/**
 * Structural Representativeness Metrics
 *
 * Compares sampled subgraphs against ground truth between-graphs
 * to measure how well expansion strategies preserve graph structure.
 */
/**
 * Result of structural representativeness comparison.
 */
export interface StructuralRepresentativenessResult {
    /** Coverage: |Sampled ∩ Ground Truth| / |Ground Truth| */
    coverage: number;
    /** Precision: |Sampled ∩ Ground Truth| / |Sampled| */
    precision: number;
    /** F1 score: 2 * (precision * coverage) / (precision + coverage) */
    f1Score: number;
    /** KL divergence of degree distributions */
    degreeKL: number;
    /** Jensen-Shannon divergence (symmetric) */
    degreeJS: number;
    /** Spearman correlation of betweenness centrality rankings */
    betweennessCorrelation: number;
    /** Fraction of ground truth communities with at least one sampled node */
    communityCoverage: number;
    /** Number of nodes in intersection */
    intersectionSize: number;
    /** Number of nodes in sampled but not in ground truth */
    falsePositives: number;
    /** Number of nodes in ground truth but not in sampled */
    falseNegatives: number;
}
/**
 * Compute set overlap metrics between sampled and ground truth node sets.
 *
 * @param sampledNodes - Nodes discovered by expansion method
 * @param groundTruthNodes - Nodes in ground truth between-graph
 * @returns Coverage, precision, and F1 score
 */
export declare const computeSetOverlap: (sampledNodes: Set<string>, groundTruthNodes: Set<string>) => {
    coverage: number;
    precision: number;
    f1Score: number;
    intersection: number;
};
/**
 * Compute Spearman rank correlation between two rankings.
 *
 * @param ranking1 - First ranking (node ID -> rank)
 * @param ranking2 - Second ranking (node ID -> rank)
 * @param commonNodes - Set of nodes to compare (intersection)
 * @returns Spearman correlation coefficient in [-1, 1]
 */
export declare const spearmanRankCorrelation: (ranking1: Map<string, number>, ranking2: Map<string, number>, commonNodes: Set<string>) => number;
/**
 * Convert degree map to betweenness-like ranking.
 *
 * Since computing true betweenness centrality is expensive,
 * we use degree as a proxy (highly correlated in most networks).
 *
 * @param degrees - Map from node ID to degree
 * @returns Map from node ID to rank (1 = highest degree)
 */
export declare const degreeToRanking: (degrees: Map<string, number>) => Map<string, number>;
/**
 * Compute community coverage.
 *
 * @param sampledNodes - Nodes discovered by expansion
 * @param communities - Array of community node sets
 * @returns Fraction of communities with at least one sampled node
 */
export declare const computeCommunityCoverage: (sampledNodes: Set<string>, communities: Array<Set<string>>) => number;
/**
 * Compute comprehensive structural representativeness metrics.
 *
 * @param sampledNodes - Nodes discovered by expansion method
 * @param groundTruthNodes - Nodes in ground truth between-graph
 * @param sampledDegrees - Degrees of sampled nodes
 * @param groundTruthDegrees - Degrees of ground truth nodes
 * @param communities - Optional community partition for coverage metric
 * @returns Complete representativeness metrics
 */
export declare const computeStructuralRepresentativeness: (sampledNodes: Set<string>, groundTruthNodes: Set<string>, sampledDegrees: Map<string, number>, groundTruthDegrees: Map<string, number>, communities?: Array<Set<string>>) => StructuralRepresentativenessResult;
/**
 * Aggregate representativeness results across multiple seed pairs.
 *
 * @param results - Array of individual representativeness results
 * @returns Averaged metrics
 */
export declare const aggregateRepresentativenessResults: (results: StructuralRepresentativenessResult[]) => StructuralRepresentativenessResult;
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