links

HITS (Hyperlink-Induced Topic Search) defines a mutually reinforcing, iterative computation over directed hyperlink graphs that separates web pages into two distinct authority roles: hubs (pages linking to many quality resources) and authorities (pages linked to by many quality hubs), solving the problem of identifying high-quality topical resources from link structure alone without relying on content analysis. The core mechanism executes repeated matrix-vector multiplications on the adjacency matrix of a query-specific subgraph (the "base set" expanded via neighborhood sampling), converging via principal eigenvector extraction to produce hub and authority weight scores that amplify pages receiving links from well-connected hub pages. This eigenvector-based, query-dependent link analysis directly informs search ranking by demonstrating that in-link count alone is insufficient - link source quality propagates authority transitively, establishing the theoretical foundation for trust-weighted, graph-theoretic ranking signals that later shaped PageRank's global, query-independent implementation and modern link equity models in crawl prioritisation and index scoring.

TrustRank is a semi-automatic spam-fighting framework that propagates trust scores from a small, manually curated seed set of high-quality pages through the hyperlink graph to assign legitimacy scores to all crawled documents. The system exploits the observation that good pages rarely link to spam, enabling trust to decay with link distance from seeds while isolating link-spam clusters that accumulate inbound links without receiving trust propagation. Search engines applying TrustRank can suppress or demote low-trust pages during ranking, reduce crawler resources wasted on spam-dense host neighbourhoods, and prioritise indexing of nodes with non-trivial trust scores - effectively making large-scale link manipulation economically unviable without proximity to authoritative seed pages.