public static SpreadingResult RunSpreading(ClusterNetwork net, double bias, int delay=0) { SpreadingResult res = new SpreadingResult(); Dictionary<Vertex, int> infectionTime = new Dictionary<Vertex, int>(); foreach (Vertex v in net.Vertices) infectionTime[v] = int.MinValue; List<Vertex> infected = new List<Vertex>(); Vertex seed = net.RandomVertex; infected.Add(seed); int i = 0; while (infected.Count < net.VertexCount) { foreach (Vertex v in infected.ToArray()) { // Biasing strategy Vertex neighbor = v.RandomNeighbor; double r = net.NextRandomDouble(); List<Vertex> intraNeighbors = new List<Vertex>(); List<Vertex> interNeighbors = new List<Vertex>(); ClassifyNeighbors(net, v, intraNeighbors, interNeighbors); if (r <= bias && interNeighbors.Count > 0) neighbor = interNeighbors.ElementAt(net.NextRandom(interNeighbors.Count)); if (neighbor != null && infectionTime[neighbor] == int.MinValue) { infectionTime[neighbor] = i; infected.Add(neighbor); } } if (delay > 0) System.Threading.Thread.Sleep(delay); i++; } res.Iterations = i; res.Modularity = (net as ClusterNetwork).NewmanModularity; return res; }
public static void Main() { List<double> results; List<double> modularity; string line = ""; System.IO.File.Delete(Properties.Settings.Default.ResultFile); int Nc = Properties.Settings.Default.clusterSize; int c = Properties.Settings.Default.clusters; double m = Properties.Settings.Default.m * Nc * c; // what is the maximum p_i possible for the given parameters? // in order to yield a connected network, at least ... double inter_thresh = 1.2d * ((c * Math.Log(c)) / 2d); // ... inter edges are required double intra_edges = m - inter_thresh; // the maximum number of expected intra edges // this yields a maximum value for p_i of ... double max_pi = intra_edges / (c * Combinatorics.Combinations(Nc, 2)); // Explore parameter space for parameters p_in and bias for (double p_in = Properties.Settings.Default.p_in_from; p_in <= max_pi; p_in += Properties.Settings.Default.p_in_step) { Console.WriteLine(); line = ""; for (double bias = Properties.Settings.Default.bias_from; bias <= Properties.Settings.Default.bias_to; bias += Properties.Settings.Default.bias_step) { results = new List<double>(); modularity = new List<double>(); // Parallely start runs for this parameter set ... System.Threading.Tasks.Parallel.For(0, Properties.Settings.Default.runs, j => { // compute the parameter p_e that will give the desired edge number double p_e = (m - c * MathNet.Numerics.Combinatorics.Combinations(Nc, 2) * p_in) / (Combinatorics.Combinations(c * Nc, 2) - c * MathNet.Numerics.Combinatorics.Combinations(Nc, 2)); if (p_e < 0) throw new Exception("probability out of range"); // Create the network ClusterNetwork net = null; do { if (net != null) Console.WriteLine("Network (inter = {0}, intra = {1} not connected ... ", net.InterClusterEdges, net.IntraClusterEdges); net = new ClusterNetwork(Properties.Settings.Default.clusters, Properties.Settings.Default.clusterSize, p_in, p_e); } while (!net.Connected); Console.WriteLine("Run {0}, created cluster network for p_in={1:0.00} with modularity={2:0.00}", j, p_in, (net as ClusterNetwork).NewmanModularity); NETGen.Dynamics.EpidemicSynchronization.EpidemicSynchronization sync = new NETGen.Dynamics.EpidemicSynchronization.EpidemicSynchronization( net, null, v => { Vertex neighbor = v.RandomNeighbor; double r = net.NextRandomDouble(); // classify neighbors List<Vertex> intraNeighbors = new List<Vertex>(); List<Vertex> interNeighbors = new List<Vertex>(); ClassifyNeighbors(net, v, intraNeighbors, interNeighbors); // biasing strategy ... if (r <= bias && interNeighbors.Count > 0) neighbor = interNeighbors.ElementAt(net.NextRandom(interNeighbors.Count)); return neighbor; } ); sync.Run(); NETGen.Dynamics.EpidemicSynchronization.SyncResults res = sync.Collect(); results.Add(res.time); modularity.Add(net.NewmanModularity); }); line = string.Format(new CultureInfo("en-US").NumberFormat, "{0} {1:0.000} {2:0.000} {3:0.000} \t", MathNet.Numerics.Statistics.Statistics.Mean(modularity.ToArray()), bias, MathNet.Numerics.Statistics.Statistics.Mean(results.ToArray()), MathNet.Numerics.Statistics.Statistics.StandardDeviation(results.ToArray())); System.IO.File.AppendAllText(Properties.Settings.Default.ResultFile, line + "\n"); Console.WriteLine("Finished runs for p_in = {0:0.00}, bias = {1:0.00}, Average time = {2:0.000000}", p_in, bias, MathNet.Numerics.Statistics.Statistics.Mean(results.ToArray())); } System.IO.File.AppendAllText(Properties.Settings.Default.ResultFile, "\n"); } Console.ReadKey(); }
