public static Pair<double, double> COC(CliqueCollection cc)
{
double simple_sum = 0, complex_sum = 0;
Matrix CBCO = cc.CliqueByCliqueOverlap;
Vector v = CBCO as Vector;
if (CBCO.Cols == 1)
return new Pair<double, double>(0.0, 0.0);
double denominator = (double)(((double)CBCO.Cols * ((double)CBCO.Cols - 1)) / 2.0);
if (v != null)
{
Vector w = new Vector(v.Size);
w.Clear();
for (int i = 0; i < v.Size; ++i)
{
for (int j = i + 1; j < v.Size; ++j)
w[j] += cc.GetCliqueByCliqueOverlap(i, j);
}
for (int j = 1; j < v.Size; ++j)
{
double sum = w[j];
simple_sum += (sum / v[j]);
if (v[j] > 1)
complex_sum += (sum / (v[j] - 1));
}
}
else
{
for (int j = 1; j < CBCO.Rows; ++j)
{
double sum = 0.0;
for (int i = 0; i < j; ++i)
sum += CBCO[i, j];
simple_sum += sum / CBCO[j, j];
if (CBCO[j, j] > 1)
complex_sum += sum / (CBCO[j, j] - 1);
}
}
return new Pair<double, double>(simple_sum / denominator, complex_sum / denominator);
}
public Vector GetDiagonalVector()
{
if (!IsSquareMatrix)
{
throw new MatrixException("Cannot get diagonal matrix from non-square matrix.");
}
Vector tmp = new Vector(this.Rows);
tmp.Clear();
for (int i = 0; i < _rows; ++i)
{
tmp[i] = this[i, i];
}
tmp.Labels.CopyFrom(_rowLabels);
tmp.NetworkId = NetworkId;
return(tmp);
}
public static Vector operator *(Matrix lhs, Vector rhs)
{
if (lhs.Cols != rhs.Size)
{
throw new MatrixException("Dimensions of matrices do not match for multiplication.");
}
Vector result = new Vector(rhs.Size);
result.Labels.CopyFrom(rhs.Labels);
result.Clear();
for (int r = 0; r < lhs.Rows; r++)
{
for (int i = 0; i < lhs.Cols; i++)
{
result[r] += lhs[r, i] * rhs[i];
}
}
return(result);
}
public static Vector BetweennessCentrality(Matrix m)
{
int N = m.Rows;
Vector BV = new Vector(N);
BV.Clear();
for (int s = 0; s < N; ++s)
{
Queue <int> Q = new Queue <int>();
Q.Enqueue(s);
Stack <int> S = new Stack <int>();
List <List <int> > P = new List <List <int> >(N);
for (int i = 0; i < N; ++i)
{
P.Add(new List <int>());
}
Vector d = new Vector(N);
Algorithms.Fill <double>(d, -1);
d[s] = 0;
Vector sigma = new Vector(N);
sigma[s] = 1;
while (Q.Count > 0)
{
int v = Q.Dequeue();
S.Push(v);
for (int w = 0; w < N; ++w)
{
if (m[v, w] > 0)
{
if (d[w] < 0)
{
Q.Enqueue(w);
d[w] = d[v] + 1;
}
if (d[w] == d[v] + 1)
{
sigma[w] = sigma[w] + sigma[v];
P[w].Add(v);
}
}
}
}
Vector delta = new Vector(N);
delta.Clear();
while (S.Count > 0)
{
int w = S.Pop();
foreach (int v in P[w])
{
delta[v] += (sigma[v] / sigma[w]) * (1 + delta[w]);
}
if (w != s)
{
BV[w] += delta[w];
}
}
}
for (int i = 0; i < BV.Size; ++i)
{
BV[i] *= 100.0 / (((double)N - 1) * ((double)N - 2));
