/// <summary>
/// Checks whether two temporal network instances are the same (including the labeling of time steps and the labeling of nodes)
/// </summary>
/// <param name="obj"></param>
/// <returns></returns>
public override bool Equals(object obj)
{
if (obj.GetType() != this.GetType())
{
return(false);
}
TemporalNetwork other = obj as TemporalNetwork;
foreach (int t in this.Keys)
{
if (!other.ContainsKey(t))
{
return(false);
}
else
{
foreach (var edge in this[t])
{
if (!other[t].Contains(edge))
{
return(false);
}
}
}
}
return(true);
}
/// <summary>
/// Creates an instance of a random walk process
/// </summary>
/// <param name="network"></param>
/// <param name="walkmode"></param>
public RandomWalk(TemporalNetwork network, RandomWalkMode walkmode)
{
RandomWalkMode = walkmode;
_network = network;
rand = new Random();
_current_visitations = new Dictionary <string, int>();
_last_visits = new Dictionary <string, int>();
// Set visitations to 0
foreach (string v in network.AggregateNetwork.Vertices)
{
_current_visitations[v] = 0;
}
foreach (string v in network.AggregateNetwork.Vertices)
{
_last_visits[v] = -1000;
}
// Reduce first and second-order aggregate network to strongly connected component
_network.AggregateNetwork.ReduceToLargestStronglyConnectedComponent();
_network.SecondOrderAggregateNetwork.ReduceToLargestStronglyConnectedComponent();
// Initialize random walk
CurrentEdge = StringToTuple(_network.SecondOrderAggregateNetwork.RandomNode);
CurrentNode = CurrentEdge.Item2;
_current_visitations[CurrentNode] = 1;
Time = 1;
// Compute stationary distribution
_stationary_dist = ComputeStationaryDist();
InitializeCumulatives();
}
/// <summary>
/// Computes the baseline betweenness preference matrix of a node under the assumption
/// that the temporal network does not contain a betweenness preference correlation. This corresponds to
/// equation (5) in the paper.
/// </summary>
/// <param name="temp_net">The temporal network for which to compute the matrix</param>
/// <param name="x">The node to compute the baseline betweenness preference for</param>
/// <param name="ego_net">The weighted, aggregate ego network of node x</param>
/// <param name="index_pred">Indices of predecessor nodes in the betweenness preference matrix</param>
/// <param name="index_succ">Indices of successor nodes in the betweenness preference matric</param>
/// <param name="normalize">Whether or not to normalize the betweenness preference matrix (i.e. whether B or P shall be returned)</param>
/// <returns>Depending on the normalization, a betweenness preference matrix B or the normalized version P will be returned</returns>
public static double[,] GetUncorrelatedBetweennessPrefMatrix(TemporalNetwork temp_net, string x, out Dictionary <string, int> index_pred, out Dictionary <string, int> index_succ)
{
// Use a mapping of indices to node labels
index_pred = new Dictionary <string, int>();
index_succ = new Dictionary <string, int>();
// Compute the matrix from the weighted ego network
return(GetUncorrelatedBetweennessPrefMatrix(temp_net.AggregateNetwork, x, out index_pred, out index_succ));
}
/// <summary>
/// Creates a temporal network instance from an ordered sequence of edges
/// </summary>
/// <param name="sequence">An ordered sequence of edges</param>
/// <returns>An instance of a temporal network</returns>
public static TemporalNetwork FromEdgeSequence(List <Tuple <string, string> > sequence)
{
TemporalNetwork net = new TemporalNetwork();
int time = 0;
foreach (Tuple <string, string> t in sequence)
{
net.AddTemporalEdge(time++, t.Item1, t.Item2);
}
return(net);
}
/// <summary>
/// Computes the (scalar) betwenness preference of a node
/// </summary>
/// <param name="temp_net">The temporal network for which to compute betweenness preference</param>
/// <param name="x">The node for which to compute betweenness preference</param>
/// <returns>The betweenness preference, defined as the mutual information of the source and target of two-paths</returns>
public static double GetBetweennessPref(TemporalNetwork temp_net, string x)
{
// This will be used to store the index mappings in the betweenness preference matrix
Dictionary <string, int> index_pred;
Dictionary <string, int> index_succ;
