evoc.label_propagation.label_propagation_init

evoc.label_propagation.label_propagation_init(graph, n_label_prop_iter=20, n_embedding_epochs=50, approx_n_parts=512, n_components=2, scaling=0.1, random_scale=1.0, noise_level=0.5, random_state=None, data=None, recursive_init=True, base_init='pca', base_init_threshold=64, upscaling='partition_expander')[source]

Initialize a node embedding using label propagation on a sparse graph.

This function provides a high-quality initialization for node embeddings by combining graph-based label propagation with hierarchical partitioning. For large graphs, it recursively partitions the data and upscales the results. For small graphs, it uses direct methods (PCA, spectral embedding, or random).

Parameters:

  • graph (scipy.sparse matrix) – A sparse adjacency or weighted graph matrix representing connectivity.

  • n_label_prop_iter (int, default=20) – Number of label propagation iterations to perform on the graph.

  • n_embedding_epochs (int, default=50) – Number of epochs when using node embedding for upscaling.

  • approx_n_parts (int, default=512) – Approximate number of partitions to create for recursive partitioning of large graphs. Useful for controlling memory and computation.

  • n_components (int, default=2) – The number of dimensions in the output embedding.

  • scaling (float, default=0.1) – Scaling factor applied to label propagation distances.

  • random_scale (float, default=1.0) – Scaling factor for random noise in the initialization.

  • noise_level (float, default=0.5) – The noise level parameter passed to node embedding algorithms.

  • random_state (RandomState instance or None, default=None) – Controls the randomness of the algorithm. If None, uses system randomness.

  • data (array-like of shape (n_samples, n_features) or None, default=None) – The original data array. Required if base_init=’pca’. Used for direct initialization methods on small graphs.

  • recursive_init (bool, default=True) – If True, uses recursive partitioning for large graphs. If False, applies the base initialization method directly.

  • base_init ({'pca', 'random', 'spectral', 'mds'}, default='pca') – The initialization method to use for small graphs (when graph size is below base_init_threshold). ‘pca’ requires the data parameter.

  • base_init_threshold (int, default=64) – The size threshold below which the base_init method is used directly. Graphs larger than this use recursive partitioning.

  • upscaling ({'partition_expander', 'node_embedding'}, default='partition_expander') – The method to use when upscaling partitions back to the full graph. ‘partition_expander’ uses a fast expansion method, ‘node_embedding’ uses full node embedding (slower but potentially better quality).

Returns:

embedding – The initialized node embedding based on label propagation and graph structure.

Return type:

array-like of shape (n_vertices, n_components)