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The web was built on simple hyperlinks, but as the internet has grown into a dense network of sites, APIs, and distributed services, the naïve link no longer meets the needs of users or machines. nthlink proposes a way to elevate linking by explicitly modeling and utilizing nth-degree relationships in the link graph. Instead of treating links as merely direct pointers, nthlink captures context, provenance, and multi-hop relationships, enabling smarter discovery, navigation, and routing. At its core, nthlink treats the web as a layered graph where nodes represent resources and edges carry rich metadata: author, trust score, type (article, dataset, API endpoint), and semantic tags. The "nth" in nthlink denotes the ability to reason about links multiple hops away. For example, a 1st-degree (1st link) is a direct hyperlink; a 2nd-degree nthlink considers resources reachable via one intermediary; higher-degree nthlinks make multi-hop relationships computable and queryable. By materializing these relationships, systems can answer questions like "Which resources two steps away are most relevant to X?" or "Which intermediary paths provide the highest trust between A and B?" Practical applications of nthlink are diverse. Search engines can use nthlink signals to surface related content that doesn't contain direct backlinks but sits within a trusted neighborhood. Content recommendation systems can identify valuable context by following curated multi-hop paths rather than single links. In federated and peer-to-peer systems, nthlink-aware routing can choose resilient paths around outages by preferring multi-hop routes with proven availability. SEO professionals may adopt nthlink metrics to evaluate the indirect visibility of pages within topical clusters. Implementing nthlink requires a mix of lightweight graph indexing, standardized metadata, and privacy-aware crawling or collaboration. A practical design might rely on localized graph snapshots (site- or network-level) and APIs for querying nth-degree relationships with configurable depth and cost. Metadata standards—covering provenance, freshness, and relevancy signals—will make edges meaningful. Because tracking multi-hop relationships can be expensive and privacy-sensitive, nthlink designs emphasize client-side computation, selective federation, and rate-limited exchange of aggregate signals. Challenges remain: scale is the biggest. The number of possible paths explodes with graph size, so efficient pruning, heuristics, and relevance scoring are essential. There are also trust and manipulation risks; malicious actors could try to game intermediate nodes to inflate indirect visibility. Finally, privacy and data ownership must be respected when exchanging linking metadata across domains. nthlink is not a single technology but a mindset—treating the link graph as a first-class, traversable data layer that can be queried beyond one-hop relationships. By combining richer metadata, careful indexing, and relevance-aware heuristics, nthlink can make navigation and discovery more robust and context-aware—an evolution of linking suited to the interconnected, distributed web of today.