public static Tuple <List <int>, double> UnifiedCGreedy(Graph graph, Bipartite bg, List <int> Type, double c, double B, double alpha)
{
List <double> P = new List <double>();
for (int id = 0; id < Type.Count; ++id)
{
int type = Type[id];
double p;
if (type == 1)
{
p = c * c;
}
else if (type == 2)
{
p = c;
}
else
{
p = (2 - c) * c;
}
P.Add(p);
}
int k = (int)(B / c);
Tuple <List <int>, double> tup = bg.Greedy(k, P);
Console.WriteLine("seeds have been selected");
return(tup);
}
public static void CoordinateDescentAlgCommonHyperGraphOneAlpha(Graph graph)
{
StreamReader initial = new StreamReader(filepath + "_ini100.txt");
List <int> seeds = new List <int>();
for (int i = 0; i < 100; i++)
{
seeds.Add(int.Parse(initial.ReadLine()));
}
double alpha = 0.6; // Step of c of searching the best discount in th Unified Discount Algorithm
while (alpha <= 1.0)
{
Bipartite bg = new Bipartite(filepath, alpha, graph.numV);
int b = 10;
while (b <= 50)
// Build a random hyper graph with mH random hyper edges.
{
DateTime Hyper_start = DateTime.Now;
ICModel icm = new ICModel(alpha);
Tuple <List <int>, double> choose = bg.Greedy(b, seeds);
DateTime Hyper_end = DateTime.Now;
double Hyper_time = (Hyper_end - Hyper_start).TotalMilliseconds;
FileStream outfile = new FileStream(filepath + "_2o.txt", FileMode.Append);
StreamWriter writer = new StreamWriter(outfile);
writer.Write("Choose time:\t" + Hyper_time + "\t");
Hyper_start = DateTime.Now;
Tuple <double, double> results = icm.InfluenceSpread(graph, choose.Item1, 100);
Hyper_end = DateTime.Now;
Hyper_time = (Hyper_end - Hyper_start).TotalMilliseconds;
string mem = Convert.ToString(Process.GetCurrentProcess().WorkingSet64 / 8 / 1024 / 1024);
writer.Write("Propagation time:\t" + Hyper_time + "\t");
writer.Write("a:" + alpha + "\tb:" + b + "\tave:" + results.Item1 + "\tstd:" + results.Item2 + "\tmemory:" + mem + "\n");
writer.Flush();
writer.Close();
b += 10;
}
alpha += 0.2;
}
}
public static Tuple <List <int>, double> UnifiedCGreedy(Graph graph, List <int> Type, double c, double B, double alpha)
{
List <double> P = new List <double>();
for (int id = 0; id < Type.Count; ++id)
{
int type = Type[id];
double p;
if (type == 1)
{
p = c * c;
}
else if (type == 2)
{
p = c;
}
else
{
p = (2 - c) * c;
}
P.Add(p);
}
ICModel icm = new ICModel(alpha);
int mH = GlobalVar.mH;
List <List <int> > RR = new List <List <int> >();
for (int r = 0; r < mH; ++r)
{
List <int> rSet = icm.RR(graph).ToList();
RR.Add(rSet);
}
Bipartite bg = new Bipartite(RR, graph.numV);
Console.WriteLine("Hyper-graph has been built");
int k = (int)(B / c);
Tuple <List <int>, double> tup = bg.Greedy(k, P);
Console.WriteLine("seeds have been selected");
return(tup);
}
public static void CoordinateDescentAlgCommonHyperGraphOneAlpha(Graph graph)
{
StreamReader initial = new StreamReader(filepath + "_ini100.txt");
StreamReader nodetype = new StreamReader(filepath + "_typeo.txt");
StreamReader threshold = new StreamReader(filepath + "_tu.txt");
List <double> thresh = new List <double>();
for (int i = 0; i < graph.numV; i++)
{
thresh.Add(double.Parse(threshold.ReadLine()));
}
List <int> type = new List <int>();
for (int i = 0; i < graph.numV; i++)
{
int flag = int.Parse(nodetype.ReadLine());
if (flag == 0)
{
type.Add(0);
}
else if (flag == 1)
{
type.Add(2);
}
else
{
type.Add(1);
}
}
List <double> d = new List <double> {
0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0
};
List <double> cu = new List <double>();
for (int i = 0; i < graph.numV; i++)
{
double t = thresh[i];
double p = 0.0;
if (type[i] == 1)
{
p = System.Math.Pow(t, 0.5);
}
else if (type[i] == 2)
{
p = t;
}
else
{
p = 1 - System.Math.Pow(1 - t, 0.5);
}
foreach (double point in d)
{
if (point < p)
{
continue;
}
cu.Add(point);
break;
}
}
List <int> seed = new List <int>();
for (int i = 0; i < 100; i++)
{
seed.Add(int.Parse(initial.ReadLine()));
}
int mh = 0;
if (filepath.Contains("Wiki"))
{
mh = 250000;
}
else if (filepath.Contains("CA"))
{
mh = 2000000;
}
else if (filepath.Contains("dblp"))
{
mh = 20000000;
}
else
{
mh = 40000000;
}
double alpha = 0.8; // Step of c of searching the best discount in th Unified Discount Algorithm
while (alpha <= 0.8)
{
Bipartite bg = new Bipartite(filepath, alpha, graph.numV);
double b = 10;
while (b <= 10)
// Build a random hyper graph with mH random hyper edges.
