def testFindAllSimilarityFromNodeOnPathSimExampleThree(self):
"""
Tests similarity for all other nodes given a single node, using example 3 from PathSim paper
"""
graph, authorMap, conferenceMap = SampleGraphUtility.constructPathSimExampleThree()
metaPath = [Author, Paper, Conference, Paper, Author]
strategy = PathSimStrategy(graph, metaPath)
mike = authorMap['Mike']
mostSimilarNodes = strategy.findMostSimilarNodes(mike, 5)
self.assertEquals([authorMap['Bob'], authorMap['Mary'], authorMap['Jim']], mostSimilarNodes)
def testFindAllSimilarityFromNodeOnPathSimExampleThree(self):
"""
Tests similarity for all other nodes given a single node, using example 3 from PathSim paper
"""
graph, authorMap, conferenceMap = SampleGraphUtility.constructPathSimExampleThree(
)
metaPath = [Author, Paper, Conference, Paper, Author]
strategy = PathSimStrategy(graph, metaPath)
mike = authorMap['Mike']
mostSimilarNodes = strategy.findMostSimilarNodes(mike, 5)
self.assertEquals(
[authorMap['Bob'], authorMap['Mary'], authorMap['Jim']],
mostSimilarNodes)
def testFindSingleSimilarityPathSimExampleThree(self):
"""
Tests pairwise similarity for nodes, using example 3 from PathSim paper (compute similarity scores from Mike)
"""
graph, authorMap, conferenceMap = SampleGraphUtility.constructPathSimExampleThree()
metaPath = [Author, Paper, Conference, Paper, Author]
strategy = PathSimStrategy(graph, metaPath)
mike = authorMap['Mike']
jimScore, maryScore, bobScore, annScore = strategy.findSimilarityScores(
mike, [authorMap['Jim'], authorMap['Mary'], authorMap['Bob'], authorMap['Ann']]
)
self.assertEquals(bobScore, max([jimScore, maryScore, bobScore, annScore]))
self.assertEquals(annScore, 0)
def testFindSingleSimilarityPathSimExampleThree(self):
"""
Tests pairwise similarity for nodes, using example 3 from PathSim paper (compute similarity scores from Mike)
"""
graph, authorMap, conferenceMap = SampleGraphUtility.constructPathSimExampleThree(
)
metaPath = [Author, Paper, Conference, Paper, Author]
strategy = PathSimStrategy(graph, metaPath)
mike = authorMap['Mike']
jimScore, maryScore, bobScore, annScore = strategy.findSimilarityScores(
mike, [
authorMap['Jim'], authorMap['Mary'], authorMap['Bob'],
authorMap['Ann']
])
self.assertEquals(bobScore,
max([jimScore, maryScore, bobScore, annScore]))
self.assertEquals(annScore, 0)
def run(self):
strategy = PathSimStrategy(
self.graph, [Conference, Paper, Author, Paper, Conference], True)
experimentHelper = LabeledExperimentHelper(
os.path.join('data', 'dbis', 'query_label', 'PathSim'))
conferenceQueryNames = [
'SIGMOD Conference', 'VLDB', 'ICDE', 'PODS', 'EDBT', 'DASFAA',
'KDD', 'ICDM', 'PKDD', 'SDM', 'PAKDD', 'WWW', 'SIGIR', 'TREC',
'APWeb'
]
for conferenceQueryName in conferenceQueryNames:
conferences = experimentHelper.getNodesByAttribute(
self.graph, 'name', conferenceQueryName)
assert (len(conferences) == 1)
target = list(conferences)[0]
number = 10
# Output the top ten most similar conferences on the CPAPC meta path
self.output(
'\n\nTop Ten Similar Conferences to %s (CPAPC meta path):' %
conferenceQueryName)
mostSimilarNodes = strategy.findMostSimilarNodes(target, number)
apaPathTable = texttable.Texttable()
headerRow = [['Rank', 'Conference', 'Relevance']]
dataRows = [[
i + 1, mostSimilarNodes[i].name,
experimentHelper.getLabelForNode(target, mostSimilarNodes[i])
] for i in xrange(0, number)]
apaPathTable.add_rows(headerRow + dataRows)
self.output(apaPathTable.draw())
# Output the nDCG for these results
self.output(
'%1.3f' %
CumulativeGainMeasures.normalizedDiscountedCumulativeGain(
