> ## Documentation Index > Fetch the complete documentation index at: https://docs.narrative.io/llms.txt > Use this file to discover all available pages before exploring further. # Graph Enrichment > Strengthening identity graph structure with third-party linkage data for improved person-level and household-level resolution Graph enrichment uses third-party identity data to create or strengthen connections between nodes in your identity graph. Unlike [addressability expansion](/concepts/identifiers/addressability), which appends identifiers to existing records, graph enrichment changes the graph's structure itself—adding edges that connect previously isolated nodes into unified profiles. ## What graph enrichment is Your first-party identity graph reflects the linkages you can directly observe: login events, CRM matches, transaction data. But your observation is limited to your own touchpoints. Graph enrichment fills the gaps by incorporating linkages observed by third-party providers across their own data ecosystems. The result is a more connected graph with fewer fragmented profiles and better person-level or household-level resolution. ## How it works Consider a retail company with this first-party data: * Customer A: hashed email + iPhone IDFA (observed via app login) * Customer A: hashed email + home address (observed via shipping) Their first-party graph has a connected component with three nodes: email, IDFA, and address. A third-party identity provider observes that the same email is also linked to an Android GAID and a second hashed email (a work address). After graph enrichment: * Customer A's component now includes five nodes: personal email, work email, IDFA, GAID, and home address * The retailer can now reach Customer A on Android devices and recognize them when they use their work email The graph's structure changed. New edges were added, and the connected component expanded. ## Effects on the graph Graph enrichment can improve your identity graph in several ways: **Person-level resolution.** Connecting identifiers that belong to the same individual across devices and channels. A customer who appears as three separate profiles (email-only, MAID-only, cookie-only) becomes one resolved profile. **Household-level resolution.** Linking identifiers across individuals within a household. Shared addresses, shared IP ranges, and shared device usage can connect household members when that level of resolution is appropriate for your use case. **Waterfall matching flexibility.** A richer graph gives you more identifiers per person, which means more opportunities to match against any given activation platform. If one identifier type fails to match, another may succeed. **Longitudinal consistency.** When identifiers change over time (cookies expire, MAIDs reset), a well-enriched graph maintains continuity through other persistent linkages. ## What to look for in providers When evaluating graph enrichment providers, focus on these criteria: | Criterion | What to measure | Why it matters | | --------------------------------- | ------------------------------------------------------ | -------------------------------------------------------- | | **Linkage depth** | Average edges per node | More edges per node means richer resolution | | **Determinism rate** | Percentage of deterministic vs. probabilistic linkages | Deterministic linkages are more reliable | | **Confidence score quality** | Distribution and calibration of confidence scores | Well-calibrated scores enable meaningful thresholds | | **Update cadence** | How frequently linkages are refreshed | Stale linkages degrade graph quality over time | | **Incremental component overlap** | How many of your existing components gain new edges | Measures actual incremental value vs. redundant coverage | ## Risks Graph enrichment carries higher stakes than addressability expansion. A bad identifier append reduces match rates—an inconvenience. A bad linkage merges two distinct people into a single profile—a structural corruption that propagates through every downstream use case. Protect against this by: * **Setting confidence thresholds.** Only incorporate linkages above a minimum confidence score. Start conservative and relax over time as you validate quality. * **Monitoring component size distribution.** A sudden increase in very large components may indicate over-linking. Healthy graphs follow a predictable size distribution. * **Evaluating incrementally.** Test a provider's linkages against a subset of your graph before full integration. Measure resolution improvement against a known truth set. *** ## Graph enrichment in Narrative Narrative's platform supports graph enrichment evaluation and execution through Rosetta Stone and NQL. ### Evaluating provider linkage coverage Use NQL to measure how much incremental coverage a provider adds to your existing graph: ```sql theme={null} SELECT provider."_rosetta_stone"."unique_identifier"."type" AS id_type, COUNT(DISTINCT provider."_rosetta_stone"."unique_identifier"."value") AS provider_ids, COUNT(DISTINCT CASE WHEN seed."_rosetta_stone"."unique_identifier"."value" IS NOT NULL THEN provider."_rosetta_stone"."unique_identifier"."value" END) AS overlapping_ids FROM external_provider."identity_linkages" provider LEFT JOIN company_data."first_party_graph" seed ON provider."_rosetta_stone"."unique_identifier"."value" = seed."_rosetta_stone"."unique_identifier"."value" AND provider."_rosetta_stone"."unique_identifier"."type" = seed."_rosetta_stone"."unique_identifier"."type" GROUP BY provider."_rosetta_stone"."unique_identifier"."type" ``` ### Measuring resolution improvement Compare connected component metrics before and after incorporating a provider's linkages to quantify the resolution improvement they deliver. *** ## Related content The foundational concepts behind identity graph structure The complementary approach: improving activation reach without changing graph structure How individual identifier linkages work Translating offline data for digital activation