// Compute the normalized betweenness preference matrix of x according to equation (2) and (3)
double[,] P = GetBetweennessPrefMatrix(temp_net, x, out index_pred, out index_succ);
return(GetBetweennessPref(temp_net.AggregateNetwork, x, P));
}
/// <summary>
/// Creates a random temporal network by shuffling the edges present in an original weighted network
/// </summary>
/// <param name="net">The weighted network to draw the microstate from</param>
/// <param name="length">The length of the sequence in terms of the number of time-stamped interactions</param>
/// <returns></returns>
public static TemporalNetwork ShuffleEdges(TemporalNetwork net, int length = 0, int precision = 1000)
{
// If no length is specified (i.e. length has the default value of 0), use the length of the original network
length = length > 0 ? length : (int)net.AggregateNetwork.CumulativeWeight;
int lcm = 1;
foreach (var twopath in net.TwoPathWeights.Keys)
{
int whole, numerator, denominator;
MathHelper.RoundToMixedFraction(net.TwoPathWeights[twopath], precision, out whole, out numerator, out denominator);
lcm = MathHelper.LCM(lcm, denominator);
}
// Collect edges in two sampling sets for in and outgoing edges of two paths
List <Tuple <string, string> > in_edges = new List <Tuple <string, string> >();
List <Tuple <string, string> > out_edges = new List <Tuple <string, string> >();
string[] split = null;
foreach (var twopath in net.TwoPathWeights.Keys)
{
for (int k = 0; k < Math.Round(net.TwoPathWeights[twopath] * lcm); k++)
{
split = twopath.Split(',');
in_edges.Add(new Tuple <string, string>(split[0], split[1]));
out_edges.Add(new Tuple <string, string>(split[1], split[2]));
}
}
TemporalNetwork output = new TemporalNetwork();
int l = 0;
while (l < length)
{
// Draw edges uniformly at random and add them to temporal network
var edge1 = in_edges.ElementAt(rand.Next(in_edges.Count));
Tuple <string, string> edge2 = null;
while (edge2 == null)
{
edge2 = out_edges.ElementAt(rand.Next(out_edges.Count));
if (edge1.Item2 != edge2.Item1)
{
edge2 = null;
}
}
// Add to the output network
output.AddTemporalEdge(l++, edge1.Item1, edge1.Item2);
output.AddTemporalEdge(l++, edge2.Item1, edge2.Item2);
}
return(output);
}
/// <summary>
/// Creates a weighted network representation of a temporal network by aggregating edge occurences
/// (and thus discarding information on the temporal ordering of edges)
/// </summary>
/// <param name="temp_net">he temporal network that shall be aggregated</param>
/// <returns>An instance of a weighted aggregate network</returns>
public static WeightedNetwork FromTemporalNetwork(TemporalNetwork temp_net)
{
WeightedNetwork weighted_net = new WeightedNetwork();
foreach (var t in temp_net.Keys)
{
foreach (Tuple <string, string> edge in temp_net[t])
{
weighted_net.AddEdge(edge.Item1, edge.Item2);
}
}
return(weighted_net);
}
/// <summary>
/// Saves a temporal network to a file
/// </summary>
/// <param name="path">The path to which the file shall be saved. Any existing file will be overwritten silently.</param>
/// <param name="net">The temporal network to save.</param>
public static void SaveToFile(string path, TemporalNetwork net)
{
StringBuilder sb = new StringBuilder();
sb.AppendLine("time node1 node2");
foreach (int t in net.Keys)
{
foreach (var edge in net[t])
{
sb.AppendLine(t + " " + edge.Item1 + " " + edge.Item2);
}
}
System.IO.File.WriteAllText(path, sb.ToString());
}
/// <summary>
/// Parallely computes the betweenness preference distribution of all nodes in a temporal network
/// </summary>
/// <param name="temp_net">The temporal network to analyze</param>
/// <returns>An IEnumerable containing betweenness preferences of all nodes</returns>
public static IEnumerable <double> GetBetweennessPrefDist(TemporalNetwork temp_net)
{
List <double> dist = new List <double>(temp_net.AggregateNetwork.VertexCount);
Parallel.ForEach <string>(temp_net.AggregateNetwork.Vertices, v =>
{
double betweennessPref = BetweennessPref.GetBetweennessPref(temp_net, v);
// Synchronized access to the list
if (temp_net.AggregateNetwork.GetIndeg(v) > 0 && temp_net.AggregateNetwork.GetOutdeg(v) > 0)
{
lock (dist)
dist.Add(betweennessPref);
}
});
return(dist);
}
/// <summary>
/// Computes the betweenness preference matrix of a node based on the set of two-paths of a node.
