def getYear_DBIS_filetered(self,paperDict,PaperRawdict,filterDict):
outputlist=[]
outputlist_null=[]
counter=0
counter_total=0
for key in paperDict:
if key in filterDict: continue
papername=PaperRawdict[key]
if papername[0]=='"' and papername[-1]=='"':
papername=papername[1:-1]
papername= self.connect.escape(papername)
self.cursor.execute(self.selectStr_easy % (papername))
resultlist=self.cursor.fetchall()
if len(resultlist)>0:
print resultlist
year=resultlist[0][1]
outputlist.append([key,year])
counter+=1
else:
outputlist_null.append([key,papername])
counter_total+=1
print str(counter_total)+'/'+str(len(paperDict)-len(filterDict))+' '+papername
print counter
util.write_csv_inlist('paperwithyear_dbis.csv',outputlist)
util.write_csv_inlist('paperwithnoresults.csv',outputlist_null)
def output(self):
outputlist=[]
for year in self.yearDict:
confDict_oneyear=self.yearDict[year]
for conf in confDict_oneyear:
outputlist.append([year,conf,self.confDict[conf],confDict_oneyear[conf]])
util.write_csv_inlist('statistic_conf_byyear.csv',outputlist)
def output(self, keyList, labels, name):
outputList = []
length = len(keyList)
for i in range(length):
clu = labels[i]
theKey = keyList[i]
outputList.append([theKey, self.authorObjDict[theKey].name, clu])
util.write_csv_inlist('./authorCluster_' + name + '.csv', outputList)
def selectData_onekey(self,outputfile,key):
selectStr="""SELECT * FROM paper WHERE conference like %s """
key= self.connect.escape(key)
self.cursor.execute(selectStr % (key))
outputlist=[]
for row in self.cursor.fetchall():
outputlist.append([row[0].encode('utf-8'),row[1],row[2].encode('utf-8')])
util.write_csv_inlist(outputfile,outputlist)
def findDBIS_paper_withfilter(self,paper_confdict,paperdict,DBIS_cleaned,myfilter=None):
DBIS_paperlist=list()
counter=0
for key in paperdict:
confkey=paper_confdict[key]
if confkey in DBIS_cleaned:
counter+=1
if key not in myfilter:
DBIS_paperlist.append([key, paperdict[key]])
print counter
util.write_csv_inlist('notmatched.csv',DBIS_paperlist)
def countAuthors(self):
focusedAuthors = dict()
print len(self.paperDict_obj)
for key in self.paperDict_obj:
paper = self.paperDict_obj[key]
authors = paper.authors
for author in authors:
if author not in focusedAuthors:
focusedAuthors[author] = 1
else:
focusedAuthors[author] = focusedAuthors[author] + 1
util.write_csv_inlist('authorCount.csv',
util.dict2list(focusedAuthors))
return focusedAuthors
def clustering(self, k):
kmeans = KMeans(n_clusters=k, random_state=0).fit_predict(self.F)
gobalIndex = 0
outputList = []
for key in self.nodeIndexDict:
keyList = self.nodeIndexDict[key]
length = len(keyList)
for i in range(length):
clu = kmeans[gobalIndex + i]
theKey = keyList[i]
outputList.append(
[theKey, self.authorObjDict[theKey].name, key, clu])
gobalIndex += length
util.write_csv_inlist('./temporal/authorCluster.csv', outputList)
def filterFeatureName(self, folder):
featureNameSet = set()
for year in range(self.start, self.end + 1):
fp = open(folder + str(year) + '-featureNames.csv', 'r')
while 1:
line = fp.readline()