target, mostSimilarNodes,
experimentHelper.labelDictionary))
def run(self):
strategy = PathSimStrategy(self.graph, [Conference, Paper, Author, Paper, Conference], True)
experimentHelper = LabeledExperimentHelper(os.path.join('data', 'dbis', 'query_label', 'PathSim'))
conferenceQueryNames = [
'SIGMOD Conference',
'VLDB',
'ICDE',
'PODS',
'EDBT',
'DASFAA',
'KDD',
'ICDM',
'PKDD',
'SDM',
'PAKDD',
'WWW',
'SIGIR',
'TREC',
'APWeb'
]
for conferenceQueryName in conferenceQueryNames:
conferences = experimentHelper.getNodesByAttribute(self.graph, 'name', conferenceQueryName)
assert(len(conferences) == 1)
target = list(conferences)[0]
number = 10
# Output the top ten most similar conferences on the CPAPC meta path
self.output('\n\nTop Ten Similar Conferences to %s (CPAPC meta path):' % conferenceQueryName)
mostSimilarNodes = strategy.findMostSimilarNodes(target, number)
apaPathTable = texttable.Texttable()
headerRow = [['Rank', 'Conference', 'Relevance']]
dataRows = [[i + 1, mostSimilarNodes[i].name, experimentHelper.getLabelForNode(target, mostSimilarNodes[i])] for i in xrange(0, number)]
apaPathTable.add_rows(headerRow + dataRows)
self.output(apaPathTable.draw())
# Output the nDCG for these results
self.output('%1.3f' % CumulativeGainMeasures.normalizedDiscountedCumulativeGain(target, mostSimilarNodes, experimentHelper.labelDictionary))
def run(self):
citationMap = {
'Mike': {
'Mike': 0,
'Jim': 0,
'Mary': 0,
'Bob': 0,
'Ann': 0,
'Joe': 0,
'Nancy': 0
},
'Jim': {
'Mike': 20,
'Jim': 0,
'Mary': 20,
'Bob': 20,
'Ann': 0,
'Joe': 20,
'Nancy': 0
},
'Mary': {
'Mike': 1,
'Jim': 10,
'Mary': 0,
'Bob': 1,
'Ann': 0,
'Joe': 1,
'Nancy': 0
},
'Bob': {
'Mike': 1,
'Jim': 10,
'Mary': 1,
'Bob': 0,
'Ann': 0,
'Joe': 1,
'Nancy': 0
},
'Ann': {
'Mike': 0,
'Jim': 0,
'Mary': 0,
'Bob': 0,
'Ann': 0,
'Joe': 0,
'Nancy': 0
},
'Joe': {
'Mike': 0,
'Jim': 0,
'Mary': 0,
'Bob': 0,
'Ann': 0,
'Joe': 0,
'Nancy': 0
},
'Nancy': {
'Mike': 1,
'Jim': 10,
'Mary': 1,
'Bob': 1,
'Ann': 0,
'Joe': 1,
'Nancy': 0
}
}
self.graph, authorMap, conferenceMap =\
SampleGraphUtility.constructPathSimExampleThree(extraAuthorsAndCitations=True, citationMap = citationMap)
# 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'],
authorMap['Joe'],
authorMap['Nancy'],
]
metaPathUtility = EdgeBasedMetaPathUtility()
# Project a 2-typed heterogeneous graph over adapted PathSim example
publicationProjectedGraph = metaPathUtility.createHeterogeneousProjection(
self.graph, [Author, Paper, Conference], symmetric=True)
self.output('\nAdjacency Matrix (Projected):')
adjMatrixTable = texttable.Texttable()
rows = [['Author'] + [conference.name for conference in conferences]]
rows += [[author.name] + [
publicationProjectedGraph.getNumberOfEdges(author, conference)
for conference in conferences
] for author in authors]
adjMatrixTable.add_rows(rows)
self.output(adjMatrixTable.draw())
# Project a homogeneous citation graph over adapted PathSim example
citationProjectedGraph = metaPathUtility.createHomogeneousProjection(
self.graph, [Author, Paper, Paper, Author])
self.output('\nCitation Matrix:')
adjMatrixTable = texttable.Texttable()
rows = [['Author'] + [author.name for author in authors]]
rows += [[author.name] + [
citationProjectedGraph.getNumberOfEdges(author, otherAuthor)
for otherAuthor in authors
] for author in authors]
adjMatrixTable.add_rows(rows)
self.output(adjMatrixTable.draw())
# Output total out/in citations
self.output('\nCitations Total:')
totalCitationsTable = texttable.Texttable()
rows = [['Author', 'In', 'Out']]
for author in authors:
inCount = sum(
citationProjectedGraph.getNumberOfEdges(otherAuthor, author)
