topic_feeder_pagerank: Topic Feeder PageRank (seeded reverse-graph PageRank)
In pagerankr: Modular Toolkit for PageRank Calculation

topic_feeder_pagerank

R Documentation

Topic Feeder PageRank (seeded reverse-graph PageRank)

Description

The reverse-graph sibling of [topic_sensitive_pagerank()]. Where Topic-Sensitive PageRank answers *"given I care about this cluster, which pages are most **authoritative** for it?"* (authority flows downstream **from** the seed cluster), 'topic_feeder_pagerank()' answers the inverse question: *"which pages **feed / power** this cluster?"* — i.e. the strongest internal hubs whose outlinks point **into** the target pages.

It is personalized PageRank with the teleport prior concentrated on the **target cluster**, run on the **transposed** link graph ('reverse = TRUE'). Mass teleports onto the cluster and then walks *backward* along links, so it accumulates on the pages that funnel authority toward the cluster. The further (in out-link hops) a page is from the cluster, the less feeder credit it earns — the PageRank damping factor is exactly that attenuation.

Like [trustrank()] and [topic_sensitive_pagerank()], this introduces **no new solver**: it builds a 'prior_df' from the seed set ([seed_prior()]) and hands it to [pagerank()] with 'reverse = TRUE'. The caller supplies the cluster; there is no topic inference. The prior builder is the *same* [seed_prior()] that [trustrank()] uses — a seed prior is orientation- agnostic; the reversed graph is what makes this a feeder query.

Usage

topic_feeder_pagerank(
  edge_list_df,
  seeds,
  seed_weight = NULL,
  seed_url_col = "url",
  seed_weight_col = "weight",
  ...
)

Arguments

`edge_list_df`	A data frame representing the edge list, typically with columns like "from" and "to". Edges are expected to be page-to-page hyperlinks: 'pagerank()' is graph-agnostic and treats every endpoint as a node, so resource links (images, CSS, JS, and other non-HTML references) must be filtered upstream or they collect authority as ordinary vertices. 'pagerank_screaming_frog()' does this at the crawl boundary via [sf_graph_eligible()] ('Hyperlink' only); a hand-built or non-SF edge list should apply the same hyperlink-only filter before scoring.
`seeds`	The target cluster. Either a character vector of cluster URLs (each gets equal seed weight unless 'seed_weight' is given), or a data frame with a URL column and a numeric weight column (see 'seed_url_col' / 'seed_weight_col') for unequal cluster emphasis. See [seed_prior()].
`seed_weight`	Optional numeric weight for a character-vector 'seeds': either one value per seed or a single value recycled to all seeds. Ignored when 'seeds' is a data frame. Default 'NULL' (every cluster page weight '1', a uniform distribution over the cluster).
`seed_url_col`, `seed_weight_col`	Column names used when 'seeds' is a data frame. Defaults '"url"' / '"weight"'. Ignored for a character vector.
`...`	Additional arguments forwarded to [pagerank()] (e.g. 'redirects_df', 'canonicals_df', 'rurl_params', 'weight_col', 'prior_transform', 'prior_alpha', 'damping'). Passing 'prior_df', 'prior_url_col', 'prior_weight_col', or 'reverse' is an error.

Details

## Why this is not recoverable from forward PageRank

In PageRank, authority flows **along** link direction: linking *to* an important page does not make the linker important. So "the pages that feed cluster X" is **not** a re-reading of forward (or Topic-Sensitive) PageRank scores — it is the reversed-graph notion. Forward PageRank and [topic_sensitive_pagerank()] rank pages by **inflow** (important because important pages point at them); 'topic_feeder_pagerank()' ranks by **outflow toward the cluster** (important because *it* points at the cluster).

## How to read the result

The seed (cluster) pages carry the teleport mass directly, so they appear in 'prior_weight' with a positive value and tend to score highly *by construction* — that is teleport, not a feeder signal. **The feeders are the high-'pagerank' pages whose 'prior_weight' is '0'** (pages outside the cluster that nonetheless accumulate reverse-walk mass). Rank by 'pagerank' and read off the top non-seed rows, or filter 'prior_weight == 0'.

## Relationship to neighboring tools

[pagerank()] (forward): Global inflow authority. Feeder PageRank is its transpose, biased to a cluster.
[topic_sensitive_pagerank()] (G2): The forward-graph sibling: same personalization plumbing, opposite flow direction. G2 finds a cluster's *authorities*; this finds its *feeders*. They are complementary, not substitutes.
Inverse PageRank ('pagerank(reverse = TRUE)'): The **global**, unseeded outflow centrality — "which pages funnel authority outward anywhere on the site". Feeder PageRank adds the cluster bias: not "good hub in general" but "good hub *for this cluster*". With no 'seeds' you would just call 'pagerank(reverse = TRUE)' directly.
HITS hubs ([hits()]): Also an outflow notion, but a co-computed eigenvector pair (hub <-> authority) with no teleport prior and no damped-surfer / dangling handling. Feeder PageRank is the random-surfer-model, cluster-seedable counterpart that stays inside the [pagerank()] graph-preparation contract (redirects, canonicals, duplicate-edge policy, weights).

Seed weights are an **additive feeder budget**: if two seed URLs fold onto the same vertex (redirect / canonical variants) their weights sum, exactly as the [pagerank()] / [align_prior_to_vertices()] prior contract specifies.

Everything [pagerank()] accepts flows through '...': redirects, canonicals, URL cleaning, domain/host filtering, edge weights, duplicate-edge policy, and the prior-shaping knobs ('prior_transform', 'prior_alpha'). Because this owns both the prior and the graph orientation, passing 'prior_df', 'prior_url_col', 'prior_weight_col', or 'reverse' is an error. Note that the direction-sensitive forward-flow devices that [pagerank()] already rejects under 'reverse = TRUE' ('nofollow_action = "evaporate"', 'indexability_df') are likewise unavailable here.

Value

The [pagerank()] result data frame ('node_name', 'pagerank', and the 'prior_weight' column the prior path adds), sorted by 'pagerank' descending, carrying the usual ‘"transition_audit"' attribute. The audit’s model configuration records 'reverse = TRUE'.

Examples

edges <- data.frame(
  from = c("/hub", "/hub", "/feeder", "/blog", "/ai", "/news"),
  to = c("/ai", "/ai-demo", "/ai", "/ai", "/ai-demo", "/sports")
)

# Which pages feed the AI-Agent cluster?
fr <- topic_feeder_pagerank(
  edges,
  seeds = c("/ai", "/ai-demo"),
  clean_edge_urls = FALSE
)

# Feeders are the top-scoring rows OUTSIDE the cluster (prior_weight == 0).
fr[fr$prior_weight == 0, c("node_name", "pagerank")]

# Build the cluster prior explicitly and run it yourself, if you prefer:
prior <- seed_prior(c("/ai", "/ai-demo"))
identical_run <- pagerank(
  edges, prior_df = prior, reverse = TRUE, clean_edge_urls = FALSE
)

pagerankr documentation built on Oct. 1, 2026, 5:09 p.m.