def run(self):
citationsPublications = {
'Alice': (100, 10),
'Bob': (80, 10),
'Carol': (100, 100),
'Dave': (10, 10),
'Ed': (20, 5)
}
self.graph, authorMap, conference, citationsPublications = \
SampleGraphUtility.constructSkewedCitationPublicationExample(
introduceRandomness=False, citationsPublicationsParameter=citationsPublications
)
# Get the nodes we care about
authors = [
authorMap['Alice'],
authorMap['Bob'],
authorMap['Carol'],
authorMap['Dave'],
authorMap['Ed']
]
# 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 PathSim similarity scores
pathSimStrategyPubs = NeighborSimStrategy(self.graph, [Conference, Paper, Author], symmetric=True)
self.outputSimilarityScores(authorMap, authors, pathSimStrategyPubs, 'APCPA PathSim')
pathSimStrategyCits = NeighborSimStrategy(self.graph, [Conference, Paper, Paper, Author])
self.outputSimilarityScores(authorMap, authors, pathSimStrategyCits, 'APPCPPA PathSim')
for w1, w2 in [(0.5, 0.5), (0.6, 0.4), (0.4, 0.6), (0.7, 0.3), (0.3, 0.7)]:
combinedPathSimStrategy = AggregateSimilarityStrategy(
self.graph, [pathSimStrategyPubs, pathSimStrategyCits], [w1, w2]
)
self.outputSimilarityScores(
authorMap, authors, combinedPathSimStrategy, 'APCPA-APPCPPAA Pathsim (%1.1f,%1.1f)' % (w1, w2)
)
# Output ShapeSim strategy
w = 1.0
neighborPathShapeStrategy = VectorProductStrategy(
self.graph, weight=w, omit=[], metaPath=[Conference, Paper, Paper, Author], symmetric=True
)
strategyTitle = 'APPCPPA %s ShapeSim (%1.2f weight)' % ('VectorProduct', w)
self.outputSimilarityScores(authorMap, authors, neighborPathShapeStrategy, strategyTitle)
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):
citationsPublications = {
'Alice': (100, 10),
'Bob': (80, 10),
'Carol': (100, 100),
'Dave': (10, 10),
'Ed': (20, 5)
}
self.graph, authorMap, conference, citationsPublications = \
SampleGraphUtility.constructSkewedCitationPublicationExample(
introduceRandomness=False, citationsPublicationsParameter=citationsPublications
)
# Get the nodes we care about
authors = [
authorMap['Alice'], authorMap['Bob'], authorMap['Carol'],
authorMap['Dave'], authorMap['Ed']
]
# 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 PathSim similarity scores
pathSimStrategyPubs = NeighborSimStrategy(self.graph,
[Conference, Paper, Author],
symmetric=True)
self.outputSimilarityScores(authorMap, authors, pathSimStrategyPubs,
'APCPA PathSim')
pathSimStrategyCits = NeighborSimStrategy(
self.graph, [Conference, Paper, Paper, Author])
self.outputSimilarityScores(authorMap, authors, pathSimStrategyCits,
'APPCPPA PathSim')
for w1, w2 in [(0.5, 0.5), (0.6, 0.4), (0.4, 0.6), (0.7, 0.3),
(0.3, 0.7)]:
combinedPathSimStrategy = AggregateSimilarityStrategy(
self.graph, [pathSimStrategyPubs, pathSimStrategyCits],
[w1, w2])
self.outputSimilarityScores(
authorMap, authors, combinedPathSimStrategy,
'APCPA-APPCPPAA Pathsim (%1.1f,%1.1f)' % (w1, w2))
# Output ShapeSim strategy
w = 1.0
neighborPathShapeStrategy = VectorProductStrategy(
self.graph,
weight=w,
omit=[],
metaPath=[Conference, Paper, Paper, Author],
symmetric=True)
strategyTitle = 'APPCPPA %s ShapeSim (%1.2f weight)' % (
'VectorProduct', w)
self.outputSimilarityScores(authorMap, authors,
neighborPathShapeStrategy, strategyTitle)
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')