for otherAuthor in authors)
outCount = sum(
citationProjectedGraph.getNumberOfEdges(author, otherAuthor)
for otherAuthor in authors)
rows += [[author.name, inCount, outCount]]
totalCitationsTable.add_rows(rows)
self.output(totalCitationsTable.draw())
# Get PathSim similarity scores
pathSimStrategy = PathSimStrategy(
self.graph, [Author, Paper, Conference, Paper, Author], True)
self.outputSimilarityScores(authorMap, authors, pathSimStrategy,
'APCPA PathSim')
# Output SimRank-related scores
strategy = SimRankStrategy(self.graph, [Author, Paper, Paper, Author],
symmetric=True)
self.outputSimilarityScores(authorMap, authors, strategy, "SimRank")
# Output the projected PageRank/HITS similarity scores
for name, algorithm in zip(
['PageRank', 'HITS'],
[PageRankDistanceStrategy, HITSDistanceStrategy]):
strategy = algorithm(self.graph, [Author, Paper, Paper, Author],
symmetric=True)
self.outputSimilarityScores(authorMap, authors, strategy,
"%s-Distance" % name)
# Get NeighborSim similarity scores
inNeighborSimStrategy = NeighborSimStrategy(
self.graph, [Author, Paper, Paper, Author])
self.outputSimilarityScores(authorMap, authors, inNeighborSimStrategy,
'APPA NeighborSim-In')
outNeighborSimStrategy = NeighborSimStrategy(
self.graph, [Author, Paper, Paper, Author],
reversed=True,
smoothed=True)
self.outputSimilarityScores(authorMap, authors, outNeighborSimStrategy,
'APPA NeighborSim-Out')
# Combined best PR-distance algorithm
simRankStrategy = SimRankStrategy(self.graph,
[Author, Paper, Paper, Author],
symmetric=True)
simRank = AggregateSimilarityStrategy(
self.graph, [pathSimStrategy, simRankStrategy], [0.5, 0.5])
self.outputSimilarityScores(authorMap, authors, simRank,
'APCPA Pathsim, APPA SimRank')
# Combined best neighborsim score
combinedNeighborSim = AggregateSimilarityStrategy(
self.graph,
[pathSimStrategy, inNeighborSimStrategy, outNeighborSimStrategy],
[0.6, 0.2, 0.2])
self.outputSimilarityScores(
authorMap, authors, combinedNeighborSim,
'APCPA Pathsim, APPA NeighborSim-Combined')
def run(self):
self.graph, authorMap, conferenceMap, totalCitationCount = SampleGraphUtility.constructMultiDisciplinaryAuthorExample(indirectAuthor = True)
# Get the nodes we care about
conferences = [
conferenceMap['VLDB'],
conferenceMap['KDD']
]
authors = [
authorMap['A'],
authorMap['B'],
authorMap['C'],
authorMap['D'],
authorMap['E'],
authorMap['F'],
authorMap['G'],
authorMap['H'],
authorMap['I'],
authorMap['J'],
]
self.metaPathUtility = EdgeBasedMetaPathUtility()
# Build homogeneous projection of network (authors, with edges for times authors cite each other)
projectedGraph = self.metaPathUtility.createHomogeneousProjection(self.graph, [Author, Paper, Paper, Author])
authorCitationCounts = {}
for author in projectedGraph.getNodes():
authorCitationCounts[author] = {}
for otherAuthor in projectedGraph.getNodes():
authorCitationCounts[author][otherAuthor] = projectedGraph.getNumberOfEdges(author, otherAuthor)
# Output the adjacency matrix for authors-authors in the graph
self.output('\nCitation Matrix:')
adjMatrixTable = texttable.Texttable()
rows = [['Author'] + [author.name for author in authors]]
rows += [[author.name] + [authorCitationCounts[author][otherAuthor] for otherAuthor in authors] for author in authors]
adjMatrixTable.add_rows(rows)
self.output(adjMatrixTable.draw())
# Output the adjacency matrix for authors & conferences in the graph
self.output('\nAdjacency Matrix:')
adjMatrixTable = texttable.Texttable()
projectedGraph = self.metaPathUtility.createHeterogeneousProjection(self.graph, [Author, Paper, Conference])
