle = "APPCPPA %s ShapeSim (%1.2f weight)" % (generalStrategyTitle, w)
self.outputSimilarityScores(authorMap, authors, neighborPathShapeStrategy, strategyTitle)
w = 1.0
neighborPathShapeStrategy = VectorProductStrategy(
self.graph, weight=w, omit=[0], metaPath=[Conference, Paper, Paper, Author], symmetric=True
)
strategyTitle = "APPCPPA VectorProduct ShapeSim omitting CPC (%1.2f weight)" % w
self.outputSimilarityScores(authorMap, authors, neighborPathShapeStrategy, strategyTitle)
# Output recursive pathsim strategy score(s)
recursivePathSimStrategy = RecursivePathSimStrategy(self.graph, [Conference, Paper, Paper, Author])
self.outputSimilarityScores(authorMap, authors, recursivePathSimStrategy, "APPCPPA Recursive PathSim")
def run(self):
self.graph, authorMap, conference, citationsPublications = \
SampleGraphUtility.constructSkewedCitationPublicationExample(introduceRandomness=False)
# Get the nodes we care about
authors = [
authorMap['Alice'],
authorMap['Bob'],
authorMap['Carol'],
authorMap['Dave'],
authorMap['Ed'],
authorMap['Frank']
]
metaPathUtility = EdgeBasedMetaPathUtility()
# Output adjacency matrices
self.output('\nCPA Adjacency Matrix:')
cpaadjMatrix, nodesIndex = metaPathUtility.getAdjacencyMatrixFromGraph(
self.graph, [Conference, Paper, Author], project=True
)
adjMatrixTable = texttable.Texttable()
rows = [['Conference'] + [author.name for author in authors]]
rows += [[conference.name] + [cpaadjMatrix[nodesIndex[conference]][nodesIndex[author]] for author in authors]]
adjMatrixTable.add_rows(rows)
self.output(adjMatrixTable.draw())
self.output('\nCPPA Adjacency Matrix:')
cpaadjMatrix, nodesIndex = metaPathUtility.getAdjacencyMatrixFromGraph(
self.graph, [Conference, Paper, Paper, Author], project=True
)
adjMatrixTable = texttable.Texttable()
rows = [['Conference'] + [author.name for author in authors]]
rows += [[conference.name] + [cpaadjMatrix[nodesIndex[conference]][nodesIndex[author]] for author in authors]]
adjMatrixTable.add_rows(rows)
self.output(adjMatrixTable.draw())
# Total citation & publication counts
self.output('\nCitation & Publication Counts')
adjMatrixTable = texttable.Texttable()
rows = [['Measure'] + [author.name for author in authors]]
rows += [['Citations'] + [citationsPublications[author][0] for author in authors]]
rows += [['Publications'] + [citationsPublications[author][1] for author in authors]]
adjMatrixTable.add_rows(rows)
self.output(adjMatrixTable.draw())
# Output NeighborSim & PathSim similarity scores
neighborSimStrategy = NeighborSimStrategy(self.graph, [Conference, Paper, Author], symmetric=True)
self.outputSimilarityScores(authorMap, authors, neighborSimStrategy, 'APCPA PathSim')
neighborSimStrategy = NeighborSimStrategy(self.graph, [Conference, Paper, Paper, Author])
self.outputSimilarityScores(authorMap, authors, neighborSimStrategy, 'APPCPPA PathSim')
# Omit extra duplicate entry in path, and weight at different levels of 'relative'
for strategy, generalStrategyTitle in [(FlattenedMatrixStrategy, 'FlatMat'), (VectorProductStrategy, 'VectorProduct')]:
for w in [1.0, 0.5, 0]:
neighborPathShapeStrategy = strategy(
self.graph, weight=w, omit=[], metaPath=[Conference, Paper, Paper, Author], symmetric=True
)
strategyTitle = 'APPCPPA %s ShapeSim (%1.2f weight)' % (generalStrategyTitle, w)
self.outputSimilarityScores(authorMap, authors, neighborPathShapeStrategy, strategyTitle)
w = 1.0
neighborPathShapeStrategy = VectorProductStrategy(