/// By this we essentially implement equations (1) and (2).
/// If additionally the normalization parameter is set, equation (3) will be computed.
/// </summary>
/// <param name="temp_net">The temporal network to compute betweeness preference for</param>
/// <param name="x">The node for which to compute the betweenness preference matrix</param>
/// <param name="ego_net">The weighted, aggregate ego network</param>
/// <param name="index_pred">A mapping of nodes to columns in the betweenness preference matrix</param>
/// <param name="index_succ">A mapping of nodes to rows in the betweenness preference matrix</param>
/// <param name="normalize">Whether or not to normalize the matrix, i.e. whether B (eq. 2) or P (eq. 3) shall be returned</param>
/// <returns>Depending on the normalization, a betweenness preference matrix B or the normalized version P will be returned</returns>
public static double[,] GetBetweennessPrefMatrix(TemporalNetwork temp_net,
string x,
out Dictionary <string, int> index_pred,
out Dictionary <string, int> index_succ,
bool normalized = true)
{
// Use a mapping of indices to node labels
index_pred = new Dictionary <string, int>();
index_succ = new Dictionary <string, int>();
// Create an empty matrix
double[,] B = new double[temp_net.AggregateNetwork.GetIndeg(x), temp_net.AggregateNetwork.GetOutdeg(x)];
// Create the index-to-node mapping
int i = 0;
foreach (string u in temp_net.AggregateNetwork.GetPredecessors(x))
{
index_pred[u] = i++;
}
i = 0;
foreach (string w in temp_net.AggregateNetwork.GetSuccessors(x))
{
index_succ[w] = i++;
}
// Here we implement equation (1) and (2), i.e. we normalize PER TIME STEP
foreach (var t in temp_net.TwoPathsByNode[x].Keys)
{
foreach (var two_path in temp_net.TwoPathsByNode[x][t])
{
B[index_pred[two_path.Item1], index_succ[two_path.Item2]] += 1d / (double)temp_net.TwoPathsByNode[x][t].Count;
}
}
// This is equation 3, i.e. we normalize ACROSS THE MATRIX
if (normalized)
{
return(NormalizeMatrix(x, temp_net.AggregateNetwork, B));
}
else
{
return(B);
}
}
/// <summary>
/// Reads a temporal network from a file containing lines with trigrams a,b,c
/// </summary>
/// <param name="path">The path to the data file</param>
/// <returns>A temporal network instance</returns>
public static TemporalNetwork FromTrigramsFile(string path)
{
TemporalNetwork temp_net = new TemporalNetwork();
string[] lines = System.IO.File.ReadAllLines(path);
// if empty
if (lines.Length == 0)
{
return(temp_net);
}
char[] split_chars = new char[] { ' ', '\t', ';', ',' };
char split_char = ' ';
// detect split character
string[] line = null;
foreach (char c in split_chars)
{
line = lines[0].Split(c);
if (line.Length == 3)
{
split_char = c;
break;
}
}
// Read trigrams and generate temporal network representation
int time = 0;
for (int i = 0; i < lines.Length; i++)
{
string[] components = lines[i].Split(split_char);
temp_net.AddTemporalEdge(time, components[0], components[1]);
temp_net.AddTemporalEdge(time + 1, components[1], components[2]);
time = time + 3;
}
return(temp_net);
}
/// <summary>
/// Reads an edge sequence from a file containing ordered edges and creates a temporal network instance. The file is
/// assumed to have one line per edge, each possibly consisting of several colums and separated by a special delimiter character.