}
return(BV);
}
private static Matrix SimulateLiberalStageTwo(Matrix M, Matrix EA1, Matrix C, Matrix D, Matrix JC, double br)
{
Matrix JCC = JC * C;
Matrix DC = D * C;
int n = M.Rows;
Matrix AM = M * EA1;
Matrix R = new Matrix(n);
for (int i = 0; i < R.Rows; ++i)
for (int j = 0; j < R.Cols; ++j)
if (i != j && (M[i, j] > 0 || AM[i, j] > 0))
R[i, j] = 1;
else
R[i, j] = 0;
Matrix EE = M * M;
Matrix BEE = new Matrix(n);
for (int i = 0; i < R.Rows; ++i)
for (int j = 0; j < R.Cols; ++j)
if (i != j && EE[i, j] > 0)
BEE[i, j] = 1;
else
BEE[i, j] = 0;
Matrix CR = R * C;
Vector AO = new Vector(n);
for (int i = 0; i < AO.Size; ++i)
{
double sum = br * (CR.GetRowSum(i) - C[i, i]);
if (sum <= C[i, i])
AO[i] = 0;
else
AO[i] = sum;
}
Matrix F = Matrix.Ones(n, n);
F.SetDiagonalFromVector(Vector.Zero(n));
Matrix PAN = (F - R) * C;
Matrix PAC = BEE * C;
Matrix EA = Matrix.Zero(n, n);
UpdateEAMatrix(AO, DC, EA);
UpdateEAMatrix(AO, PAC, EA);
UpdateEAMatrix(AO, PAN, EA);
UpdateEAMatrix(AO, JCC, EA);
Matrix BEA = new Matrix(n);
for (int i = 0; i < n; ++i)
for (int j = 0; j < n; ++j)
BEA[i, j] = EA[i, j] < double.Epsilon ? 0 : 1;
Matrix BEAT = BEA.GetTranspose();
AO.Clear();
PAC.Clear();
EA.Clear();
DC.Clear();
return (BEA + BEAT) / 2;
}
public static Vector BetweennessCentrality(Matrix m)
{
int N = m.Rows;
Vector BV = new Vector(N);
BV.Clear();
for (int s = 0; s < N; ++s)
{
Queue<int> Q = new Queue<int>();
Q.Enqueue(s);
Stack<int> S = new Stack<int>();
List<List<int>> P = new List<List<int>>(N);
for (int i = 0; i < N; ++i)
P.Add(new List<int>());
Vector d = new Vector(N);
Algorithms.Fill<double>(d, -1);
d[s] = 0;
Vector sigma = new Vector(N);
sigma[s] = 1;
while (Q.Count > 0)
{
int v = Q.Dequeue();
S.Push(v);
for (int w = 0; w < N; ++w)
{
if (m[v, w] > 0)
{
if (d[w] < 0)
{
Q.Enqueue(w);
d[w] = d[v] + 1;
}
if (d[w] == d[v] + 1)
{
sigma[w] = sigma[w] + sigma[v];
P[w].Add(v);
}
}
}
}
Vector delta = new Vector(N);
delta.Clear();
while (S.Count > 0)
{
int w = S.Pop();
foreach (int v in P[w])
{
delta[v] += (sigma[v] / sigma[w]) * (1 + delta[w]);
}
if (w != s)
{
BV[w] += delta[w];
}
}
}
for (int i = 0; i < BV.Size; ++i)
BV[i] *= 100.0 / (((double)N - 1) * ((double)N - 2));
return BV;
}
public static Vector operator *(Matrix lhs, Vector rhs)
{
if (lhs.Cols != rhs.Size)
throw new MatrixException("Dimensions of matrices do not match for multiplication.");
Vector result = new Vector(rhs.Size);
result.Labels.CopyFrom(rhs.Labels);
result.Clear();
for (int r = 0; r < lhs.Rows; r++)
for (int i = 0; i < lhs.Cols; i++)
result[r] += lhs[r, i] * rhs[i];
return result;
}