self.graph, weight=w, omit=[0], metaPath=[Conference, Paper, Paper, Author], symmetric=True
)
strategyTitle = 'APPCPPA VectorProduct ShapeSim omitting CPC (%1.2f weight)' % w
self.outputSimilarityScores(authorMap, authors, neighborPathShapeStrategy, strategyTitle)
# Output recursive pathsim strategy score(s)
recursivePathSimStrategy = RecursivePathSimStrategy(self.graph, [Conference, Paper, Paper, Author])
self.outputSimilarityScores(authorMap, authors, recursivePathSimStrategy, 'APPCPPA Recursive PathSim')
def run(self):
self.graph, authorMap, conferenceMap = SampleGraphUtility.constructPathSimExampleThree()
# Get the nodes we care about
conferences = [
conferenceMap['SIGMOD'],
conferenceMap['VLDB'],
conferenceMap['ICDE'],
conferenceMap['KDD']
]
authors = [
authorMap['Mike'],
authorMap['Jim'],
authorMap['Mary'],
authorMap['Bob'],
authorMap['Ann'],
]
metaPathUtility = EdgeBasedMetaPathUtility()
self.output('\nAPC Adjacency Matrix:')
apcadjMatrix, nodesIndex = metaPathUtility.getAdjacencyMatrixFromGraph(self.graph, [Author, Paper, Conference], project=True)
adjMatrixTable = texttable.Texttable()
rows = [['Author'] + [conference.name for conference in conferences]]
rows += [[author.name] + [apcadjMatrix[nodesIndex[author]][nodesIndex[conference]] for conference in conferences] for author in authors]
adjMatrixTable.add_rows(rows)
self.output(adjMatrixTable.draw())
self.output('\nCPA Adjacency Matrix:')
cpaadjMatrix, dsad = metaPathUtility.getAdjacencyMatrixFromGraph(self.graph, [Conference, Paper, Author], project=True)
adjMatrixTable = texttable.Texttable()
rows = [['Conference'] + [author.name for author in authors]]
rows += [[conference.name] + [cpaadjMatrix[nodesIndex[conference]][nodesIndex[author]] for author in authors] for conference in conferences]
adjMatrixTable.add_rows(rows)
self.output(adjMatrixTable.draw())
self.output('\nAPCPA Adjacency Matrix (Computed):')
adjMatrix = numpy.dot(apcadjMatrix, cpaadjMatrix)
adjMatrixTable = texttable.Texttable()
rows = [['Author'] + [author.name for author in authors]]
rows += [[author.name] + [adjMatrix[nodesIndex[author]][nodesIndex[otherAuthor]] for otherAuthor in authors] for author in authors]
adjMatrixTable.add_rows(rows)
self.output(adjMatrixTable.draw())
# Output homogeneous simrank comparison
homogeneousSimRankStrategy = SimRankStrategy(self.graph)
self.outputSimilarityScores(authorMap, authors, homogeneousSimRankStrategy, 'Homogeneous SimRank')
projectedGraph = metaPathUtility.createHeterogeneousProjection(self.graph, [Author, Paper, Conference], symmetric = True)
# Output heterogeneous simrank comparison
heterogeneousSimRankStrategy = SimRankStrategy(projectedGraph)
self.outputSimilarityScores(authorMap, authors, heterogeneousSimRankStrategy, 'APC Heterogeneous SimRank')
# Output heterogeneous simrank w/ squared neighbors comparison
def sqNeighborsNorm(graph, a, b, sim):
aNeighbors, bNeighbors = graph.getPredecessors(a), graph.getPredecessors(b)
return float(len(aNeighbors)**2 * len(bNeighbors)**2)
heterogeneousSquaredSimRankStrategy = SimRankStrategy(projectedGraph, normalization=sqNeighborsNorm)
self.outputSimilarityScores(authorMap, authors, heterogeneousSquaredSimRankStrategy, 'Squared Heterogeneous SimRank')
# Output NeighborSim similarity scores
neighborSimStrategy = NeighborSimStrategy(self.graph, [Author, Paper, Conference], symmetric=True)
self.outputSimilarityScores(authorMap, authors, neighborSimStrategy, 'APC NeighborSim')