if not line:
break
content = line.strip().split(',')
for val in content:
featureNameSet.add(val)
output = ''
for key in featureNameSet:
output += key + ','
print len(featureNameSet)
util.write_csv_inlist(folder + 'out-featureNames.csv', [output])
def getYear_DBIS_notmatched(self,paperDict,notmatchDict):
outputlist=[]
counter=0
for key in notmatchDict:
papername=paperDict[key]
if papername[0]=='"' and papername[-1]=='"':
papername=papername[1:-1]
papername= self.connect.escape(papername)
# print papername
self.cursor.execute(self.selectStr_easy % (papername))
resultlist=self.cursor.fetchall()
if len(resultlist)>0:
print resultlist
year=resultlist[0][1]
outputlist.append([key,year])
counter+=1
print counter
util.write_csv_inlist('paperwithyear_dbis.csv',outputlist)
def matchAminerAndDBLP(self, focusedPaperDict, filterDict=None):
haveCounter=0
havelist=[]
nolist=[]
html_parser = HTMLParser.HTMLParser()
paperTitleDict=self.reverseDict()
for key in focusedPaperDict:
originalTitle = html_parser.unescape(focusedPaperDict[key].title)
originalTitle=util.simplifyStr(originalTitle)
# print originalTitle
if originalTitle in paperTitleDict:
havelist.append([paperTitleDict[originalTitle],key,focusedPaperDict[key].title])
haveCounter+=1
print haveCounter
else:
nolist.append([key,focusedPaperDict[key].title])
util.write_csv_inlist('havelist.csv',havelist,['aminerKey','dblpKey','title'])
util.write_csv_inlist('nolist.csv',nolist,['aminerKey','title'])
print haveCounter
print len(focusedPaperDict)
def generateEdge(self):
edgeDict = collections.OrderedDict()
edgeIndexDict = collections.OrderedDict()
for yearkey in self.author_nodes:
author_node = self.author_nodes[yearkey]
key_list = []
for key in author_node:
key_list.append(key)
mlength = len(key_list)
ajmatrix = np.zeros((mlength, mlength))
for i in range(mlength):
ikey = key_list[i]
iauthor = self.authorObjDict[ikey]
for pkey in iauthor.papers:
thePaper = self.paperObjDict[pkey]
theYear = thePaper.year
if theYear > yearkey: continue
theAuthors = thePaper.authors
for akey in theAuthors:
if akey == ikey: continue
if akey in key_list:
akey_index = key_list.index(akey)
val = math.exp(-self.rou * (yearkey - theYear))
ajmatrix[i][
akey_index] = ajmatrix[i][akey_index] + val
# ajmatrix[akey_index][i]=ajmatrix[i][akey_index]
# b = np.nonzero(ajmatrix)
# print(np.array(b).ndim)
np.savetxt('./proces/temporal/year_' + str(yearkey) + '.csv',
ajmatrix,
fmt='%d',
delimiter=',')
util.write_csv_inlist(
'./proces/temporal/nodeslist_' + str(yearkey) + '.csv',
key_list)
edgeDict[yearkey] = ajmatrix
edgeIndexDict[yearkey] = key_list
return edgeDict, edgeIndexDict
def toCSV(self,filename):
util.write_csv_inlist(filename,self.outputList)
def buildNetwork(self):
edgeDict_new = collections.OrderedDict()
edgeIndexDict_new = collections.OrderedDict()
for key in self.nodeIndexDict:
myGraph = snap.TNEANet.New()
keyList = self.nodeIndexDict[key]
length = len(keyList)
for i in range(length):
theKey = keyList[i]
nid = myGraph.AddNode(i)
myGraph.AddStrAttrDatN(nid, theKey, 'key')
