A–Z index

Every entry and alias, alphabetically.

0

0.85: See The damping factor.

2

2DRank: See CheiRank and 2DRank.

3

301 redirect: See Redirects and canonicalisation.
302 redirect: See Redirects and canonicalisation.

A

A short history of PageRank: From a Stanford research project to Google's founding idea, a 0–10 number on millions of toolbars, a market in links, and finally a retired public metric that Google says it still uses internally.
Absolute spam mass: See Spam mass.
ACL algorithm: See Push algorithms.
Adjacency list: See Adjacency matrices and lists.
Adjacency matrices and lists: Two ways to write a graph down. The matrix suits the algebra behind every spectral ranking; compressed lists are how a real web graph fits in memory.
Adjacency matrix: See Adjacency matrices and lists.
Aged domain: See Expired domains.
Ahrefs DR: See Domain Rating (Ahrefs).
Alpha: See The damping factor.
Alpha centrality: See Katz centrality.
Anchor: See Anchor text.
Anchor text: The clickable words of a link. Search engines have used them since 1998 to learn what the target is about, which made them a prime target for manipulation and makes them the quickest way to read a domain's past.
ANF: See HyperBall.
Anti-TrustRank: See TrustRank.
Approximate counting: See HyperLogLog.
Approximate neighbourhood function: See HyperBall.
Approximate PageRank: See Push algorithms.
AS: See Authority Score (Semrush).
Assortative mixing: See Assortativity.
Assortativity: Whether well-connected nodes link to other well-connected nodes. Social networks tend to be assortative; the web, where hubs collect links from countless small sites, tends to be disassortative.
Attenuation factor: See Katz centrality.
Auditing vendor metrics: Checking Semrush and Majestic numbers from one day of GoDaddy's auction inventory against Common Crawl's open graph. A referring-domain floor made of the same few domains, a Majestic feed that is mostly empty, and a nearly constant Authority Score.
Authority score: See HITS (hubs and authorities).
Authority Score (Semrush): Semrush's compound 100-point score combining link power, estimated organic traffic and spam signals. Designed to resist manipulation, and in the 2026-09-23 auction data almost constant.
Average distance: See The neighbourhood function.
Axioms for centrality: Boldi and Vigna's three tests (size, density and score monotonicity) check whether a centrality measure agrees with intuition on graphs simple enough that the right answer is obvious. Only harmonic centrality passes all three.

B

Backlink: See Backlinks and referring domains.
Backlinks and referring domains: A backlink is a link from someone else's page to yours; referring domains count the distinct sites those links come from. Both are in-degree at different granularities, and both depend entirely on whose crawl counted them.
BackRub: See A short history of PageRank.
Backward push: See Push algorithms.
Barabási–Albert model: See Random graph models.
Bavelas closeness: See Closeness centrality.
Betweenness: See Betweenness centrality.
Betweenness algorithm: See Brandes' algorithm.
Betweenness centrality: A node is central if many shortest paths between other nodes pass through it. Betweenness finds bridges and brokers, not popular destinations.
BFS: See Paths, distance and reachability.
BFS order: See Node orderings.
Boldi–Vigna axioms: See Axioms for centrality.
Bonacich centrality: See Eigenvector centrality.
Bookmark coloring: See Push algorithms.
Bow tie: See The bow-tie structure.
Bowtie model: See The bow-tie structure.
Brandes: See Brandes' algorithm.
Brandes' algorithm: Exact betweenness centrality for every node in O(nm) time. One breadth-first search per source counts shortest paths, and one backward sweep hands each node its share.
Breadth-first search: See Paths, distance and reachability.
Brokerage: See Betweenness centrality.
BVGraph: See WebGraph compression.