# Output the PathSim similarity scores
pathsimStrategy = PathSimStrategy(self.graph, [Author, Paper, Conference, Paper, Author], symmetric=True)
self.outputSimilarityScores(authorMap, authors, pathsimStrategy, 'APCPA PathSim')
def run(self):
self.graph, authorMap, conferenceMap = SampleGraphUtility.constructPathSimExampleThree(
)
# Get the nodes we care about
conferences = [
conferenceMap['SIGMOD'], conferenceMap['VLDB'],
conferenceMap['ICDE'], conferenceMap['KDD']
]
authors = [
authorMap['Mike'],
authorMap['Jim'],
authorMap['Mary'],
authorMap['Bob'],
authorMap['Ann'],
]
metaPathUtility = EdgeBasedMetaPathUtility()
self.output('\nAPC Adjacency Matrix:')
apcadjMatrix, nodesIndex = metaPathUtility.getAdjacencyMatrixFromGraph(
self.graph, [Author, Paper, Conference], project=True)
adjMatrixTable = texttable.Texttable()
rows = [['Author'] + [conference.name for conference in conferences]]
rows += [[author.name] + [
apcadjMatrix[nodesIndex[author]][nodesIndex[conference]]
for conference in conferences
] for author in authors]
adjMatrixTable.add_rows(rows)
self.output(adjMatrixTable.draw())
self.output('\nCPA Adjacency Matrix:')
cpaadjMatrix, dsad = metaPathUtility.getAdjacencyMatrixFromGraph(
self.graph, [Conference, Paper, Author], project=True)
adjMatrixTable = texttable.Texttable()
rows = [['Conference'] + [author.name for author in authors]]
rows += [[conference.name] + [
cpaadjMatrix[nodesIndex[conference]][nodesIndex[author]]
for author in authors
] for conference in conferences]
adjMatrixTable.add_rows(rows)
self.output(adjMatrixTable.draw())
self.output('\nAPCPA Adjacency Matrix (Computed):')
adjMatrix = numpy.dot(apcadjMatrix, cpaadjMatrix)
adjMatrixTable = texttable.Texttable()
rows = [['Author'] + [author.name for author in authors]]
rows += [[author.name] + [
adjMatrix[nodesIndex[author]][nodesIndex[otherAuthor]]
for otherAuthor in authors
] for author in authors]
adjMatrixTable.add_rows(rows)
self.output(adjMatrixTable.draw())
# Output homogeneous simrank comparison
homogeneousSimRankStrategy = SimRankStrategy(self.graph)
self.outputSimilarityScores(authorMap, authors,
homogeneousSimRankStrategy,
'Homogeneous SimRank')
projectedGraph = metaPathUtility.createHeterogeneousProjection(
self.graph, [Author, Paper, Conference], symmetric=True)
# Output heterogeneous simrank comparison
heterogeneousSimRankStrategy = SimRankStrategy(projectedGraph)
self.outputSimilarityScores(authorMap, authors,
heterogeneousSimRankStrategy,
'APC Heterogeneous SimRank')
# Output heterogeneous simrank w/ squared neighbors comparison
def sqNeighborsNorm(graph, a, b, sim):
aNeighbors, bNeighbors = graph.getPredecessors(
a), graph.getPredecessors(b)
return float(len(aNeighbors)**2 * len(bNeighbors)**2)
heterogeneousSquaredSimRankStrategy = SimRankStrategy(
projectedGraph, normalization=sqNeighborsNorm)
self.outputSimilarityScores(authorMap, authors,
heterogeneousSquaredSimRankStrategy,
'Squared Heterogeneous SimRank')
# Output NeighborSim similarity scores
neighborSimStrategy = NeighborSimStrategy(self.graph,
[Author, Paper, Conference],
symmetric=True)
self.outputSimilarityScores(authorMap, authors, neighborSimStrategy,
'APC NeighborSim')
# Output the PathSim similarity scores
pathsimStrategy = PathSimStrategy(
self.graph, [Author, Paper, Conference, Paper, Author],
symmetric=True)
self.outputSimilarityScores(authorMap, authors, pathsimStrategy,
'APCPA PathSim')
