/**
 * Corpus graph construction + analytics algorithms (#1501).
 *
 * NetworkX-equivalent ports (no graph library): co-author + citation graphs from
 * the #1497 parser, plus h-index, CD-index (disruption), PageRank, betweenness,
 * eigenvector, and clustering. Used by profile-metrics / profile-communities.
 *
 * @source historical: profiles/build_graphs.py, profiles/compute_metrics.py
 * References: Hirsch 2005 (h-index); Wu-Wang-Evans 2019 (CD-index);
 *   Brandes 2001 (betweenness); Newman 2003 (centrality).
 */
import type { RefRecord } from '../corpus-views/ref-parser.js';
/** Directed citation graph: node → {out: refs it cites (in corpus), in: refs that cite it}. */
export interface CitationGraph {
    nodes: string[];
    out: Map<string, Set<string>>;
    in: Map<string, Set<string>>;
}
/** Build the citation graph from corpus outgoing edges (in-corpus targets only). */
export declare function buildCitationGraph(records: RefRecord[]): CitationGraph;
/** Undirected co-author graph: person → (person → shared-paper weight). */
export declare function buildCoauthorGraph(records: RefRecord[]): Map<string, Map<string, number>>;
/** h-index: max k such that ≥k papers have ≥k in-corpus citations (Hirsch 2005). */
export declare function hIndex(citationCounts: number[]): number;
/**
 * CD-index (disruption): (F − B) / (F + B + N). +1 fully disruptive, −1 consolidating.
 * Null when < 3 citers (low confidence). Wu-Wang-Evans 2019.
 */
export declare function cdIndex(ref: string, g: CitationGraph): number | null;
/** PageRank over the directed citation graph (α=0.85), with dangling redistribution. */
export declare function pageRank(g: CitationGraph, alpha?: number, maxIter?: number, tol?: number): Map<string, number>;
/** Brandes betweenness centrality on an undirected unweighted graph, normalized. */
export declare function betweenness(g: Map<string, Map<string, number>>): Map<string, number>;
/** Eigenvector centrality via power iteration on the undirected (weighted) graph. */
export declare function eigenvector(g: Map<string, Map<string, number>>, maxIter?: number, tol?: number): Map<string, number>;
/** Local clustering coefficient per node on the undirected graph. */
export declare function clustering(g: Map<string, Map<string, number>>): Map<string, number>;
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