C

Canonical tag: See Redirects and canonicalisation.
Cardinality estimation: See HyperLogLog.
CC web graph: See The Common Crawl web graph.
cc-main: See The Common Crawl web graph.
Centrality: A centrality measure assigns every node a score of importance computed from the graph's structure alone. Different measures formalise different ideas of importance, and they disagree.
Centrality measure: See Centrality.
CF: See Trust Flow and Citation Flow (Majestic).
CheiRank: See CheiRank and 2DRank.
CheiRank and 2DRank: PageRank computed on the graph with every link reversed. It scores nodes by how well they link out to the rest of the graph, and paired with ordinary PageRank it places every node on a two-dimensional map.
Citation count: See Degree centrality.
Closeness: See Closeness centrality.
Closeness centrality: A node is central if it is close, on average, to all other nodes. A classic idea from social network analysis that breaks down on graphs where most pairs cannot reach each other, like the web.
Clustering: See Clustering coefficient.
Clustering coefficient: How often two neighbours of a node are also neighbours of each other. Real networks form far more triangles than chance predicts, and unnaturally dense triangles are a classic fingerprint of link farms.
Comment spam: See Link spam.
Common Crawl: See The Common Crawl web graph.
Common Crawl ranks: See The Common Crawl web graph.
Communities: See Community detection.
Community detection: Finding groups of nodes that link to each other much more than to the rest of the graph. Modularity, Louvain, Leiden, label propagation and Infomap are the standard tools; on the web they find topics, regions and link farms.
Complete path method: See Monte Carlo PageRank.
Compressed sparse row: See Adjacency matrices and lists.
Concordant pairs: See Kendall's tau.
Condensation: See Strongly connected components.
Configuration model: See Random graph models.
Convergence: See Power iteration.
Copying model: See Random graph models.
Core number: See k-core decomposition.
Coreness: See k-core decomposition.
COST: See PageRank at scale.
CSR: See Adjacency matrices and lists.

D

DA: See Domain Authority (Moz).
Dangling mass: See Dangling nodes.
Dangling node: See Dangling nodes.
Dangling nodes: Nodes with no out-links, where a random surfer has nowhere to go. On a crawled web graph they are the majority, and how a PageRank implementation treats them changes the scores of everyone.
Dangling page: See Dangling nodes.
Degeneracy: See k-core decomposition.
Degree: In-degree counts the links a node receives and out-degree the links it makes; their distributions shape everything built on a link graph.
Degree assortativity: See Assortativity.
Degree centrality: Score each node by how many nodes link to it. The oldest, cheapest and most transparent centrality, and the one that "referring domains" reports.
Degree correlation: See Assortativity.
Degree distribution: See Degree distributions and power laws.
Degree distributions and power laws: Most sites receive almost no links and a few receive millions. That heavy tail explains why link metrics live on logarithmic scales and why averages mislead.
Density axiom: See Axioms for centrality.
Dependency accumulation: See Brandes' algorithm.
Disavow: See Link penalties and the disavow tool.
Discordant pairs: See Kendall's tau.
Distance: See Paths, distance and reachability.
Distance distribution: See The neighbourhood function.
Distinct counting: See HyperLogLog.
Distributed PageRank: See PageRank at scale.
dofollow: See Link attributes (nofollow, sponsored, ugc).
Domain appraisal: See Reading a domain's link metrics.
Domain auction: See Expired domains.
Domain Authority (Moz): Moz's 1–100 score that predicts how likely a domain is to rank in search results, computed by a machine-learning model over Moz's own link index. A comparative, logarithmic-feeling number, not a Google metric.
Domain Authority 2.0: See Domain Authority (Moz).
Domain evaluation: See Reading a domain's link metrics.
Domain graph: See Page, host and domain graphs.
Domain Rating (Ahrefs): Ahrefs' 0–100 score for the strength of a domain's backlink profile. By Ahrefs' own description it is a PageRank-like computation on a domain graph of followed links, mapped to a logarithmic scale.
Dominant eigenvector: See Eigenvector centrality.
DR: See Domain Rating (Ahrefs).
Dropped domain: See Expired domains.

E

Effective diameter: See The neighbourhood function.
Eigenvector: See Eigenvector centrality.
Eigenvector centrality: A node's score is proportional to the sum of the scores of the nodes linking to it. The cleanest form of "important nodes are those linked by important nodes", and the parent of PageRank.
End-point method: See Monte Carlo PageRank.
Erdős–Rényi model: See Random graph models.
Ergodic: See Random walks and Markov chains.
eTLD+1: See Reversed domain names and public suffixes.
Exact-match anchor: See Anchor text.
Expired domain: See Expired domains.
Expired domains: Domains whose owners let the registration lapse, sold at auction or re-registered by someone else. Their links often outlive their sites, which is why buyers value them and why search engines watch how they are reused.