# there is only the first order!!!!!!!!!!!!!!!!!!
A = self.edgeDict[key]
# B=np.dot(A,A)
# B=np.dot(A,np.dot(A,A))
# B_sim=cosine_similarity(A)
C = A
outputList = []
outputList_line = []
outputList_dw = []
for i in range(length):
for j in range(i + 1, length):
if C[i, j] > 0:
eid = myGraph.AddEdge(i, j)
myGraph.AddFltAttrDatE(eid, A[i, j], 'weigth')
eid = myGraph.AddEdge(j, i)
myGraph.AddFltAttrDatE(eid, A[j, i], 'weigth')
outputList.append([keyList[i], keyList[j], A[i, j]])
outputList_line.append(
[keyList[j], keyList[i], A[j, i]])
outputList_line.append(
[keyList[i], keyList[j], A[i, j]])
outputList_dw.append([keyList[i], keyList[j]])
outputList_dw.append([keyList[j], keyList[i]])
util.write_csv_inlist(str(key) + '.csv', outputList)
util.write_csv_inlist(str(key) + '_line.txt', outputList_line)
util.write_csv_inlist(str(key) + '_dw.txt', outputList_dw)
print str(key) + '-original: ' + str(
myGraph.GetEdges()) + ' ' + str(myGraph.GetNodes())
MxWcc = snap.GetMxWcc(myGraph)
print str(key) + '-mxWcc: ' + str(MxWcc.GetEdges()) + ' ' + str(
MxWcc.GetNodes())
# labels = snap.TIntStrH()
# for NI in MxWcc.Nodes():
# labels[NI.GetId()] = str(NI.GetId())
# snap.DrawGViz(MxWcc, snap.gvlSfdp, './graph/'+str(key)+".gif", " ", labels)
keyList_new = []
for node in MxWcc.Nodes():
keyList_new.append(keyList[int(node.GetId())])
ajmatrix = np.zeros((MxWcc.GetNodes(), MxWcc.GetNodes()))
counter_out = 0
for node_out in MxWcc.Nodes():
counter_in = 0
for node_in in MxWcc.Nodes():
ajmatrix[counter_out,
counter_in] = A[int(node_out.GetId()),
int(node_in.GetId())]
counter_in += 1
counter_out += 1
edgeDict_new[key] = ajmatrix
edgeIndexDict_new[key] = keyList_new
self.graphList.append(MxWcc)
return edgeDict_new, edgeIndexDict_new
def getNodeAttributes(self):
attributeslist = []
outputList = []
for UGraph in self.graphList:
attriList = []
for index in range(UGraph.GetNodes()):
nodelist = []
attriList.append(nodelist)
#page rank
PRankH = snap.TIntFltH()
snap.GetPageRank(UGraph, PRankH)
counter = 0
for item in PRankH:
attriList[counter].append(PRankH[item])
counter += 1
#HIN
counter = 0
NIdHubH = snap.TIntFltH()
NIdAuthH = snap.TIntFltH()
snap.GetHits(UGraph, NIdHubH, NIdAuthH)
for item in NIdHubH:
attriList[counter].append(NIdHubH[item])
attriList[counter].append(NIdAuthH[item])
counter += 1
# Betweenness Centrality
counter = 0
Nodes = snap.TIntFltH()
Edges = snap.TIntPrFltH()
snap.GetBetweennessCentr(UGraph, Nodes, Edges, 1.0)
for node in Nodes:
attriList[counter].append(Nodes[node])
counter += 1
# closeness centrality
counter = 0
for NI in UGraph.Nodes():
CloseCentr = snap.GetClosenessCentr(UGraph, NI.GetId())
attriList[counter].append(CloseCentr)
counter += 1
# farness centrality
counter = 0
for NI in UGraph.Nodes():
FarCentr = snap.GetFarnessCentr(UGraph, NI.GetId())
attriList[counter].append(FarCentr)
counter += 1
# node eccentricity
counter = 0
for NI in UGraph.Nodes():
attriList[counter].append(
snap.GetNodeEcc(UGraph, NI.GetId(), True))
counter += 1
atrriMarix = np.array(attriList)
attributeslist.append(atrriMarix)
outputList.append(attriList)
# convert to undirected graph
# GOut = snap.ConvertGraph(snap.PUNGraph, UGraph)
# for NI in UGraph.Nodes():
# DegCentr = snap.GetDegreeCentr(UGraph, NI.GetId())
# print "node: %d centrality: %f" % (NI.GetId(), DegCentr)
util.write_csv_inlist('attributeslist.csv', outputList)
return attributeslist
