public void TestClusterNetwork() { ClusterNetwork network = new ClusterNetwork(2000, 5000, 100, 0.9d, false); Assert.LessOrEqual(Math.Abs(network.NewmanModularityUndirected - 0.9d), 0.02); Assert.LessOrEqual(Math.Abs(network.EdgeCount - 5000), 200); Assert.AreEqual(network.VertexCount, 2000); Assert.AreEqual(network.ClusterIDs.Length, 100); Assert.AreEqual(network.EdgeCount, network.InterClusterEdgeNumber + network.IntraClusterEdgeNumber); try{ foreach(Edge e in network.IntraClusterEdges) { int id1 = network.GetClusterForNode(e.Source); int id2 = network.GetClusterForNode(e.Target); Assert.AreEqual(id1, id2); } foreach(Edge e in network.InterClusterEdges) { int id1 = network.GetClusterForNode(e.Source); int id2 = network.GetClusterForNode(e.Target); Assert.AreNotEqual(id1, id2); } foreach(Vertex v in network.Vertices) { int id = network.GetClusterForNode(v); Assert.LessOrEqual(id, network.ClusterIDs.Length); } List<Vertex> vertices = new List<Vertex>(); foreach(int id in network.ClusterIDs) { vertices.AddRange(network.GetNodesInCluster(id)); } Assert.AreEqual(vertices.Count, network.VertexCount); } catch(Exception ex) { Assert.Fail(ex.Message); } }
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; }
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); }