import { Graph } from '../graph/graph';
import { CorePeripheryResult } from '../types/clustering-types';
import { Edge, Node } from '../types/graph';
/**
 * Options for core-periphery decomposition.
 */
export interface CorePeripheryOptions {
    /** Threshold for core membership (default: 0.7) */
    coreThreshold?: number;
    /** Maximum iterations for convergence (default: 100) */
    maxIterations?: number;
    /** Convergence epsilon (default: 0.001) */
    epsilon?: number;
}
/**
 * Perform core-periphery decomposition on a graph using Borgatti-Everett model.
 *
 * Identifies a dense core of highly connected nodes and a sparse periphery.
 * Core nodes have high coreness scores (> threshold) and are densely connected to each other.
 * Periphery nodes have low coreness scores and are sparsely connected.
 * @template N - Node type
 * @template E - Edge type
 * @param graph - Input graph (directed or undirected)
 * @param options - Optional parameters (threshold, iterations, epsilon)
 * @returns Result with core-periphery structure or error
 * @example
 * ```typescript
 * const result = corePeripheryDecomposition(citationGraph, { coreThreshold: 0.7 });
 * if (result.ok) {
 *   console.log(`Core: ${result.value.structure.coreNodes.size} nodes`);
 *   console.log(`Periphery: ${result.value.structure.peripheryNodes.size} nodes`);
 *   console.log(`Fit quality: ${result.value.structure.fitQuality}`);
 * }
 * ```
 */
export declare const corePeripheryDecomposition: <N extends Node, E extends Edge>(graph: Graph<N, E>, options?: CorePeripheryOptions) => CorePeripheryResult<string>;
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