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pagerankr: SEO-Focused PageRank Modeling Toolkit

1 pagerankr

1 pagerankr

pagerankr is an SEO-focused R toolkit for PageRank modeling on crawl data. It supports both a single end-to-end wrapper (pagerank()) and modular building blocks for cleaning URLs, auditing redirects, resolving link graphs, running scenario comparisons, and exporting graph outputs.

The package currently includes:

End-to-end and low-level PageRank calculation
Redirect diagnostics and resolution policies
Nofollow and indexability-aware PageRank modeling
Domain/host filtering and edge-weight transformations
Screaming Frog Internal: All plus All Inlinks/Outlinks import adapters
Model comparison, parameter sweeps, and what-if simulations
Graph export utilities and an optional interactive Shiny explorer
HITS hub and authority scores (hits())
SALSA hub and authority scores (salsa())
TrustRank seed-biased PageRank (trustrank())
Topic-Sensitive PageRank (per-topic authority with blended scores)
Reverse-graph feeder PageRank (find pages that power a cluster)
GA4 behavioral transition modeling (transition counts, structural smoothing, entrance teleport)
PageRank convergence controls (algo/eps/niter) and alpha-stability reporting (pagerank_stability())

1.1 Installation

# install.packages("devtools")
devtools::install_gitlab("bart-turczynski/pagerankr")

1.2 Quick Start

library(pagerankr)

edges <- data.frame(
  from = c("http://example.com/home",
           "http://example.com/about",
           "http://example.com/blog"),
  to   = c("http://example.com/about",
           "http://example.com/home",
           "http://example.com/home")
)

redirects <- data.frame(
  from = "http://example.com/old-blog",
  to   = "http://example.com/blog"
)

pr <- pagerank(edges, redirects_df = redirects)
print(pr)

1.3 Current Capabilities

1.3.1 Crawl Data Prep and Redirect QA

clean_url_columns() canonicalizes URL columns using rurl::get_clean_url
audit_redirects() reports redirect chains, loops, conflicts, self-refs, and optional orphaned rules vs. an edge list
resolve_redirects() applies redirect maps to an edge list with conflict and loop policies
resolve_redirect_urls() resolves a character vector of URLs without requiring an edge list
resolve_links() returns the resolved/deduplicated graph without computing PR
get_unique_edges() and drop_isolates() provide explicit graph hygiene tools

audit <- audit_redirects(redirects, edge_list_df = edges)
print(audit)

resolve_redirect_urls(
  c("http://example.com/old-blog", "http://example.com/home"),
  redirects
)

1.3.2 Screaming Frog Crawl Imports

Use screaming_frog_bundle() with an Internal: All export and either All Inlinks or All Outlinks. The node export supplies page facts, redirects, canonicals, and indexability. The link export supplies raw link observations and graph-eligible Hyperlink edges. Resource, canonical, hreflang, and other non-Hyperlink link rows are retained in diagnostics but excluded from the PageRank graph by default.

bundle <- screaming_frog_bundle(
  internal = "internal_all.csv",
  links = "all_outlinks.csv",
  link_export_kind = "all_outlinks"
)

pr <- pagerank_screaming_frog(bundle)
attr(pr, "screaming_frog_import")
attr(pr, "transition_audit")

Placement and rendered-vs-HTML policies are explicit scoring choices:

pagerank_screaming_frog(
  bundle,
  accepted_placements = c("nav", "content"),
  link_origins = c("html", "html_rendered"),
  placement_weights = c(nav = 2, content = 1)
)

1.3.3 PageRank Modeling Controls

pagerank() supports weighted edges via weight_col
duplicate_edge_policy = "collapse" keeps the standard binary destination-level surfer as the default: repeated from -> to rows become one edge. Opt into "aggregate" to sum duplicate numeric weights, or "count_instances" for a link-slot surfer where repeated links to the same target increase transition probability.
nofollow_col + nofollow_action = c("evaporate", "drop", "keep")
indexability_df support for noindex and Blocked by robots.txt behaviors (robots_blocked_action = "show" or "vanish")
Domain scoping directly in pagerank() (keep_domains, exclude_domains) or via filter_links_by_domain() with domain/host keep/ignore rules
transform_weights() provides rank/log/zipf/percentile transforms for raw edge signals

edges_w <- data.frame(
  from = c("Home", "Home", "Home"),
  to = c("About", "Blog", "Contact"),
  position = c(1, 2, 5)
)

edges_w$weight <- transform_weights(
  edges_w$position,
  method = "zipf",
  descending = FALSE
)

pagerank(edges_w, weight_col = "weight", clean_edge_urls = FALSE)

1.3.4 Comparison, Grid Search, and Simulation

compare_pagerank() calculates deltas, rank shifts, and summary stats
auto_grid() and pagerank_grid() run parameter sweeps
analyze_pagerank_grid() summarizes concentration/distribution effects
simulate_changes() compares baseline vs proposed links/redirects
pr_gini(), pr_entropy(), and pr_top_k_share() compute distribution metrics

grid <- auto_grid(
  damping = c(0.85, 0.95),
  nofollow_action = c("evaporate", "drop")
)

grid_results <- pagerank_grid(edges, params_grid = grid, clean_edge_urls = FALSE)
analyze_pagerank_grid(grid_results)

