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);
}
}
static void Main(string[] args)
{
GlobValues glob = new GlobValues();
string configFile, dir = Path.GetDirectoryName(Assembly.GetEntryAssembly().Location), inputPath, outputPath;
String[] path = dir.Split(new string[] { "Launch" }, StringSplitOptions.None);
inputPath = path[0] + "Launch" + Path.DirectorySeparatorChar + "input";
configFile = inputPath + Path.DirectorySeparatorChar + "config.param.txt";
// outputPath = path[0] + "Launch" + Path.DirectorySeparatorChar + "output";
Console.WriteLine("File is : " + configFile);
Console.WriteLine("Starting the program to generate the network based on modularity..");
try
{
// Read parameters from param.config file
read_parameters(configFile, glob);
}
catch
{
Console.WriteLine("Usage: mono Demo.exe [nodes] [edges] [clusters] [resultfile]");
return;
}
// Displays the information
Console.WriteLine("Given Parameter values");
Console.WriteLine("\n Nodes: " + glob.nodes + "\n Edges: " + glob.edges + "\n Clusters: " + glob.clusters + "\n Modularity MinValue: " + glob.modularityMinValue + "\n Modularity MaxValue: " + glob.modularityMaxValue);
Console.WriteLine(" Number of runs: " + glob.numberOfGraphs + "\n Coupling probability value: "+glob.couplingProb*100);
string sourceNetworkFile = inputPath = path[0] + "Launch" + Path.DirectorySeparatorChar + "Launch" + Path.DirectorySeparatorChar + "bin" + Path.DirectorySeparatorChar + "Debug" + Path.DirectorySeparatorChar + "network.edges";
string sourceResultFile = inputPath = path[0] + "Launch" + Path.DirectorySeparatorChar + "Launch" + Path.DirectorySeparatorChar + "bin" + Path.DirectorySeparatorChar + "Debug" + Path.DirectorySeparatorChar + "result.dat";
// For loop to make n number of Networks with the given size and modularity ...
double modularity = glob.modularityMinValue;
while (modularity <= glob.modularityMaxValue)
{
String outputFile = "Result_M" + modularity;
outputPath = path[0] + "Launch" + Path.DirectorySeparatorChar + outputFile;
System.IO.Directory.CreateDirectory(outputPath);
try
{
for (int n = 1; n <= glob.numberOfGraphs; n++)
{
network = new ClusterNetwork(glob.nodes, glob.edges, glob.clusters, modularity, true);
// Restricting the modularity value upto 1 decimal place
// modularity = Math.Round(network.NewmanModularityUndirected, 1);
String memberOutputFile = outputPath + Path.DirectorySeparatorChar + "membership.dat";
System.IO.StreamWriter sw = System.IO.File.CreateText(memberOutputFile);
int i = 0;
foreach (Vertex v in network.Vertices)
{
v.Label = (i++).ToString();
sw.WriteLine(network.GetClusterForNode(v).ToString());
}
sw.Close();
Network.SaveToEdgeFile(network, "network.edges");
Console.WriteLine("Created network with {0} vertices, {1} edges and modularity {2:0.00}", network.VertexCount, network.EdgeCount, modularity);
// To move a file or folder to a new location without renaming it. We rename the files after running the Kuramoto model.