/// The caller can specify which (zero-based) column number indicate the source and the target of an interaction. If no parameters
/// are specified apart from the path, each line in the file is assumed to contain two strings representing the source and target of an
/// edge in the first two columns separated by a space. No header line is assumed by default.
/// </summary>
/// <param name="path">The path of the file containing the edge sequence.</param>
/// <param name="source_col">The zero-based column number on the source node of an edge. 0 by default</param>
/// <param name="target_col">The zero-based column number on the target node of an edge. 1 by default</param>
/// <param name="header">Whether or not there is a header line that shall be ignored</param>
/// <param name="split_char">The character used to separate columns in each line</param>
/// <returns>An instance of a temporal network corresponding to the input sequence</returns>
public static TemporalNetwork ReadFromFile(string path, bool undirected = false)
{
TemporalNetwork temp_net = new TemporalNetwork();
// Read all data from file
string[] lines = System.IO.File.ReadAllLines(path);
// If empty ...
if (lines.Length == 0)
{
return(temp_net);
}
// Extract header
char[] split_chars = new char[] { ' ', '\t', ';', ',' };
char split_char = ' ';
// Detect the correct separator character in CSV format
string[] header = null;
foreach (char c in split_chars)
{
header = lines[0].Split(c);
if (header.Length >= 2 && ((header.Contains("node1") && header.Contains("node2")) || (header.Contains("source") && header.Contains("target"))))
{
split_char = c;
break;
}
}
if (header.Length < 2)
{
return(temp_net);
}
// Extract indices of columns
int time_ix = -1;
int source_ix = -1;
int target_ix = -1;
for (int i = 0; i < header.Length; i++)
{
if (header[i] == "time")
{
time_ix = i;
}
else if (header[i] == "node1" || header[i] == "source")
{
source_ix = i;
}
else if (header[i] == "node2" || header[i] == "target")
{
target_ix = i;
}
}
// If there is no source and target column
if (source_ix < 0 || target_ix < 0)
{
return(temp_net);
}
for (int i = 1; i < lines.Length; i++)
{
string[] components = lines[i].Split(split_char);
if (components[source_ix] != "" && components[target_ix] != "")
{
// If there is no explicit time, just consider each edge occuring at consecutive time steps
int t = time_ix >= 0 ? int.Parse(components[time_ix]) : i;
temp_net.AddTemporalEdge(t, components[source_ix], components[target_ix]);
if (undirected)
{
temp_net.AddTemporalEdge(t, components[target_ix], components[source_ix]);
}
}
}
return(temp_net);
}
public static IDictionary <int, double> RunRW_BWP(TemporalNetwork temp_net, int max_steps = 100000, bool null_model = false)
{
Random r = new Random();
var cumulatives = new Dictionary <Tuple <string, string>, Dictionary <double, string> >();
var sums = new Dictionary <Tuple <string, string>, double>();
// Dictionary<string, Dictionary<double, string>> cumulatives =
// Dictionary<string, double> sums = new Dictionary<string, double>();
Dictionary <string, Dictionary <string, int> > indices_pred = new Dictionary <string, Dictionary <string, int> >();
Dictionary <string, Dictionary <string, int> > indices_succ = new Dictionary <string, Dictionary <string, int> >();
Dictionary <string, double[, ]> matrices = new Dictionary <string, double[, ]>();
Dictionary <string, int> visitations = new Dictionary <string, int>();
Dictionary <string, double> stationary = new Dictionary <string, double>();
Dictionary <Tuple <string, string>, int> edge_visitations = new Dictionary <Tuple <string, string>, int>();
Dictionary <Tuple <string, string>, double> edge_stationary = new Dictionary <Tuple <string, string>, double>();
Dictionary <int, double> tvd = new Dictionary <int, double>();
// Aggregate network
WeightedNetwork network = temp_net.AggregateNetwork;
// Read analytical stationary distribution (i.e. flow-corrected edge weights) from disk
string[] lines = System.IO.File.ReadAllLines("stationary_dist_RM.dat");
foreach (string x in lines)
{
string[] split = x.Split(' ');
string[] nodes = split[0].Split('.');
var edge = new Tuple <string, string>(nodes[0], nodes[1]);
// Extract stationary dist, set visitations to zero and adjust edge weights ...