rows = [[''] + [conference.name for conference in conferences]]
rows += [[author.name] + [projectedGraph.getNumberOfEdges(author, conference) for conference in conferences] for author in authors]
adjMatrixTable.add_rows(rows)
self.output(adjMatrixTable.draw())
# Output total citation counts
self.output('\nTotal Citation Counts:')
rows = [[author.name for author in authors],['%d' % totalCitationCount[author.name] for author in authors]]
citationCountTable = texttable.Texttable()
citationCountTable.add_rows(rows)
self.output(citationCountTable.draw())
# Output the PathSim similarity scores
pathsimStretegy = PathSimStrategy(self.graph, [Author, Paper, Conference, Paper, Author], True)
self.outputSimilarityScores(authorMap, authors, pathsimStretegy, "PathSim")
# Output the NeighborSim similarity scores
neighborsimStrategy = NeighborSimStrategy(self.graph, [Conference, Paper, Paper, Author])
self.outputSimilarityScores(authorMap, authors, neighborsimStrategy, "NeighborSim (CPPA)")
# Output the NeighborSim similarity scores
neighborsimStrategy = NeighborSimStrategy(self.graph, [Author, Paper, Paper, Author])
self.outputSimilarityScores(authorMap, authors, neighborsimStrategy, "NeighborSim (APPA)")
# Constant weight propagation strategy
propagatedNeighborsimStrategy = NeighborSimConstantPropagationStrategy(self.graph, [Author, Paper, Paper, Author], iterations = 2)
self.outputSimilarityScores(authorMap, authors, propagatedNeighborsimStrategy, "NeighborSim-ConstantPropagation-2")
propagatedNeighborsimStrategy = NeighborSimConstantPropagationStrategy(self.graph, [Author, Paper, Paper, Author], iterations = 3)
self.outputSimilarityScores(authorMap, authors, propagatedNeighborsimStrategy, "NeighborSim-ConstantPropagation-3")
propagatedNeighborsimStrategy = NeighborSimConstantPropagationStrategy(self.graph, [Author, Paper, Paper, Author], iterations = 4)
self.outputSimilarityScores(authorMap, authors, propagatedNeighborsimStrategy, "NeighborSim-ConstantPropagation-4")
propagatedNeighborsimStrategy = NeighborSimConstantPropagationStrategy(self.graph, [Author, Paper, Paper, Author], iterations = 50)
self.outputSimilarityScores(authorMap, authors, propagatedNeighborsimStrategy, "NeighborSim-ConstantPropagation-50")
# Preferential attachment propagation strategy
propagatedNeighborsimStrategy = NeighborSimConstantPreferentialAttachmentStrategy(self.graph, [Author, Paper, Paper, Author], iterations = 2)
self.outputSimilarityScores(authorMap, authors, propagatedNeighborsimStrategy, "NeighborSim-WeightedPropagation-2")
propagatedNeighborsimStrategy = NeighborSimConstantPreferentialAttachmentStrategy(self.graph, [Author, Paper, Paper, Author], iterations = 3)
self.outputSimilarityScores(authorMap, authors, propagatedNeighborsimStrategy, "NeighborSim-WeightedPropagation-3")
propagatedNeighborsimStrategy = NeighborSimConstantPreferentialAttachmentStrategy(self.graph, [Author, Paper, Paper, Author], iterations = 4)
self.outputSimilarityScores(authorMap, authors, propagatedNeighborsimStrategy, "NeighborSim-WeightedPropagation-4")
propagatedNeighborsimStrategy = NeighborSimConstantPreferentialAttachmentStrategy(self.graph, [Author, Paper, Paper, Author], iterations = 50)
self.outputSimilarityScores(authorMap, authors, propagatedNeighborsimStrategy, "NeighborSim-WeightedPropagation-50")
# Neighbor citation count difference strategy
citeCountNeighborsimStrategy = NeighborSimStrategy(self.graph, [Paper, Paper, Author], commonNeighbors = False)
self.outputSimilarityScores(authorMap, authors, citeCountNeighborsimStrategy, "NeighborSim-CiteCountDiff", citationCounts = totalCitationCount)
