public CoordinateDescent(Graph graph, Bipartite bg, List<int> initNodes, double init_c, List<int> type, int batch_num, double alpha, int mH) { this.graph = graph; this.initNodes = initNodes; this.init_c = init_c; this.type = type; this.batch_num = batch_num; this.alpha = alpha; this.mH = mH; this.bg = bg; C = new List<double>(); for (int i = 0; i < graph.numV; ++i) C.Add(0.0); foreach (int u in initNodes) C[u] = init_c; prob_edge = new List<double>(); for (int h = 0; h < bg.numS; ++h) { double res = 1.0; foreach (int u in bg.S2V[h]) res *= (1 - SeedProb(u, C[u])); //res = 1.0 - res; prob_edge.Add(res); } }
// Monte Carlo simlutation public int Propagation(Graph graph, List<int> seeds) { int num = seeds.Count; bool[] vis = new bool[graph.numV]; for (int u = 0; u < graph.numV; ++u) vis[u] = false; Queue<int> que = new Queue<int> (); foreach (int u in seeds) { vis[u] = true; que.Enqueue (u); } while (que.Count > 0) { int u = que.Dequeue (); foreach (Node node in graph.adj[u]) { int v = node.id; double pp = node.pp * alpha; if (!vis[v]) { double rp = random.NextDouble (); if (rp <= pp) { vis[v] = true; que.Enqueue (v); num++; } } } } return num; }
public CoordinateDescent(Graph graph, List<int> initNodes, double init_c, List<int> type, int batch_num, double alpha, int mH) { this.graph = graph; this.initNodes = initNodes; this.init_c = init_c; this.type = type; this.batch_num = batch_num; this.alpha = alpha; this.mH = mH; C = new List<double>(); for (int i = 0; i < graph.numV; ++i) C.Add(0.0); foreach (int u in initNodes) C[u] = init_c; ICModel icm = new ICModel(alpha); List<List<int>> RR = new List<List<int>>(); for (int r = 0; r < mH; ++r) { HashSet<int> rawSet = icm.RR(graph); List<int> rSet = rawSet.ToList(); RR.Add(rSet); } bg = new Bipartite(RR, graph.numV); prob_edge = new List<double>(); for (int h = 0; h < bg.numS; ++h) { double res = 1.0; foreach (int u in bg.S2V[h]) res *= (1 - SeedProb(u, C[u])); //res = 1.0 - res; prob_edge.Add(res); } }
// Compute UI(C), and return the estimated standard deviation public Tuple<double, double> InfluenceSpread(Graph graph, List<Pair> P, int R) { double sum = 0; List<double> spreads = new List<double>(); for (int rnd = 0; rnd < R; ++rnd) { List<int> seeds = new List<int> (); foreach (Pair pair in P) { int u = pair.id; double p = pair.prob; if (random.NextDouble () <= p) seeds.Add (u); } double sp = Propagation(graph, seeds); sum += sp; spreads.Add(sp); } double ave = sum / R; double sd = 0; foreach (double sp in spreads) sd += (ave - sp) * (ave - sp); sd = sd / R; sd = Math.Sqrt(sd); return new Tuple<double, double>(ave, sd); }
//Gadget graph is for computing UI(C). See the proof of Theorem 4. public Graph gadgetGraph(List<double> sp) { Graph gg = new Graph (this.numV * 2); for (int u = 0; u < this.numV; ++u) { Node node = new Node (u + this.numV, sp [u]); gg.adj [u].Add (node); Node rNode = new Node (u, sp [u]); gg.rAdj [u + this.numV].Add (rNode); } for (int u = 0; u < this.numV; ++u) { foreach (Node node in this.adj[u]) { Node nnode = new Node (node.id + this.numV, node.pp); gg.adj [u + this.numV].Add (nnode); Node rnNode = new Node (u + this.numV, node.pp); gg.rAdj [node.id + this.numV].Add (rnNode); } } return gg; }
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"); }
public static void Main(string[] args) { for (int i = 0; i < args.Length; ++i) Console.WriteLine(args[i]); if (args.Length > -1) { string ConfigPath = args[0]; StreamReader ConfigReader = new StreamReader(ConfigPath); string GraphPath, FunPath, RsltDir; string line; while ((line = ConfigReader.ReadLine()) != null) { if (line.ElementAt(0) != '#' && line != "") break; } Console.WriteLine(line); GraphPath = line; while ((line = ConfigReader.ReadLine()) != null) { if (line.ElementAt(0) != '#' && line != "") break; } Console.WriteLine(line); FunPath = line; while ((line = ConfigReader.ReadLine()) != null) { if (line.ElementAt(0) != '#' && line != "") break; } Console.WriteLine(line); RsltDir = line; while ((line = ConfigReader.ReadLine()) != null) { if (line.ElementAt(0) != '#' && line != "") break; } Console.WriteLine(line); GlobalVar.MC = int.Parse(line); while ((line = ConfigReader.ReadLine()) != null) { if (line.ElementAt(0) != '#' && line != "") break; } Console.WriteLine(line); GlobalVar.mH = int.Parse(line); while ((line = ConfigReader.ReadLine()) != null) { if (line.ElementAt(0) != '#' && line != "") break; } Console.WriteLine(line); GlobalVar.batch_num = int.Parse(line); while ((line = ConfigReader.ReadLine()) != null) { if (line.ElementAt(0) != '#' && line != "") break; } Console.WriteLine(line); GlobalVar.Alpha = double.Parse(line); while ((line = ConfigReader.ReadLine()) != null) { if (line.ElementAt(0) != '#' && line != "") break; } Console.WriteLine(line); GlobalVar.St = int.Parse(line); while ((line = ConfigReader.ReadLine()) != null) { if (line.ElementAt(0) != '#' && line != "") break; } Console.WriteLine(line); GlobalVar.End = int.Parse(line); while ((line = ConfigReader.ReadLine()) != null) { if (line.ElementAt(0) != '#' && line != "") break; } Console.WriteLine(line); GlobalVar.b = double.Parse(line); ConfigReader.Close(); Graph graph = new Graph(GraphPath); List<int> Type = new List<int>(); StreamReader reader = new StreamReader(FunPath); while ((line = reader.ReadLine()) != null) { string[] strs = line.Split(); //int id = int.Parse (strs [0]); int type = int.Parse(strs[1]); if (type == 4) type = 3; Type.Add(type); } CoordinateDescentAlgCommonHyperGraphOneAlpha(graph, Type, RsltDir); } }
// Generate a random RR set public HashSet<int> RR(Graph graph) { HashSet<int> rr = new HashSet<int> (); int V = graph.numV; int u = random.Next (V); Queue<int> que = new Queue<int> (); que.Enqueue(u); rr.Add(u); while (que.Count > 0) { int v = que.Dequeue (); foreach (Node node in graph.rAdj[v]) { int w = node.id; double pp = node.pp * alpha; if (!rr.Contains(w)) { double rp = random.NextDouble (); if (rp <= pp) { rr.Add (w); que.Enqueue (w); } } } } return rr; }