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"); }