{
DateTime Hyper_start = DateTime.Now;
ICModel icm = new ICModel(alpha);
Tuple <List <int>, double> realize = bg.Greedy(cu, b, seed);
DateTime Hyper_end = DateTime.Now;
double Hyper_time = (Hyper_end - Hyper_start).TotalMilliseconds;
FileStream outfile = new FileStream(filepath + "_5o.txt", FileMode.Append);
StreamWriter writer = new StreamWriter(outfile);
Console.WriteLine(realize.Item1.Count);
Hyper_start = DateTime.Now;
Tuple <double, double> results = icm.InfluenceSpread(graph, realize.Item1, 200);
Hyper_end = DateTime.Now;
writer.Write("Choose time:" + Hyper_time + "\t");
Hyper_time = (Hyper_end - Hyper_start).TotalMilliseconds;
string mem = Convert.ToString(Process.GetCurrentProcess().WorkingSet64 / 8 / 1024 / 1024);
writer.Write("Propagation time:" + Hyper_time + "\t");
writer.Write("a:" + alpha + "\tb:" + b + "\tave:" + results.Item1 + "\tstd:" + results.Item2 + "\tmemory:" + mem + "\n");
writer.Flush();
writer.Close();
b += 10.0;
}
alpha += 0.2;
}
}
public static void CoordinateDescentAlgCommonHyperGraphOneAlpha(Graph graph, List <int> Type, string RsltDir)
{
double b = GlobalVar.b; // Step of c of searching the best discount in th Unified Discount Algorithm
double alpha = GlobalVar.Alpha;
string Dir = RsltDir + "/Alpha=" + alpha;
string Path = Dir + "/AllResults.txt";
StreamWriter writer = new StreamWriter(Path);
// Build a random hyper graph with mH random hyper edges.
DateTime Hyper_start = DateTime.Now;
ICModel icm = new ICModel(alpha);
int mH = GlobalVar.mH;
Console.WriteLine(mH);
List <List <int> > RR = new List <List <int> >();
for (int r = 0; r < mH; ++r)
{
List <int> rSet = icm.RR(graph).ToList();
RR.Add(rSet);
if (r > 0 && r % 100000 == 0)
{
Console.WriteLine(r + " samples");
}
}
Bipartite bg = new Bipartite(RR, graph.numV);
DateTime Hyper_end = DateTime.Now;
double Hyper_time = (Hyper_end - Hyper_start).TotalMilliseconds;
Console.WriteLine("Hyper-graph has been built");
writer.WriteLine("Hyper-graph time:\t" + Hyper_time);
writer.Flush();
for (int ind = GlobalVar.St; ind <= GlobalVar.End; ++ind)
{
double B = ind * 10.0;
bg.Greedy((int)B);
// Unified Discount Algorithm
DateTime startTime = DateTime.Now;
List <Tuple <List <int>, double> > Res = new List <Tuple <List <int>, double> >();
for (int i = 1; i <= 20; ++i)
{
double c = i * b;
Tuple <List <int>, double> tup = UnifiedCGreedy(graph, bg, Type, c, B, alpha);
Res.Add(tup);
Console.WriteLine("Alpha=" + alpha + "\tc=" + c + "\t" + tup.Item2);
}
// Discrete Influence Maximization
double IM_greedy = 0;
DateTime IM_greedy_start = DateTime.Now;
Tuple <List <int>, double> tup_IM = UnifiedCGreedy(graph, bg, Type, 20 * b, B, alpha);
Res.Add(tup_IM);
Console.WriteLine("Alpha=" + alpha + "\tc=1\t" + tup_IM.Item2);
DateTime IM_greedy_end = DateTime.Now;
IM_greedy = (IM_greedy_end - IM_greedy_start).TotalMilliseconds;
//Influence Maximization results
double IM_sp = Res[Res.Count - 1].Item2;
List <int> IM_seeds = Res[Res.Count - 1].Item1;
double IM_ts = Hyper_time + IM_greedy;
// Unified Discount results (except standard deviation)
int max_i = 0;
for (int i = 0; i < Res.Count - 1; ++i)
{
if (Res[i].Item2 > Res[max_i].Item2)
{
max_i = i;
}
}