edge_stationary[edge] = double.Parse(split[1], System.Globalization.CultureInfo.GetCultureInfo("en-US").NumberFormat);
edge_visitations[edge] = 0;
network[edge] = edge_stationary[edge];
}
// Compute stationary dist of vertices ...
double total = 0d;
foreach (string x in network.Vertices)
{
stationary[x] = 0d;
foreach (string s in network.GetPredecessors(x))
{
stationary[x] += edge_stationary[new Tuple <string, string>(s, x)];
}
total += stationary[x];
}
foreach (string x in network.Vertices)
{
stationary[x] = stationary[x] / total;
}
// Compute betweenness preference matrices
if (!null_model)
{
Console.Write("Computing betweenness preference in temporal network ...");
}
else
{
Console.Write("Calculating null model betweenness preference ...");
}
foreach (string x in network.Vertices)
{
var ind_p = new Dictionary <string, int>();
var ind_s = new Dictionary <string, int>();
if (!null_model)
{
matrices[x] = BetweennessPref.GetBetweennessPrefMatrix(temp_net, x, out ind_p, out ind_s, false);
}
else
{
matrices[x] = BetweennessPref.GetUncorrelatedBetweennessPrefMatrix(temp_net, x, out ind_p, out ind_s);
}
indices_pred[x] = ind_p;
indices_succ[x] = ind_s;
}
Console.WriteLine("done.");
// Initialize visitations, stationary distribution and cumulatives ...
foreach (string x in network.Vertices)
{
visitations[x] = 0;
stationary[x] = 0d;
foreach (string s in indices_pred[x].Keys)
{
Tuple <string, string> key = new Tuple <string, string>(s, x);
stationary[x] += network.GetWeight(s, x);
// Compute the transition probability for a edge (x,t) given that we are in (s,x)
cumulatives[key] = new Dictionary <double, string>();
double sum = 0d;
foreach (string t in indices_succ[x].Keys)
{
double transition_prob = 0d;
string two_path = s + "," + x + "," + t;
transition_prob = matrices[x][indices_pred[x][s], indices_succ[x][t]];
if (transition_prob > 0)
{
sum += transition_prob;
cumulatives[key][sum] = t;
}
}
sums[key] = sum;
}
}
// Draw two initial nodes ...
string pred = network.RandomNode;
string current = network.GetRandomSuccessor(pred);
visitations[pred] = 1;
visitations[current] = 1;
edge_visitations[new Tuple <string, string>(pred, current)] = 1;
// Run the random walk (over edges)
for (int t = 0; t < max_steps; t++)
{
// The edge via which we arrived at the current node
Tuple <string, string> current_edge = new Tuple <string, string>(pred, current);
// If this happens, we are stuck in a sink, i.e. there is no out edge
System.Diagnostics.Debug.Assert(sums[current_edge] > 0, string.Format("Network not strongly connected! RW stuck after passing through edge {0}", current_edge));
// Draw a sample uniformly from [0,1] and multiply it with the cumulative sum for the current edge ...
double sample = rand.NextDouble() * sums[current_edge];
// Determine the next transition ...
string next_node = null;
for (int i = 0; i < cumulatives[current_edge].Count; i++)
{
if (cumulatives[current_edge].Keys.ElementAt(i) > sample)
{
next_node = cumulatives[current_edge].Values.ElementAt(i);
break;
}
}
pred = current;
current = next_node;
visitations[current] = visitations[current] + 1;
edge_visitations[new Tuple <string, string>(pred, current)] = edge_visitations[new Tuple <string, string>(pred, current)] + 1;
tvd[t] = TVD(visitations, stationary);
}
return(tvd);
}
/// <summary>
/// Runs a simple SI spreading on a given temporal network and returns the dynamics of the infections
/// </summary>
/// <param name="temp_net">The temporal network to use</param>
/// <param name="p">The infection probability (default is 1)</param>
/// <returns>An enumerable of infection numbers, each entry giving the number of infected individuals at a certain time of the simulation</returns>
public static string RunSpreading(TemporalNetwork temp_net, out string times, double p = 1d)
{
// Which nodes are already infected?