string destinationResultFile = outputPath + Path.DirectorySeparatorChar + n + "_res_N" + network.VertexCount + "_E" + network.EdgeCount + "_C" + glob.clusters + "_M" + modularity + "_K" + glob.couplingStrength + ".dat";
string destinationNetworkFile = outputPath + Path.DirectorySeparatorChar + n + "_network_N" + network.VertexCount + "_E" + network.EdgeCount + "_C" + glob.clusters + "_M" + modularity + "_K" + glob.couplingStrength + ".edges";
System.IO.File.Move(outputPath + Path.DirectorySeparatorChar + "membership.dat", outputPath + Path.DirectorySeparatorChar + n + "_mem_N" + network.VertexCount + "_E" + network.EdgeCount + "_C" + glob.clusters + "_M" + modularity + "_K" + glob.couplingStrength + ".dat");
try
{
Console.WriteLine("Moving the generated files to output directory..");
System.IO.File.Move(sourceNetworkFile, destinationNetworkFile);
}
catch (IOException e)
{
Console.WriteLine(e.Message);
}
// Run the Kuramoto model here and store the results in the output directory
NetworkColorizer colorizer = new NetworkColorizer();
// Distribution of natural frequencies
double mean_frequency = 1d;
Normal normal = new Normal(mean_frequency, mean_frequency / 5d);
sync = new Kuramoto(network,
glob.couplingStrength,
glob.couplingProb,
colorizer,
new Func<Vertex, Vertex[]>(v => { return new Vertex[] { v.RandomNeighbor }; })
);
foreach (Vertex v in network.Vertices)
sync.NaturalFrequencies[v] = normal.Sample();
foreach (int g in network.ClusterIDs)
pacemaker_mode[g] = false;
sync.OnStep += new Kuramoto.StepHandler(recordOrder);
Logger.AddMessage(LogEntryType.AppMsg, "Press enter to start synchronization experiment...");
Console.ReadLine();
// Run the simulation
sync.Run();
// Write the time series to the resultfile
if (sourceResultFile != null)
sync.WriteTimeSeries(sourceResultFile);
// Moving results of kuramoto model into output directory
System.IO.File.Move(sourceResultFile, destinationResultFile);
}
}
catch (Exception e)
{
Console.WriteLine("Error: " + e);
}
modularity = modularity + 0.1;
}
// End line of the program
Console.WriteLine("Program ended successfully..");
}
static void Main(string[] args)
{
try
{
// The resultfile is given as command line argument
//nodes = Int32.Parse(args[0]);
//edges = Int32.Parse(args[1]);
//clusters = Int32.Parse(args[2]);
resultfile = args[3];
} catch {
Console.WriteLine("Usage: mono Demo.exe [nodes] [edges] [clusters] [resultfile]");
return;
}
// Create a network of the given size and modularity ...
network = new ClusterNetwork(nodes, edges, clusters, 0.63d, true);
System.IO.StreamWriter sw = System.IO.File.CreateText("membership.dat");
int i = 0;
foreach (Vertex v in network.Vertices)
{
v.Label = (i++).ToString();
sw.WriteLine(network.GetClusterForNode(v).ToString());
}
sw.Close();
Network.SaveToEdgeFile(network, "network.edges");
Console.WriteLine("Created network with {0} vertices, {1} edges and modularity {2:0.00}", network.VertexCount, network.EdgeCount, network.NewmanModularityUndirected);
// Run the real-time visualization
NetworkColorizer colorizer = new NetworkColorizer();
//NetworkVisualizer.Start(network, new NETGen.Layouts.FruchtermanReingold.FruchtermanReingoldLayout(15), colorizer);
//NetworkVisualizer.Layout.DoLayoutAsync();
// Distribution of natural frequencies
double mean_frequency = 1d;
Normal normal = new Normal(mean_frequency, mean_frequency/5d);
sync = new Kuramoto( network,
K,
colorizer,
new Func<Vertex, Vertex[]>(v => { return new Vertex[] {v.RandomNeighbor}; })
);
foreach(Vertex v in network.Vertices)
sync.NaturalFrequencies[v] = normal.Sample();
foreach(int g in network.ClusterIDs)
pacemaker_mode[g] = false;
sync.OnStep += new Kuramoto.StepHandler(recordOrder);
Logger.AddMessage(LogEntryType.AppMsg, "Press enter to start synchronization experiment...");
Console.ReadLine();
// Run the simulation
sync.Run();
// Write the time series to the resultfile
if(resultfile!=null)
sync.WriteTimeSeries(resultfile);
}
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;
}