DateTime UC_endTime = DateTime.Now;
Console.WriteLine("Best c=" + (max_i + 1) * b + "\t" + Res[max_i].Item2);
double UC_sp = Res[max_i].Item2;
List <int> UC_seeds = Res[max_i].Item1;
double UC_ts = (UC_endTime - startTime).TotalMilliseconds + Hyper_time;
// Proceed to Coordinate Descent. Use the best result of Unified Discount as initial value
double init_c = (max_i + 1) * b;
List <int> initNodes = UC_seeds;
CoordinateDescent cd = new CoordinateDescent(graph, bg, initNodes, init_c, Type, GlobalVar.batch_num, alpha, GlobalVar.mH);
List <double> C = cd.IterativeMinimize();
DateTime CD_endTime = DateTime.Now;
double CD_ts = (CD_endTime - startTime).TotalMilliseconds + Hyper_time;
// Evaluation using Monte Carlo Simulations
List <Pair> IM_P = new List <Pair>();
foreach (int u in IM_seeds)
{
Pair pair = new Pair(u, 1.0);
IM_P.Add(pair);
}
List <Pair> UC_P = new List <Pair>();
foreach (int u in UC_seeds)
{
double p = 0;
if (Type[u] == 1)
{
p = init_c * init_c;
}
else if (Type[u] == 2)
{
p = init_c;
}
else
{
p = (2 - init_c) * init_c;
}
Pair pair = new Pair(u, p);
UC_P.Add(pair);
}
List <Pair> CD_P = new List <Pair>();
for (int i = 0; i < graph.numV; ++i)
{
double p = 0;
if (Type[i] == 1)
{
p = C[i] * C[i];
}
else if (Type[i] == 2)
{
p = C[i];
}
else
{
p = (2 - C[i]) * C[i];
}
Pair pair = new Pair(i, p);
if (p > GlobalVar.epsilon)
{
CD_P.Add(pair);
}
}
// //ICModel icm = new ICModel(alpha);
Tuple <double, double> IM_tup = icm.InfluenceSpread(graph, IM_P, GlobalVar.MC);
Tuple <double, double> UC_tup = icm.InfluenceSpread(graph, UC_P, GlobalVar.MC);
Tuple <double, double> CD_tup = icm.InfluenceSpread(graph, CD_P, GlobalVar.MC);
double Numerator = 2 * graph.numV * (1 - 1.0 / Math.E) * (Math.Log(Cnk(graph.numV, (int)B)) + Math.Log(graph.numV) + Math.Log(2.0));
double appro = 1 - 1.0 / Math.E - Math.Sqrt(Numerator / (IM_tup.Item1 * (double)GlobalVar.mH));
writer.WriteLine("B=" + B);
writer.WriteLine("IM:\t" + IM_tup.Item1 + "\t" + IM_tup.Item2 + "\t" + IM_ts + "\t" + appro);
writer.WriteLine("UC:\t" + UC_tup.Item1 + "\t" + UC_tup.Item2 + "\t" + UC_ts);
writer.WriteLine("CD:\t" + CD_tup.Item1 + "\t" + CD_tup.Item2 + "\t" + CD_ts);
writer.Flush();
string outPath = Dir + "/B=" + B + ".txt";
StreamWriter outWriter = new StreamWriter(outPath);
outWriter.WriteLine("IM\t" + IM_ts + "\t" + IM_seeds.Count);
foreach (int u in IM_seeds)
{
outWriter.WriteLine(u + "\t1\t1");
}
outWriter.WriteLine("UC\t" + UC_ts + "\t" + UC_seeds.Count);
foreach (Pair pair in UC_P)
{
outWriter.WriteLine(pair.id + "\t" + init_c + "\t" + pair.prob);
}
outWriter.WriteLine("CD\t" + CD_ts + "\t" + CD_P.Count);
foreach (Pair pair in CD_P)
{
outWriter.WriteLine(pair.id + "\t" + C[pair.id] + "\t" + pair.prob);
}
outWriter.Close();
string ccurvePath = Dir + "/curve_c(" + B + ").txt";
StreamWriter cWriter = new StreamWriter(ccurvePath);
for (int i = 0; i < Res.Count; ++i)
{
double c = (i + 1) * b;
cWriter.WriteLine(c + "\t" + Res[i].Item2);
}
cWriter.Close();
}
writer.Close();
}
public static Tuple<List<int>, double> UnifiedCGreedy(Graph graph, Bipartite bg, List<int> Type, double c, double B, double alpha)
{
List<double> P = new List<double>();
