import { type GraphInstance } from './graphology-compat.js';
import type { Community } from '../types.js';
/**
 * Shape of a community-member node for summary generation — a `title` plus the
 * `tags` frontmatter list. Decoupled from the graphology graph so callers
 * without a live graph (e.g. the write-path prune in `store/communities.ts`)
 * can regenerate summaries from a SQLite lookup.
 */
export interface SummaryMember {
    title: string;
    tags: string[];
}
/**
 * Render a human-readable community summary from its live members.
 *
 * Shape: `"Key members: T1, T2, .... Tags: a, b, c. N nodes total."` — the
 * tag clause is omitted when no tags exist. Expects `members` to already be
 * sorted in display order (typically by PageRank descending).
 *
 * `totalCount` defaults to `members.length` but can be overridden when the
 * caller has some ids that aren't resolvable to a `SummaryMember` (e.g. a
 * half-stale cache row where an id no longer exists in the node store) and
 * wants the trailing `N nodes total` to reflect the authoritative count
 * rather than just the nodes that had title/tag data.
 *
 * Extracted so both the Louvain first-pass (`detectCommunities` below) and
 * the write-path cache prune (`pruneNodeFromCommunities` in the store)
 * produce identical strings. Without this, the `summary` field drifts out of
 * sync with `nodeIds` on every delete.
 */
export declare function buildSummary(members: SummaryMember[], totalCount?: number): string;
/**
 * Louvain community detection with a heuristic summary per community.
 *
 * For each detected community we:
 *  - Sort members by PageRank and take the top member as the community label.
 *  - List the titles of the top 5 members.
 *  - Tally `frontmatter.tags` across members for a short tag summary.
 *
 * Accepts any graphology graph; callers typically pass an undirected
 * projection (Louvain requires undirected / can mutate weights otherwise).
 */
export declare function detectCommunities(graph: GraphInstance, resolution?: number): Community[];
/**
 * Score modularity of a community partition on an undirected graph.
 *
 * Louvain maximizes modularity — values close to 1 indicate strongly
 * separable communities; values near 0 (or negative) mean the partition
 * barely improves on random assignment. `detect_themes` warns callers
 * when this score drops below 0.3, which is the conventional "clusters
 * are meaningful" threshold in the network-science literature.
 *
 * Wraps graphology-communities-louvain's `.modularity` helper so callers
 * don't import louvain directly just for scoring.
 */
export declare function computeModularity(graph: GraphInstance, assignments: Record<string, number>): number;