import { Graph } from '../graph/graph';
import { LabelPropagationResult } from '../types/clustering-types';
import { Edge, Node } from '../types/graph';
import { WeightFunction } from '../types/weight-function';
/**
 * Label Propagation clustering algorithm.
 *
 * Fast semi-supervised clustering algorithm that propagates labels through
 * the network based on neighbor voting. Nodes iteratively adopt the most
 * frequent label among their neighbors.
 * @template N - Node type
 * @template E - Edge type
 * @param graph - Input graph (directed or undirected)
 * @param options - Optional configuration
 * @param options.weightFn - Weight function for edges (default: all edges weight 1.0)
 * @param options.maxIterations - Maximum iterations (default: 100)
 * @param options.seed - Random seed for reproducibility (default: Date.now())
 * @returns Result containing clusters or error
 * @example
 * ```typescript
 * const graph = new Graph<PaperNode, CitationEdge>(true);
 * // ... add nodes and edges ...
 *
 * const result = labelPropagation(graph);
 * if (result.ok) {
 *   console.log(`Found ${result.value.clusters.length} clusters`);
 *   console.log(`Converged: ${result.value.metadata.converged}`);
 *   console.log(`Iterations: ${result.value.metadata.iterations}`);
 * }
 * ```
 */
export declare const labelPropagation: <N extends Node, E extends Edge>(graph: Graph<N, E>, options?: {
    weightFn?: WeightFunction<N, E>;
    maxIterations?: number;
    seed?: number;
}) => LabelPropagationResult<N>;
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