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);
}
