def BULK(self, graph, Q, Wq, k, d):
l = 0
Gl1 = copy.deepcopy(graph)
distG = GraphFunc.getDistG(graph, Q)
d = max(max(distG.values()), d)
S = [v for v in Gl1.keys() if distG[v] <= d]
Gl2 = GraphFunc.buildNewGraph(Gl1, S)
ktemp = self.INF
TE, supE = ATindex.StructuralTrussness(Gl2)
for q in Q:
ktmp = max(TE[self.cedge(q, v)] for v in Gl2[q][0])
k = min(ktemp, ktmp)
k = min(ktemp, k)
while True:
if k == 2: break
if GraphFunc.connected(self.MaintainKDTruss(k, d, Gl2, Q, Wq), Q):
break
k -= 1
Gl = self.MaintainKDTruss(k, d, Gl2, Q, Wq)
maxfunc, ansg = -self.INF, None
while GraphFunc.connected(Gl, Q):
attriscore = AttributeScoreFunc.attributeScore(Gl, Wq)
if maxfunc < attriscore:
maxfunc, ansg = attriscore, copy.deepcopy(Gl)
gain = AttributeScoreFunc.computeGainFunc(k, Gl, Wq)
mingain = self.INF
for v in gain:
if gain[v] < mingain and not v in Q: mingain = gain[v]
if mingain == self.INF:
break
S = [v for v in gain.keys() if gain[v] == mingain and not v in Q]
if len(S) == 0:
break
S = random.sample(
S, max(1, int(len(S) * self.erlta / (self.erlta + 1))))
edgesToDelete = GraphFunc.getNeighborEdges(Gl, S)
Gl = GraphFunc.deleteEdges(Gl, edgesToDelete)
Gl = self.MaintainKDTruss(k, d, Gl, Q, Wq)
return list(ansg.keys())
def MaintainKDTruss(self, k, d, graph, Q, Wq):
Gt = copy.deepcopy(graph)
while GraphFunc.connected(Gt, Q):
TE, supE = ATindex.StructuralTrussness(Gt)
distG = GraphFunc.getDistG(Gt, Q)
edgestodelete = {}
for v in distG:
if distG[v] > d:
for u in Gt[v][0]:
edgestodelete[self.cedge(u, v)] = 1
for e in supE:
if supE[e] < k - 2:
edgestodelete[e] = 1
if len(edgestodelete) == 0: return Gt
Gt = GraphFunc.deleteEdges(Gt, edgestodelete)
if len(Gt) == 0: break
return Gt