Algorithms & Models

HITS Algorithm — How It Ranks Hubs and Authorities

HITS scores every page twice — as an authority worth linking to and as a hub worth reading — and lets the two scores reinforce each other until they stabilize.

Reviewed 2026 Updated only when the core methodology changes

What the HITS Algorithm Is

HITS (Hyperlink-Induced Topic Search) is a link-analysis ranking algorithm developed by Jon Kleinberg in the late 1990s. Where PageRank assigns each page a single importance score, HITS assigns two complementary scores to every page: authority (is this page a definitive source?) and hubness (does this page point to many good sources?).

HITS was developed at IBM and influenced early-2000s search engines, most notably Teoma and later Ask.com.

What HITS Ranks

HITS ranks web pages within a query-specific subgraph. This is its sharpest difference from PageRank: HITS is query-dependent — it computes rankings over the pages relevant to a particular search topic, not one global ranking of the web.

Core Inputs Used by HITS

  • The link structure of a topic-focused subgraph — typically the top results for a query plus the pages they link to and that link to them
  • Two scores per page — authority and hub, mutually defined

Like PageRank, HITS does not analyze page content; ranking emerges from links alone.

How HITS Scores Are Calculated (High-Level)

The algorithm runs a mutual reinforcement loop:

  1. Every page starts with equal authority and hub scores.
  2. Authority update — a page’s authority becomes the sum of the hub scores of pages linking to it.
  3. Hub update — a page’s hub score becomes the sum of the authority scores of pages it links to.
  4. Repeat until scores converge. The loop is mathematically equivalent to computing dominant eigenvectors of matrices derived from the link graph.

The intuition: good authorities are pointed to by good hubs, and good hubs point to good authorities — a directory page and a definitive source are valuable in different ways.

Conceptual model: PageRank asks “who do important pages endorse?”; HITS asks two questions — “who curates this topic?” and “who do the curators trust?”

Update Frequency

HITS is computed per query, at search time (or precomputed per topic cluster), not as a standing global index — a key practical difference from PageRank.

Known Limitations and Criticisms

  • Topic drift — the expanded subgraph can be captured by a densely linked off-topic community, dragging results away from the query
  • Computational cost — per-query iteration is expensive at search-engine scale
  • Spam vulnerability — mutually linking farms can inflate hub and authority scores
  • Query dependence as a cost — no reusable global ranking; every new query recomputes

Where HITS Is Used

HITS-style analysis appears in:

  • Historical web search (Teoma/Ask.com)
  • Social network analysis — finding brokers (hubs) and leaders (authorities)
  • Citation and recommendation network research
  • Teaching, as the canonical counterpart to PageRank in link-analysis courses

Summary

HITS introduced the idea that importance has two roles — curators and sources — and that ranking can be tailored per topic rather than computed once for the whole web. PageRank’s global simplicity won the search-engine era, but the hub/authority insight persists wherever networks are analyzed for influence.

References and Sources

  • Kleinberg, J. M. Authoritative Sources in a Hyperlinked Environment (1999).
  • Wikipedia. HITS algorithm.
  • Langville, A. N., & Meyer, C. D. Google’s PageRank and Beyond.