evoc.graph_construction.neighbor_graph_matrix

evoc.graph_construction.neighbor_graph_matrix(n_neighbors, knn_indices, knn_dists, symmetrize=True)[source]

Construct a sparse graph from k-nearest neighbor distances.

Converts k-nearest neighbor indices and distances into a weighted sparse graph matrix using Gaussian kernel weights. Optionally symmetrizes the graph to create an undirected graph.

Parameters:

  • n_neighbors (float) – The effective number of neighbors. Used in the kernel width (sigma) computation via the smooth_knn_dist function.

  • knn_indices (array-like of shape (n_samples, k)) – The indices of the k-nearest neighbors for each sample.

  • knn_dists (array-like of shape (n_samples, k)) – The distances from each sample to its k-nearest neighbors.

  • symmetrize (bool, default=True) – If True, the graph is symmetrized using the formula: A_sym = A + A^T - A * A^T (union of forward and reverse edges). If False, the graph remains directed (asymmetric).

Returns:

graph – A sparse matrix representing the weighted nearest neighbor graph. The (i, j) entry contains the Gaussian kernel weight from sample i to sample j, or 0 if j is not in the k-nearest neighbors of i. If symmetrize=True, the matrix is symmetric and in CSR format. If symmetrize=False, returns a CSR matrix (asymmetric).

Return type:

scipy.sparse._csr_matrix or scipy.sparse._coo_matrix