Dictionary <string, bool> discovered = new Dictionary <string, bool>();
Dictionary <int, int> infections = new Dictionary <int, int>(temp_net.Count);
// Initialize
foreach (string s in temp_net.AggregateNetwork.Vertices)
{
discovered[s] = false;
}
// Build the initial matrix of fastestPathLengths
Dictionary <string, int> indices = new Dictionary <string, int>(temp_net.AggregateNetwork.VertexCount);
short[,] fastestPathLengths = new short[temp_net.AggregateNetwork.VertexCount, temp_net.AggregateNetwork.VertexCount];
short[,] fastestPath_mid_Lengths = new short[temp_net.AggregateNetwork.VertexCount, temp_net.AggregateNetwork.VertexCount];
uint[,] fastestPathDurations = new uint[temp_net.AggregateNetwork.VertexCount, temp_net.AggregateNetwork.VertexCount];
// Build the indices
int i = 0;
foreach (string s in temp_net.AggregateNetwork.Vertices)
{
indices[s] = i++;
}
// Initialize with maximum values ...
foreach (string s in temp_net.AggregateNetwork.Vertices)
{
foreach (string d in temp_net.AggregateNetwork.Vertices)
{
fastestPathLengths[indices[s], indices[d]] = short.MaxValue;
fastestPath_mid_Lengths[indices[s], indices[d]] = short.MaxValue;
fastestPathDurations[indices[s], indices[d]] = uint.MaxValue;
}
}
// Run a spreading process starting at each node in the network ...
foreach (var start in temp_net.AggregateNetwork.Vertices)
{
// Reset the infected state for all
foreach (string s in temp_net.AggregateNetwork.Vertices)
{
discovered[s] = false;
}
// Infect the start node
int discovered_n = 1;
discovered[start] = true;
fastestPathLengths[indices[start], indices[start]] = 0;
fastestPathDurations[indices[start], indices[start]] = 0;
try
{
// Get the first time stamp where the start node was source of two path
var start_time = temp_net.TwoPathsByStartTime.Keys.First(z =>
{
foreach (string twopath in temp_net.TwoPathsByStartTime[z])
{
if (twopath.StartsWith(start))
{
return(true);
}
}
return(false);
});
uint t_ = (uint)start_time;
// Create a list of ordered time stamps starting with the first activity of the start node
var time_stamps = from x in temp_net.TwoPathsByStartTime.Keys where x >= start_time orderby x ascending select x;
// Extract all paths consisting of "two path" building blocks
foreach (int t in time_stamps)
{
// A path continues if the source node of a two-path has already been discovered
foreach (var two_path in temp_net.TwoPathsByStartTime[t])
{
// Get the three components of the two path
string[] comps = two_path.Split(',');
// Record the geodesic distance between the start and the middle node comps[1]
fastestPath_mid_Lengths[indices[start], indices[comps[1]]] = (short)Math.Min(fastestPath_mid_Lengths[indices[start], indices[comps[1]]], fastestPathLengths[indices[start], indices[comps[0]]] + 1);
// If the target node of a two path has not been discovered before, we just discovered a new fastest time-respecting path!
if (discovered[comps[0]] && !discovered[comps[2]])
{
// Add to nodes already discovered
discovered_n++;
discovered[comps[2]] = true;
// Record geodesic distance of fastest time-respecting path
fastestPathLengths[indices[start], indices[comps[2]]] = (short)(fastestPathLengths[indices[start], indices[comps[0]]] + 2);
// Record temporal length of fastest time-respecting path
fastestPathDurations[indices[start], indices[comps[2]]] = (uint)(t - start_time);
}
}
// Stop as soon as all nodes have been discovered
if (discovered_n == temp_net.AggregateNetwork.VertexCount)
{
break;
}
// Advance the time by two
t_ += 2;
}
}
catch (Exception) {
// In this case, a start node is never the source of two-path, so we just ignore it ...