for (int id = 0; id < Type.Count; ++id)
{
int type = Type[id];
double p;
if (type == 1)
p = c * c;
else if (type == 2)
p = c;
else
p = (2 - c) * c;
P.Add(p);
}
int k = (int)(B / c);
Tuple<List<int>, double> tup = bg.Greedy(k, P);
Console.WriteLine("seeds have been selected");
return tup;
}
public static Tuple<List<int>, double> UnifiedCGreedy(Graph graph, List<int> Type, double c, double B, double alpha)
{
List<double> P = new List<double>();
for (int id = 0; id < Type.Count; ++id)
{
int type = Type[id];
double p;
if (type == 1)
p = c * c;
else if (type == 2)
p = c;
else
p = (2 - c) * c;
P.Add(p);
}
ICModel icm = new ICModel(alpha);
int mH = GlobalVar.mH;
List<List<int>> RR = new List<List<int>>();
for (int r = 0; r < mH; ++r)
{
List<int> rSet = icm.RR(graph).ToList();
RR.Add(rSet);
}
Bipartite bg = new Bipartite(RR, graph.numV);
Console.WriteLine("Hyper-graph has been built");
int k = (int)(B / c);
Tuple<List<int>, double> tup = bg.Greedy(k, P);
Console.WriteLine("seeds have been selected");
return tup;
}
public static void CoordinateDescentAlgCommonHyperGraphOneAlpha(Graph graph, List<int> Type, string RsltDir)
{
double b = GlobalVar.b; // Step of c of searching the best discount in th Unified Discount Algorithm
double alpha = GlobalVar.Alpha;
string Dir = RsltDir + "/Alpha=" + alpha;
string Path = Dir + "/AllResults.txt";
StreamWriter writer = new StreamWriter(Path);
// Build a random hyper graph with mH random hyper edges.
DateTime Hyper_start = DateTime.Now;
ICModel icm = new ICModel(alpha);
int mH = GlobalVar.mH;
Console.WriteLine(mH);
List<List<int>> RR = new List<List<int>>();
for (int r = 0; r < mH; ++r)
{
List<int> rSet = icm.RR(graph).ToList();
RR.Add(rSet);
if (r > 0 && r % 100000 == 0)
Console.WriteLine(r + " samples");
}
Bipartite bg = new Bipartite(RR, graph.numV);
DateTime Hyper_end = DateTime.Now;
double Hyper_time = (Hyper_end - Hyper_start).TotalMilliseconds;
Console.WriteLine("Hyper-graph has been built");
writer.WriteLine("Hyper-graph time:\t" + Hyper_time);
writer.Flush();
for (int ind = GlobalVar.St; ind <= GlobalVar.End; ++ind)
{
double B = ind * 10.0;
bg.Greedy((int)B);
// Unified Discount Algorithm
DateTime startTime = DateTime.Now;
List<Tuple<List<int>, double>> Res = new List<Tuple<List<int>, double>>();
for (int i = 1; i*b - 1.0 <= 1e-4; ++i)
{
double c = i * b;
Tuple<List<int>, double> tup = UnifiedCGreedy(graph, bg, Type, c, B, alpha);
Res.Add(tup);
Console.WriteLine("Alpha=" + alpha + "\tc=" + c + "\t" + tup.Item2);
}
// Discrete Influence Maximization
double IM_greedy = 0;
DateTime IM_greedy_start = DateTime.Now;
Tuple<List<int>, double> tup_IM = UnifiedCGreedy(graph, bg, Type, 20 * b, B, alpha);
Res.Add(tup_IM);
Console.WriteLine("Alpha=" + alpha + "\tc=1\t" + tup_IM.Item2);
DateTime IM_greedy_end = DateTime.Now;
IM_greedy = (IM_greedy_end - IM_greedy_start).TotalMilliseconds;
//Influence Maximization results
double IM_sp = Res[Res.Count - 1].Item2;
List<int> IM_seeds = Res[Res.Count - 1].Item1;
double IM_ts = Hyper_time + IM_greedy;
// Unified Discount results (except standard deviation)
int max_i = 0;
for (int i = 0; i < Res.Count - 1; ++i)
{
if (Res[i].Item2 > Res[max_i].Item2)
