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; }