double TCascade::TransProb(const int& NId1, const int& NId2) const {
if (! IsNode(NId1) || ! IsNode(NId2)) { return Eps; }
if (GetTm(NId1) >= GetTm(NId2)) { return Eps; }
if (Model==0)
return Alpha*exp(-Alpha*(GetTm(NId2)-GetTm(NId1))); // exponential
else if (Model==1)
return (Alpha-1)*pow((GetTm(NId2)-GetTm(NId1)), -Alpha); // power-law
else
return Alpha*(GetTm(NId2)-GetTm(NId1))*exp(-0.5*Alpha*pow(GetTm(NId2)-GetTm(NId1), 2)); // rayleigh
return (-1);
}
// update the cascade probability given a new edge (N1, N2) in the graph
double TCascade::UpdateProb(const int& N1, const int& N2, const bool& UpdateProb) {
if (!IsNode(N1) || !IsNode(N2)) { return CurProb; }
if (GetTm(N1) >= GetTm(N2)) { return CurProb; }
const double P1 = log(TransProb(GetParent(N2), N2));
const double P2 = log(TransProb(N1, N2)); // N1 influences N2
if (P1 < P2) {
if (UpdateProb) { // the edge is there, update the CurProb and best Parent
CurProb = CurProb - P1 + P2;
NIdHitH.GetDat(N2).Parent = N1;
} else {
return CurProb - P1 + P2; }
}
return CurProb;
}
bool TJsonVal::IsTm() const {
try {
GetTm();
return true;
} catch (...) {
return false;
}
}
double TCascade::GetProb(const PNGraph& G) {
double P = 0;
for (int n = 0; n < Len(); n++) {
const int DstNId = GetNode(n);
const double DstTm = GetTm(DstNId);
TNGraph::TNodeI NI = G->GetNI(DstNId);
double MxProb = log(Eps);
int BestParent = -1;
for (int e = 0; e < NI.GetInDeg(); e++) {
const int SrcNId = NI.GetInNId(e);
if (IsNode(SrcNId) && GetTm(SrcNId) < DstTm) {
const double Prob = log(TransProb(SrcNId, DstNId));
if (MxProb < Prob) { MxProb = Prob; BestParent = SrcNId; }
}
}
NIdHitH.GetDat(DstNId).Parent = BestParent;
P += MxProb;
}
return P;
}