}
}
// Aggregate the matrices
foreach (string s in temp_net.AggregateNetwork.Vertices)
{
foreach (string d in temp_net.AggregateNetwork.Vertices)
{
fastestPathLengths[indices[s], indices[d]] = Math.Min(fastestPathLengths[indices[s], indices[d]], fastestPath_mid_Lengths[indices[s], indices[d]]);
}
}
StringBuilder sb = new StringBuilder();
StringBuilder sb_t = new StringBuilder();
var nodes = from n in temp_net.AggregateNetwork.Vertices.AsParallel() orderby n ascending select n;
foreach (string s in nodes)
{
foreach (string d in nodes)
{
sb.Append(fastestPathLengths[indices[s], indices[d]] + "\t");
sb_t.Append(fastestPathDurations[indices[s], indices[d]] + "\t");
}
sb.Append("\n");
sb_t.Append("\n");
}
times = sb_t.ToString();
return(sb.ToString());
}
/// <summary>
/// Creates a TikZ representation of the temporal unfolding of the temporal network
/// </summary>
/// <param name="path">The path to which to write the tikz file</param>
/// <param name="temp_net">The temporal network that shall be exported</param>
public static void CreateTikzUnfolding(string path, string between_node, TemporalNetwork temp_net)
{
WeightedNetwork net = WeightedNetwork.FromTemporalNetwork(temp_net);
StringBuilder strB = new StringBuilder();
strB.AppendLine("\\newcounter{a}");
strB.AppendLine("\\begin{tikzpicture}[->,>=stealth',auto,scale=0.5, every node/.style={scale=0.9}]");
strB.AppendLine("\\tikzstyle{node} = [fill=lightgray,text=black,circle]");
strB.AppendLine("\\tikzstyle{v} = [fill=black,text=white,circle]");
strB.AppendLine("\\tikzstyle{dst} = [fill=lightgray,text=black,circle]");
strB.AppendLine("\\tikzstyle{lbl} = [fill=white,text=black,circle]");
string last = "";
foreach (string v in net.Vertices.OrderBy(s => s))
{
if (last == "")
{
strB.AppendLine("\\node[lbl] (" + v + "-0) {$" + v + "$};");
}
else
{
strB.AppendLine("\\node[lbl,right=0.5cm of " + last + "-0] (" + v + "-0) {$" + v + "$};");
}
last = v;
}
strB.AppendLine("\\setcounter{a}{0}");
strB.AppendLine("\\foreach \\number in {1,...," + (temp_net.Length + 1) + "}{");
strB.AppendLine("\\setcounter{a}{\\number}");
strB.AppendLine("\\addtocounter{a}{-1}");
strB.AppendLine("\\pgfmathparse{\\thea}");
foreach (string v in net.Vertices)
{
if (v != between_node)
{
strB.AppendLine("\\node[node,below=0.3cm of " + v + "-\\pgfmathresult] (" + v + "-\\number) {};");
}
else
{
strB.AppendLine("\\node[v,below=0.3cm of " + v + "-\\pgfmathresult] (" + v + "-\\number) {};");
}
}
strB.AppendLine("\\node[lbl,left=0.5cm of " + net.Vertices.OrderBy(s => s).ElementAt(0) + "-\\number] (col-\\pgfmathresult) {$t=$\\number};");
strB.AppendLine("}");
strB.AppendLine("\\path[->,thick]");
int i = 1;
foreach (var t in temp_net.Keys)
{
foreach (var edge in temp_net[t])
{
strB.AppendLine("(" + edge.Item1 + "-" + (t + 1) + ") edge (" + edge.Item2 + "-" + (t + 2) + ")");
i++;
}
}
strB.AppendLine(";");
strB.AppendLine("\\end{tikzpicture} ");
System.IO.File.WriteAllText(path, strB.ToString());
}
/// <summary>
/// This method creates a random sequence of two paths, where the betweenness preferences as well as edge weights match those of a given temporal network.