1.3.5 Export and Exploration

export_graph() writes outputs in graphml, dot, edgelist, or pajek formats
launch_pagerank_explorer() launches an interactive Shiny app for uploads, visualization, redirect auditing, and exports

pr <- pagerank(edges, clean_edge_urls = FALSE)
export_graph(pr, edges, file = "pagerank.graphml", format = "graphml")

# Optional interactive app:
# install.packages(c("shiny", "DT", "visNetwork"))
# launch_pagerank_explorer()

1.4 Key Functions

Function	Purpose
`pagerank()`	End-to-end PageRank pipeline
`compute_pagerank()`	Low-level wrapper around `igraph::page_rank()`
`resolve_links()`	Resolve redirects and deduplicate graph without PR
`resolve_redirects()`	Apply redirect rules to an edge list
`resolve_redirect_urls()`	Resolve standalone URL vectors through redirects
`resolve_canonicals()`	Apply rel=canonical folds to edge endpoints
`resolve_folded_urls()`	Resolve URL vectors through redirects plus canonicals
`audit_redirects()`	Diagnose redirect chains, loops, and conflicts
`screaming_frog_bundle()`	Compose Screaming Frog node and link exports
`pagerank_screaming_frog()`	Score a Screaming Frog bundle via `pagerank()`
`clean_url_columns()`	Canonicalize URL columns in data frames
`get_unique_edges()`	Deduplicate edges and handle self-loops
`drop_isolates()`	Build vertex sets with or without isolates
`filter_links_by_domain()`	Filter edges by keep/ignore domain or host lists
`transform_weights()`	Transform raw signals into PageRank edge weights
`compare_pagerank()`	Compare two PageRank outputs with rank deltas
`simulate_changes()`	Evaluate proposed link/redirect changes
`auto_grid()`	Build exhaustive parameter grids
`pagerank_grid()`	Run PageRank across multiple parameter sets
`analyze_pagerank_grid()`	Summarise PageRank distribution by model
`pr_gini()`	Gini concentration metric
`pr_entropy()`	Entropy dispersion metric
`pr_top_k_share()`	Top-k PageRank concentration share
`export_graph()`	Export graph and PageRank metadata for external tools
`launch_pagerank_explorer()`	Start the interactive Shiny explorer
`hits()`	End-to-end HITS hub + authority scores
`compute_hits()`	Low-level igraph HITS wrapper
`salsa()`	End-to-end SALSA hub + authority scores
`compute_salsa()`	Low-level SALSA computational core
`trustrank()`	TrustRank: seed-biased PageRank from a trusted seed set
`topic_sensitive_pagerank()`	Per-topic personalized PageRank with blended scores
`topic_feeder_pagerank()`	Reverse-graph seeded PR: find pages that feed a cluster
`seed_prior()`	Build a teleport prior from a seed set (for trustrank / topic_feeder_pagerank)
`align_prior_to_vertices()`	Align a prior/teleport data frame to the graph vertex set
`damping_sensitivity()`	Sweep PageRank across a range of damping factors
`pagerank_stability()`	Alpha-stability report: rank correlation across a damping grid
`ga4_page_transitions()`	Consecutive page-view transition counts from a GA4 export
`smooth_transitions()`	Shrink sparse empirical transitions toward a structural prior
`ga4_entrance_teleport()`	Entrance/landing-page counts as a PageRank teleport vector
`aggregate_edges()`	Aggregate duplicate edges after URL folding
`transform_edge_weights()`	Per-source grouped edge weight transforms
`validate_edge_weights()`	Validate per-source weight totals
`screaming_frog_internal()`	Import Screaming Frog Internal: All export
`screaming_frog_links()`	Import Screaming Frog All Inlinks / All Outlinks export
`audit_redirects()`	Diagnose redirect chains, loops, and conflicts
`audit_canonicals()`	Diagnose rel=canonical fold coverage and conflicts
`resolve_canonicals()`	Apply rel=canonical folds to an edge list
`resolve_canonical_urls()`	Resolve a URL vector through rel=canonical folds
`resolve_folded_urls()`	Resolve a URL vector through redirects plus canonicals

1.5 Further Information

The reference and vignettes ship with the package and are reachable through help(package = "pagerankr") and the vignette() calls below; the rendered website is being rebuilt after the move to GitLab. To report a bug or request an enhancement, use GitLab Issues. Please read CONTRIBUTING.md before proposing a change; it sets out the test, lint, and R CMD check requirements. For privately reported security vulnerabilities, follow SECURITY.md.

help(package = "pagerankr")
vignette("pagerankr-usage")
vignette("trustrank")
vignette("topic_feeder_pagerank")

1.6 Code of Conduct

Please note that the pagerankr project is released with a Contributor Code of Conduct. By contributing to this project, you agree to abide by its terms.

1.7 License

MIT License. See the LICENSE file for details.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.