import { Graph } from '../graph/graph';
import { InfomapResult } from '../types/clustering-types';
import { Edge, Node } from '../types/graph';
import { WeightFunction } from '../types/weight-function';
/**
 * Detect communities using the Infomap algorithm.
 *
 * Infomap uses information theory to find communities by minimizing the
 * description length (map equation) of random walks on the network.
 * @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 for optimization (default: 100)
 * @param options.numTrials - Number of random trials for greedy search (default: 10)
 * @param options.seed - Random seed for reproducibility (default: undefined)
 * @returns Result containing modules with compression ratio
 * @example
 * ```typescript
 * const graph = new Graph<PaperNode, CitationEdge>(true);
 * // ... add nodes and edges ...
 *
 * const result = infomap(graph);
 * if (result.ok) {
 *   console.log(`Found ${result.value.modules.length} modules`);
 *   console.log(`Compression ratio: ${result.value.compressionRatio}`);
 * }
 * ```
 */
export declare const infomap: <N extends Node, E extends Edge>(graph: Graph<N, E>, options?: {
    weightFn?: WeightFunction<N, E>;
    maxIterations?: number;
    numTrials?: number;
    seed?: number;
}) => InfomapResult<N>;
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