F

Fingerprints: See Monte Carlo PageRank.
Forward push: See Push algorithms.
Freeman betweenness: See Betweenness centrality.

G

Gauss–Seidel: See PageRank as a linear system.
Geodesic: See Paths, distance and reachability.
Good core: See Spam mass.
Google bomb: See Anchor text.
Google PageRank: See PageRank.
Granularity: See Page, host and domain graphs.
Graph clustering: See Community detection.
Graph compression: See WebGraph compression.
Graph partitioning: See Community detection.
Graph reordering: See Node orderings.

H

Harmonic: See Harmonic centrality.
Harmonic centrality: Add up one over the distance from every other node. Nodes that the whole graph reaches in few steps score high, unreachable nodes simply count zero, and link farms gain almost nothing.
Harmonic closeness: See Harmonic centrality.
harmonicc: See Harmonic centrality.
hc: See Harmonic centrality.
Heavy tail: See Degree distributions and power laws.
HITS: See HITS (hubs and authorities).
HITS (hubs and authorities): Every node gets two scores. A good authority is linked by good hubs, and a good hub links to good authorities. Kleinberg's algorithm computes both from the links around a query's results.
HLL: See HyperLogLog.
Hop plot: See The neighbourhood function.
Host graph: See Page, host and domain graphs.
Hub score: See HITS (hubs and authorities).
Hubs and authorities: See HITS (hubs and authorities).
HyperANF: See HyperBall.
HyperBall: Grow a probabilistic counter around every node by one link per pass over the graph. After a few dozen passes you know, for every node, roughly how many nodes lie within each distance, and with it harmonic centrality, closeness and the distance distribution.
Hyperbolic weighting: See Weighted tau.
Hyperlink graph: See The web graph.
hyperlinkgraph: See The Common Crawl web graph.
HyperLogLog: A counter that estimates how many distinct items it has seen, within a few percent, using a few bytes. Its counters merge by taking maxima, which is what lets HyperBall count reachable nodes for a hundred million nodes at once.

I

IN component: See The bow-tie structure.
In-degree: See Degree.
In-degree centrality: See Degree centrality.
Inbound link: See Backlinks and referring domains.
Indegree: See Degree.
Infomap: See Community detection.
Inlink: See Backlinks and referring domains.
Intersection at depth: See Rank-biased overlap.
Inverse PageRank: See CheiRank and 2DRank.
Irreducible matrix: See The Perron–Frobenius theorem.

J

Jaccard at k: See Rank-biased overlap.
Jacobi iteration: See Power iteration.
Jacobi method: See PageRank as a linear system.

K

k-core: See k-core decomposition.
k-core decomposition: Repeatedly peel off every node with fewer than k links until none is left. The layer at which a node falls off, its core number, measures how deeply it is embedded in a dense part of the graph, in linear time.
k-shell: See k-core decomposition.
Katz: See Katz centrality.
Katz centrality: Count every walk that ends at a node, weighting a walk of length k by β to the power k. Short walks count most, everyone gets a baseline, and one parameter slides the measure from in-degree to eigenvector centrality.
Katz index: See Katz centrality.
Kendall rank correlation: See Kendall's tau.
Kendall tau: See Kendall's tau.
Kendall's tau: Compare two rankings by counting pairs of items they order the same way and pairs they order oppositely. The standard measure of agreement between centralities, computable in O(n log n) time.
Kleinberg: See HITS (hubs and authorities).