static void Main(string[] args) { double bias; try{ // The neighbor selection bias is given as command line argument bias1 = double.Parse(args[0]); bias2 = double.Parse(args[1]); } catch(Exception) { Console.WriteLine("Usage: mono ./DemoSimulation.exe [initial_bias] [secondary_bias]"); return; } // The number of clusters (c) and the nodes within a cluster (Nc) int c = 20; int Nc = 20; // The number of desired edges int m = 6 * c * Nc; // In order to yield a connected network, at least ... double inter_thresh = 3d * ((c * Math.Log(c)) / 2d); // ... edges between communities are required // So the maximum number of edges within communities we s create is ... double intra_edges = m - inter_thresh; Console.WriteLine("Number of intra_edge pairs = " + c * Combinatorics.Combinations(Nc, 2)); Console.WriteLine("Number of inter_edge pairs = " + (Combinatorics.Combinations(c * Nc, 2) - (c * Combinatorics.Combinations(Nc, 2)))); // Calculate the p_i necessary to yield the desired number of intra_edges double pi = intra_edges / (c * Combinatorics.Combinations(Nc, 2)); // From this we can compute p_e ... double p_e = (m - c * MathNet.Numerics.Combinatorics.Combinations(Nc, 2) * pi) / (Combinatorics.Combinations(c * Nc, 2) - c * MathNet.Numerics.Combinatorics.Combinations(Nc, 2)); Console.WriteLine("Generating cluster network with p_i = {0:0.0000}, p_e = {1:0.0000}", pi, p_e); // Create the network ... network = new NETGen.NetworkModels.Cluster.ClusterNetwork(c, Nc, pi, p_e); // ... and reduce it to the GCC network.ReduceToLargestConnectedComponent(); Console.WriteLine("Created network has {0} vertices and {1} edges. Modularity = {2:0.00}", network.VertexCount, network.EdgeCount, network.NewmanModularity); // Run the OopenGL visualization NetworkColorizer colorizer = new NetworkColorizer(); NetworkVisualizer.Start(network, new FruchtermanReingoldLayout(15), colorizer); currentBias = bias1; // Setup the synchronization simulation, passing the bias strategy as a lambda expression sync = new EpidemicSynchronization( network, colorizer, v => { Vertex neighbor = v.RandomNeighbor; double r = network.NextRandomDouble(); // classify neighbors List<Vertex> intraNeighbors = new List<Vertex>(); List<Vertex> interNeighbors = new List<Vertex>(); ClassifyNeighbors(network, v, intraNeighbors, interNeighbors); neighbor = intraNeighbors.ElementAt(network.NextRandom(intraNeighbors.Count)); // biasing strategy ... if (r <= currentBias && interNeighbors.Count > 0) neighbor = interNeighbors.ElementAt(network.NextRandom(interNeighbors.Count)); return neighbor; }, 0.9d); Dictionary<int, double> _groupMus = new Dictionary<int, double>(); Dictionary<int, double> _groupSigmas = new Dictionary<int, double>(); MathNet.Numerics.Distributions.Normal avgs_normal = new MathNet.Numerics.Distributions.Normal(300d, 50d); MathNet.Numerics.Distributions.Normal devs_normal = new MathNet.Numerics.Distributions.Normal(20d, 5d); for(int i=0; i<c; i++) { double groupAvg = avgs_normal.Sample(); double groupStdDev = devs_normal.Sample(); foreach(Vertex v in network.GetNodesInCluster(i)) { sync._MuPeriods[v] = groupAvg; sync._SigmaPeriods[v] = groupStdDev; } } sync.OnStep+=new EpidemicSynchronization.StepHandler(collectLocalOrder); // Run the simulation synchronously sync.Run(); Console.ReadKey(); // Collect and print the results SyncResults res = sync.Collect(); Console.WriteLine("Order {0:0.00} reached after {1} rounds", res.order, res.time); }