/// The model implemented here can be viewd in two different ways:
/// 1.) It can be seen as a reshuffling of two paths realized in a given temporal network, while destroying other correlations like bursty activity patterns
/// 2.) Alternatively, it can be seen as a random sampling based on the betweenness preference matrices of nodes as computed from an empirical network
/// </summary>
/// <param name="temp_net">The temporal network based on which a randomized sequence of two paths will be created</param>
/// <param name="length">The length of the sequence in terms of the number of time-stamped interactions</param>
/// <param name="precision">The numerical precision that will be used when sampling from the distribution. This
/// at the same time affects the memory requirements of the procedure, which is at most precision*two_paths in the input network</param>
/// <returns>A temporal network that preserves betweenness preferences as well as the aggregated network of the input</returns>
public static TemporalNetwork ShuffleTwoPaths(TemporalNetwork temp_net, int length = 0, int precision = 1000)
{
// If no length is specified (i.e. length has the default value of 0), use the length of the original sequence
length = length > 0 ? length : temp_net.Length;
// This will take the betweenness pref. matrices of all nodes ...
Dictionary <string, double[, ]> betweenness_matrices = new Dictionary <string, double[, ]>();
Dictionary <string, Dictionary <string, int> > pred_indices = new Dictionary <string, Dictionary <string, int> >();
Dictionary <string, Dictionary <string, int> > succ_indices = new Dictionary <string, Dictionary <string, int> >();
int lcm = 1;
// Compute unnormalized betweenness preference matrices of all nodes in the aggregate network
foreach (string x in temp_net.AggregateNetwork.Vertices)
{
Dictionary <string, int> preds = new Dictionary <string, int>();
Dictionary <string, int> succs = new Dictionary <string, int>();
betweenness_matrices[x] = BetweennessPref.GetBetweennessPrefMatrix(temp_net, x, out preds, out succs, normalized: false);
pred_indices[x] = preds;
succ_indices[x] = succs;
// Compute the least common multiple of the denominators of all entries ...
// Eventually we will multiply ALL entries of the matrices of ALL nodes with the LCM in order to
// ensure that all two paths are represented with the correct relative frequencies
foreach (string s in temp_net.AggregateNetwork.GetPredecessors(x))
{
foreach (string d in temp_net.AggregateNetwork.GetSuccessors(x))
{
int whole, numerator, denominator;
MathHelper.RoundToMixedFraction(betweenness_matrices[x][pred_indices[x][s], succ_indices[x][d]], precision, out whole, out numerator, out denominator);
lcm = MathHelper.LCM(lcm, denominator);
}
}
}
List <string> sampling_set = new List <string>();
// Create a list of two_paths whose relative frequencies match the betweenness preference matrix...
foreach (string v in betweenness_matrices.Keys)
{
// Add source and target of two paths to list according to their relative frequencies in the matrices
foreach (string s in temp_net.AggregateNetwork.GetPredecessors(v))
{
foreach (string d in temp_net.AggregateNetwork.GetSuccessors(v))
{
for (int k = 0; k < Math.Round(betweenness_matrices[v][pred_indices[v][s], succ_indices[v][d]] * lcm); k++)
{
sampling_set.Add(s + "," + v + "," + d);
}
}
}
}
// Create an empty temporal network
TemporalNetwork microstate = new TemporalNetwork();
int time = 0;
// Draw two-paths at random from the sampling set, this is equivalent to reshuffling existing two paths of the original sequence
// However, it additionally correctly accounts for continued two paths (in which case an edge overlaps) and for multiple edges in
// a single step (such two paths will be counted fractionally)
for (int l = 0; l < length / 2; l++)
{
// Draw a random two path
int r = rand.Next(sampling_set.Count);
string tp = sampling_set[r];
string[] nodes = tp.Split(',');
// Add two temporal edges
microstate.AddTemporalEdge(time++, nodes[0], nodes[1]);
microstate.AddTemporalEdge(time++, nodes[1], nodes[2]);
}
return(microstate);
}