L

Label propagation: See Community detection.
Large-scale PageRank: See PageRank at scale.
Layered label propagation: See Node orderings.
Leiden: See Community detection.
Lin centrality: See Closeness centrality.
Lin's index: See Closeness centrality.
Linear system formulation: See PageRank as a linear system.
Link analysis: See Centrality.
Link attributes (nofollow, sponsored, ugc): The rel values that let a publisher say a link is not an endorsement. Introduced in 2005 against comment spam, extended in 2019, and since then treated by Google as hints rather than commands.
Link equity: See Expired domains.
Link equity transfer: See Redirects and canonicalisation.
Link exchange: See Reciprocity.
Link farm: See Link spam.
Link graph: See The web graph.
Link metrics guide: See Reading a domain's link metrics.
Link penalties and the disavow tool: How Google acts on manipulative links, from the 2012 Penguin update and manual actions to today's practice of silently ignoring spam links, and what the disavow tool does.
Link scheme: See Link spam.
Link spam: Links created to manipulate rankings rather than to help readers. Link farms, bought links, private blog networks and parasite pages, what each does to PageRank and harmonic centrality, and the traces they leave in a link graph.
Link spam update: See Link penalties and the disavow tool.
Link text: See Anchor text.
Linking root domains: See Backlinks and referring domains.
LLP: See Node orderings.
Local clustering coefficient: See Clustering coefficient.
Local push: See Push algorithms.
Locality: See Node orderings.
Log scale: See Ranks and scores.
Louvain: See Community detection.

M

Majestic: See Trust Flow and Citation Flow (Majestic).
Manual action: See Link penalties and the disavow tool.
Markov chain: See Random walks and Markov chains.
Mass estimation: See Spam mass.
Metric audit: See Auditing vendor metrics.
Milgram experiment: See Small worlds.
Modularity: See Community detection.
Monte Carlo PageRank: Estimate PageRank by actually running random surfers and counting their visits. A few walks per node rank the important nodes well, the walks parallelise trivially, and they can be updated as links change.
Moz DA: See Domain Authority (Moz).
Mutual links: See Reciprocity.

N

Node importance: See Centrality.
Node orderings: Renumbering a graph's nodes changes nothing about its structure and everything about its cost. Good orders shrink compressed graphs, speed up PageRank through cache locality and balance parallel work.
nofollow: See Link attributes (nofollow, sponsored, ugc).
Null model: See Random graph models.

O

OUT component: See The bow-tie structure.
Out-degree: See Degree.
Outdegree: See Degree.

P

PA: See Domain Authority (Moz).
Page Authority: See Domain Authority (Moz).
Page graph: See Page, host and domain graphs.
Page, host and domain graphs: The same links give three different graphs depending on whether a node is a URL, a host name or a registered domain. Each level answers different questions and hides different problems.
PageRank: A node is important if important nodes link to it. PageRank makes that circular idea precise as the long-run behaviour of a random surfer who sometimes gets bored and jumps.
PageRank as a linear system: PageRank is also the solution of a sparse system of linear equations. That view brings faster solvers such as Gauss–Seidel, explains why the problem is well conditioned, and makes PageRank linear in the teleport vector.
PageRank at scale: The arithmetic of ranking a hundred million nodes and billions of links. What must fit in memory, what each pass costs, why random reads dominate, and why one well-programmed machine often beats a cluster.
PageRank patent: See A short history of PageRank.
PageRank sculpting: See A short history of PageRank.
Paid links: See Link spam.
Parasite hosting: See Link spam.
Pareto distribution: See Degree distributions and power laws.
Paths, distance and reachability: How many clicks separate two sites, and whether one can reach the other at all. Breadth-first search answers both, and geometric centralities are built on the answer.
PBN: See Link spam.
Peeling: See k-core decomposition.
Penguin: See Link penalties and the disavow tool.
Percentile: See Ranks and scores.
Perron vector: See The Perron–Frobenius theorem.
Perron–Frobenius: See The Perron–Frobenius theorem.
Personalized PageRank: PageRank with the random surfer's jumps aimed at chosen nodes instead of anywhere. It measures importance from a point of view, and underlies TrustRank, topic-sensitive search and local graph clustering.
Popularity: See Degree centrality.
Position: See Ranks and scores.
Power iteration: Start from any distribution and apply one random-surfer step to all nodes at once, over and over. The error shrinks by a factor of about α per pass, and each pass reads every link exactly once.
Power law: See Degree distributions and power laws.
Power method: See Power iteration.
PPR: See Personalized PageRank.
PR: See PageRank.
Preference vector: See Personalized PageRank.
Preferential attachment: See Random graph models.
Prestige: See Degree centrality.
Primitive matrix: See The Perron–Frobenius theorem.
Private blog network: See Link spam.
Pseudo-PageRank: See PageRank as a linear system.
PSL: See Reversed domain names and public suffixes.
Public Suffix List: See Reversed domain names and public suffixes.
Push algorithms: Compute personalized PageRank by pushing probability outward from the seed, one node at a time, and stopping when the leftovers are small. The work depends on the precision you want, not on the size of the graph.