private static AggregationResult RunAggregation(ClusterNetwork net, double bias) { Dictionary<Vertex, double> _attributes = new Dictionary<Vertex, double>(); Dictionary<Vertex, double> _aggregates = new Dictionary<Vertex, double>(); MathNet.Numerics.Distributions.Normal normal = new MathNet.Numerics.Distributions.Normal(0d, 5d); AggregationResult result = new AggregationResult(); result.Modularity = net.NewmanModularity; double average = 0d; foreach (Vertex v in net.Vertices) { _attributes[v] = normal.Sample(); _aggregates[v] = _attributes[v]; average += _attributes[v]; } average /= (double)net.VertexCount; double avgEstimate = double.MaxValue; result.FinalVariance = double.MaxValue; result.FinalOffset = 0d; for (int k = 0; k < Properties.Settings.Default.ConsensusRounds; k++) { foreach (Vertex v in net.Vertices.ToArray()) { Vertex w = v.RandomNeighbor; List<Vertex> intraNeighbors = new List<Vertex>(); List<Vertex> interNeighbors = new List<Vertex>(); ClassifyNeighbors(net, v, intraNeighbors, interNeighbors); double r = net.NextRandomDouble(); if (r <= bias && interNeighbors.Count > 0) w = interNeighbors.ElementAt(net.NextRandom(interNeighbors.Count)); _aggregates[v] = aggregate(_aggregates[v], _aggregates[w]); _aggregates[w] = aggregate(_aggregates[v], _aggregates[w]); } avgEstimate = 0d; foreach (Vertex v in net.Vertices.ToArray()) avgEstimate += _aggregates[v]; avgEstimate /= (double)net.VertexCount; result.FinalVariance = 0d; foreach (Vertex v in net.Vertices.ToArray()) result.FinalVariance += Math.Pow(_aggregates[v] - avgEstimate, 2d); result.FinalVariance /= (double)net.VertexCount; double intraVar = 0d; foreach (int c in net.ClusterIDs) { double localavg = 0d; double localvar = 0d; foreach (Vertex v in net.GetNodesInCluster(c)) localavg += _aggregates[v]; localavg /= net.GetClusterSize(c); foreach (Vertex v in net.GetNodesInCluster(c)) localvar += Math.Pow(_aggregates[v] - localavg, 2d); localvar /= net.GetClusterSize(c); intraVar += localvar; } intraVar /= 50d; //Console.WriteLine("i = {0:0000}, Avg = {1:0.000}, Estimate = {2:0.000}, Intra-Var = {3:0.000}, Total Var = {4:0.000}", result.iterations, average, avgEstimate, intraVar, totalVar); } result.FinalOffset = average - avgEstimate; return result; }
public static SIRResult RunSpreading(ClusterNetwork net, double bias, double k, int delay = 0) { SIRResult res = new SIRResult(); Dictionary<Vertex, bool> infections = new Dictionary<Vertex, bool>(); foreach (Vertex v in net.Vertices) infections[v] = false; List<Vertex> infected = new List<Vertex>(); List<Vertex> active = new List<Vertex>(); Vertex seed = net.RandomVertex; infected.Add(seed); active.Add(seed); int i = 0; while (active.Count > 0) { foreach (Vertex v in active.ToArray()) { // Biasing strategy Vertex neighbor = v.RandomNeighbor; double r = net.NextRandomDouble(); // First classify neighbors as intra- or inter-cluster neighbors List<Vertex> intraNeighbors = new List<Vertex>(); List<Vertex> interNeighbors = new List<Vertex>(); ClassifyNeighbors(net, v, intraNeighbors, interNeighbors); Console.Write("Local choice: {0} intra, {1} inter neighbors, ", intraNeighbors.Count, interNeighbors.Count); // with a probability given by bias select a random inter-cluster neighbor if (r <= bias && interNeighbors.Count > 0) neighbor = interNeighbors.ElementAt(net.NextRandom(interNeighbors.Count)); if (net.GetClusterForNode(neighbor) == net.GetClusterForNode(v)) Console.WriteLine("intra-cluster neighbor chosen!"); else Console.WriteLine("inter-cluster neighbor chosen!"); if (neighbor != null && !infections[neighbor]) { infections[neighbor] = true; infected.Add(neighbor); active.Add(neighbor); } else if (neighbor != null) if (net.NextRandomDouble() <= 1d / (double) k) active.Remove(v); } if (delay > 0) System.Threading.Thread.Sleep(delay); i++; } res.Duration = i; res.InfectedRatio = (double)infected.Count / (double)net.VertexCount; res.Modularity = (net as ClusterNetwork).NewmanModularity; return res; }