import { Graph } from '../graph/graph';
import { Partition, PartitioningError } from '../types/clustering-types';
import { Edge, Node } from '../types/graph';
import { Result } from '../types/result';
import { WeightFunction } from '../types/weight-function';
/**
 * Perform spectral graph partitioning.
 *
 * Divides graph into k balanced partitions by analyzing the spectral properties
 * of the graph Laplacian matrix.
 * @template N - Node type
 * @template E - Edge type
 * @param graph - Input graph (directed or undirected)
 * @param k - Number of partitions to create
 * @param options - Optional configuration
 * @param options.weightFn - Weight function for edges (default: all edges weight 1.0)
 * @param options.balanceTolerance - Maximum allowed imbalance (default: 1.2)
 * @param options.maxKMeansIterations - Max iterations for k-means (default: 100)
 * @param options.seed - Random seed for k-means initialization (default: undefined)
 * @returns Result containing array of k partitions or error
 * @example
 * ```typescript
 * const graph = new Graph<PaperNode, CitationEdge>(true);
 * // ... add nodes and edges ...
 *
 * const result = spectralPartition(graph, 4);
 * if (result.ok) {
 *   console.log(`Created ${result.value.length} partitions`);
 * }
 * ```
 */
export declare const spectralPartition: <N extends Node, E extends Edge>(graph: Graph<N, E>, k: number, options?: {
    weightFn?: WeightFunction<N, E>;
    balanceTolerance?: number;
    maxKMeansIterations?: number;
    seed?: number;
}) => Result<Partition<N>[], PartitioningError>;
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