R

Random graph models: Recipes for generating graphs at random, from uniform random links to preferential attachment and copying. They explain where web structure comes from, serve as null models for measurements, and provide test graphs for algorithms.
Random surfer model: See The random surfer.
Random walk: See Random walks and Markov chains.
Random walk sampling: See Monte Carlo PageRank.
Random walk with restart: See Personalized PageRank.
Random walks and Markov chains: A walker who follows random links visits some nodes more often than others. The long-run visit frequencies, the stationary distribution, are the mathematical core of PageRank and its relatives.
Rank: See Ranks and scores.
Rank correlation: See Spearman's rho.
Rank-biased overlap: Compare two rankings by how much their top lists overlap at every depth, with geometrically decreasing weight on deeper levels. Works on truncated lists and on lists that do not contain the same items.
Ranks and scores: A centrality gives each node a value; a ranking gives it a position. Positions are easy to read and easy to misread. How values are distributed, how ties are broken and how precise the values are decide what a position means.
RBO: See Rank-biased overlap.
Reachability: See Paths, distance and reachability.
Reading a domain's link metrics: A practical guide for buyers. Which numbers to look at, what thresholds mean on real data, how to tell real link standing from noise, and two worked examples from the 2026-09-23 auction inventory.
Reasonable surfer: See The random surfer.
Reciprocal links: See Reciprocity.
Reciprocity: The share of links that are returned. In Common Crawl's 2026 domain graph about one link in eight has a link back, hundreds of thousands of times more than chance, from sites under common ownership, communities that cite each other and link exchanges.
Redirect: See Redirects and canonicalisation.
Redirects and canonicalisation: How 301s, 302s and canonical tags move link signals from one URL to another. They decide where a moved site's links end up, and they are the main mechanism by which an expired domain's links are put to new use.
Ref domains: See Backlinks and referring domains.
Reference compression: See WebGraph compression.
Referring domain: See Backlinks and referring domains.
Registrable domain: See Reversed domain names and public suffixes.
rel attribute: See Link attributes (nofollow, sponsored, ugc).
rel=canonical: See Redirects and canonicalisation.
rel=nofollow: See Link attributes (nofollow, sponsored, ugc).
Relative spam mass: See Spam mass.
Reverse PageRank: See CheiRank and 2DRank.
Reversed domain names and public suffixes: Why web graphs write google.com as com.google, and how the Public Suffix List decides where a registrable domain begins.
Reversed host names: See Reversed domain names and public suffixes.

S

SALSA: A random-walk version of HITS that alternates forward and backward steps. Its authority scores turn out to be in-degree, normalised within each group of co-cited nodes, which makes them immune to topic drift.
Scale-free: See Degree distributions and power laws.
SCC: See Strongly connected components.
Score monotonicity: See Axioms for centrality.
Seed set: See TrustRank.
Semi-external PageRank: See PageRank at scale.
Semrush AS: See Authority Score (Semrush).
Semrush Authority Score: See Authority Score (Semrush).
Semrush referring domains: See Auditing vendor metrics.
Shortest path: See Paths, distance and reachability.
Sink: See Dangling nodes.
Six degrees of separation: See Small worlds.
Size axiom: See Axioms for centrality.
Small worlds: Real networks combine dense local clusters with surprisingly short paths between any two nodes. That combination explains why "six degrees of separation" holds, why harmonic values crowd together and why links travel far.
Small-world network: See Small worlds.
Spam floor: See Auditing vendor metrics.
Spam mass: The share of a node's PageRank that does not come from known good nodes. A high-PageRank site whose rank comes mostly from outside the reputable web is a link-spam suspect.
SpamBrain: See Link penalties and the disavow tool.
Spearman correlation: See Spearman's rho.
Spearman rank correlation: See Spearman's rho.
Spearman's rho: The ordinary correlation coefficient computed on positions instead of values. Easy to compute and explain, robust to skewed scores, and like Kendall's tau blind to whether disagreements happen at the top or in the tail.
Spectral radius: See The Perron–Frobenius theorem.
spid: See The neighbourhood function.
sponsored: See Link attributes (nofollow, sponsored, ugc).
Stationary distribution: See Random walks and Markov chains.
Stochastic Approach for Link-Structure Analysis: See SALSA.
Strong components: See Strongly connected components.
Strongly connected components: Groups of nodes that can all reach each other along links. They decide where a random surfer gets trapped and which nodes a spectral ranking can see.
SURT: See Reversed domain names and public suffixes.