def run(self):
self.graph, authorMap, conferenceMap, totalCitationCount = SampleGraphUtility.constructMultiDisciplinaryAuthorExample()
# Get the nodes we care about
conferences = [
conferenceMap['VLDB'],
conferenceMap['KDD']
]
authors = [
authorMap['A'],
authorMap['B'],
authorMap['C'],
authorMap['D'],
authorMap['E'],
authorMap['F'],
authorMap['G'],
authorMap['H'],
authorMap['I'],
]
self.metaPathUtility = EdgeBasedMetaPathUtility()
# Build homogeneous projection of network (authors, with edges for times authors cite each other)
projectedGraph = self.metaPathUtility.createHomogeneousProjection(self.graph, [Author, Paper, Paper, Author])
authorCitationCounts = {}
for author in projectedGraph.getNodes():
authorCitationCounts[author] = {}
for otherAuthor in projectedGraph.getNodes():
authorCitationCounts[author][otherAuthor] = projectedGraph.getNumberOfEdges(author, otherAuthor)
# Output the adjacency matrix for authors-authors in the graph
self.output('\nCitation Matrix:')
adjMatrixTable = texttable.Texttable()
rows = [['Author'] + [author.name for author in authors]]
rows += [[author.name] + [authorCitationCounts[author][otherAuthor] for otherAuthor in authors] for author in authors]
adjMatrixTable.add_rows(rows)
self.output(adjMatrixTable.draw())
# Output the adjacency matrix for authors & conferences in the graph
self.output('\nAdjacency Matrix:')
adjMatrixTable = texttable.Texttable()
projectedGraph = self.metaPathUtility.createHeterogeneousProjection(self.graph, [Author, Paper, Conference])
rows = [[''] + [conference.name for conference in conferences]]
rows += [[author.name] + [projectedGraph.getNumberOfEdges(author, conference) for conference in conferences] for author in authors]
adjMatrixTable.add_rows(rows)
self.output(adjMatrixTable.draw())
# Output total citation counts
self.output('\nTotal Citation Counts:')
rows = [[author.name for author in authors],['%d' % totalCitationCount[author.name] for author in authors]]
citationCountTable = texttable.Texttable()
citationCountTable.add_rows(rows)
self.output(citationCountTable.draw())
# Output the NeighborSim similarity scores
strategy = NeighborSimStrategy(self.graph, [Conference, Paper, Paper, Author])
self.outputSimilarityScores(authorMap, authors, strategy, "NeighborSim")
# Output the PathSim similarity scores
pathsimStretegy = PathSimStrategy(self.graph, [Author, Paper, Conference, Paper, Author], True)
self.outputSimilarityScores(authorMap, authors, pathsimStretegy, "PathSim")
# Output SimRank-related scores
simrankStrategy = SimRankStrategy(self.graph, [Author, Paper, Paper, Author])
self.outputSimilarityScores(authorMap, authors, simrankStrategy, "SimRank")
# Output pathsim - simrank scores
combinedNeighborSim = AggregateSimilarityStrategy(self.graph, [simrankStrategy, pathsimStretegy], [0.5, 0.5])
self.outputSimilarityScores(authorMap, authors, combinedNeighborSim, 'APCPA Pathsim, APPA SimRank')
# Output the projected PageRank/HITS similarity scores
for name, algorithm in zip(['PageRank', 'HITS'], [PageRankDistanceStrategy, HITSDistanceStrategy]):
researchAreas = {
(authorMap['A'], authorMap['B'], authorMap['C'], authorMap['D'], authorMap['E'], authorMap['I']),
(authorMap['F'], authorMap['G'], authorMap['H'], authorMap['D'], authorMap['E'], authorMap['I']),
}
strategy = algorithm(self.graph, [Author, Paper, Paper, Author], nodeSets=researchAreas, symmetric=True)
self.outputSimilarityScores(authorMap, authors, strategy, "%s-Distance" % name)
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')