max_i = i;
}
DateTime UC_endTime = DateTime.Now;
Console.WriteLine("Best c=" + (max_i + 1) * b + "\t" + Res[max_i].Item2);
double UC_sp = Res[max_i].Item2;
List<int> UC_seeds = Res[max_i].Item1;
double UC_ts = (UC_endTime - startTime).TotalMilliseconds + Hyper_time;
// Proceed to Coordinate Descent. Use the best result of Unified Discount as initial value
double init_c = (max_i + 1) * b;
List<int> initNodes = UC_seeds;
CoordinateDescent cd = new CoordinateDescent(graph, bg, initNodes, init_c, Type, GlobalVar.batch_num, alpha, GlobalVar.mH);
List<double> C = cd.IterativeMinimize();
DateTime CD_endTime = DateTime.Now;
double CD_ts = (CD_endTime - startTime).TotalMilliseconds + Hyper_time;
// Evaluation using Monte Carlo Simulations
List<Pair> IM_P = new List<Pair>();
foreach (int u in IM_seeds)
{
Pair pair = new Pair(u, 1.0);
IM_P.Add(pair);
}
List<Pair> UC_P = new List<Pair>();
foreach (int u in UC_seeds)
{
double p = 0;
if (Type[u] == 1)
p = init_c * init_c;
else if (Type[u] == 2)
p = init_c;
else
p = (2 - init_c) * init_c;
Pair pair = new Pair(u, p);
UC_P.Add(pair);
}
List<Pair> CD_P = new List<Pair>();
for (int i = 0; i < graph.numV; ++i)
{
double p = 0;
if (Type[i] == 1)
p = C[i] * C[i];
else if (Type[i] == 2)
p = C[i];
else
p = (2 - C[i]) * C[i];
Pair pair = new Pair(i, p);
if (p > GlobalVar.epsilon)
CD_P.Add(pair);
}
// //ICModel icm = new ICModel(alpha);
Tuple<double, double> IM_tup = icm.InfluenceSpread(graph, IM_P, GlobalVar.MC);
Tuple<double, double> UC_tup = icm.InfluenceSpread(graph, UC_P, GlobalVar.MC);
Tuple<double, double> CD_tup = icm.InfluenceSpread(graph, CD_P, GlobalVar.MC);
double Numerator = 2 * graph.numV * (1 - 1.0 / Math.E) * (Math.Log(Cnk(graph.numV, (int)B)) + Math.Log(graph.numV) + Math.Log(2.0));
double appro = 1 - 1.0 / Math.E - Math.Sqrt(Numerator / (IM_tup.Item1*(double)GlobalVar.mH));
writer.WriteLine("B=" + B);
writer.WriteLine("IM:\t" + IM_tup.Item1 + "\t" + IM_tup.Item2 + "\t" + IM_ts + "\t" + appro);
writer.WriteLine("UC:\t" + UC_tup.Item1 + "\t" + UC_tup.Item2 + "\t" + UC_ts);
writer.WriteLine("CD:\t" + CD_tup.Item1 + "\t" + CD_tup.Item2 + "\t" + CD_ts);
writer.Flush();
string outPath = Dir + "/B=" + B + ".txt";
StreamWriter outWriter = new StreamWriter(outPath);
outWriter.WriteLine("IM\t" + IM_ts + "\t" + IM_seeds.Count);
foreach (int u in IM_seeds)
outWriter.WriteLine(u + "\t1\t1");
outWriter.WriteLine("UC\t" + UC_ts + "\t" + UC_seeds.Count);
foreach (Pair pair in UC_P)
outWriter.WriteLine(pair.id + "\t" + init_c + "\t" + pair.prob);
outWriter.WriteLine("CD\t" + CD_ts + "\t" + CD_P.Count);
foreach (Pair pair in CD_P)
outWriter.WriteLine(pair.id + "\t" + C[pair.id] + "\t" + pair.prob);
outWriter.Close();
string ccurvePath = Dir + "/curve_c(" + B + ").txt";
StreamWriter cWriter = new StreamWriter(ccurvePath);
for (int i = 0; i < Res.Count; ++i)
{
double c = (i + 1) * b;
cWriter.WriteLine(c + "\t" + Res[i].Item2);
}
cWriter.Close();
}
writer.Close();
ProcessData4Visual.Fig3(Path, Dir + "/Fig3.txt");
ProcessData4Visual.Fig4(Path, Dir + "/Fig4.txt");
ProcessData4Visual.Fig5(Dir + "/curve_c(50).txt", Dir + "/Fig5.txt");
ProcessData4Visual.Fig6(Path, Dir + "/Fig6.txt");
}