T

Tarjan's algorithm: See Strongly connected components.
TBPR: See A short history of PageRank.
Teleport: See The random surfer.
Teleportation: See The random surfer.
Teleportation probability: See The damping factor.
Tendrils: See The bow-tie structure.
TF: See Trust Flow and Citation Flow (Majestic).
TF/CF ratio: See Trust Flow and Citation Flow (Majestic).
The bow-tie structure: Seen from its largest strongly connected component, the web looks like a bow tie. Pages that reach the core sit on one side, pages the core reaches sit on the other, and stragglers hang off both.
The Common Crawl web graph: Common Crawl publishes free host- and domain-level link graphs every few months, with harmonic centrality and PageRank for every node. What the files contain, how they are built, and how to read them.
The damping factor: The probability α that PageRank's random surfer follows a link rather than jumping. It trades link structure against uniformity, sets the speed of convergence, and is conventionally 0.85 for no deep reason.
The neighbourhood function: For each distance t, how many pairs of nodes are within t links of each other. From it come the distance distribution, the average distance and the effective diameter, the robust ways to say how "small" a graph is.
The Perron–Frobenius theorem: The theorem that guarantees a nonnegative matrix has a dominant eigenvector with positive entries. It is why PageRank exists, why it is unique, and why eigenvector centrality fails on graphs that are not strongly connected.
The random surfer: The story behind PageRank. A surfer clicks random links, never presses Back, and now and then jumps to a random page; the share of time spent on each page is its PageRank.
The web graph: The directed graph with a node for every page, host or domain and an arc for every hyperlink between them.
Tie-breaking: See Ranks and scores.
Ties: See Ranks and scores.
Toolbar PageRank: See A short history of PageRank.
Top-k overlap: See Rank-biased overlap.
Topic-sensitive PageRank: See Personalized PageRank.
Topical Trust Flow: See Trust Flow and Citation Flow (Majestic).
Transition matrix: See Random walks and Markov chains.
Transitivity: See Clustering coefficient.
Triangles: See Clustering coefficient.
Trust Flow and Citation Flow (Majestic): Majestic's two 0–100 scores. Citation Flow measures link volume and power; Trust Flow measures closeness in the link graph to manually reviewed trusted sites, a TrustRank-style idea. Their ratio is a popular spam heuristic.
Trust propagation: See TrustRank.
TrustRank: Pick a small set of sites a human has checked, and let trust flow outward from them along links, fading with each step. Spam sites, which reputable sites rarely link to, end up with little trust however many links they have.
TTF: See Trust Flow and Citation Flow (Majestic).

U

ugc: See Link attributes (nofollow, sponsored, ugc).
Unnatural links: See Link penalties and the disavow tool.
UR: See Domain Rating (Ahrefs).
URL Rating: See Domain Rating (Ahrefs).

V

Vendor metric quality: See Auditing vendor metrics.
Vigna's tau: See Weighted tau.

W

Watts–Strogatz model: See Small worlds.
Webgraph: See The web graph.
WebGraph compression: How the WebGraph framework stores billions of links in a few bits each, using sorted gaps, copying from similar neighbours, runs of consecutive ids and codes tuned to power laws, while still decoding any node's links quickly.
WebGraph framework: See WebGraph compression.
Weighted Kendall tau: See Weighted tau.
Weighted tau: Kendall's tau with each pair weighted by how high its items rank, so disagreements at the top count more than disagreements in the tail. Designed by Sebastiano Vigna for comparing web-scale rankings with many ties.

Z

Zeta codes: See WebGraph compression.
Zipf's law: See Degree distributions and power laws.

Α

α: See The damping factor.

Ρ

ρ: See Spearman's rho.

Τ

τ: See Kendall's tau.
τ_w: See Weighted tau.