Citation Graph Rank

The foundational mechanism mirrors PageRank: a source's authority is defined recursively. A citation from a high-authority source carries more weight than one from a low-authority source. This creates a virtuous cycle where trusted nodes confer trust upon the sources they cite.

• Weighted edges: Not all citations are equal; links from Tier 1 sources have greater mass.
• Damping factor: A probability (typically 0.85) that the authority flow continues, preventing rank sinks.
• Convergence: The algorithm iterates until authority scores stabilize across the entire graph.

Unlike simple citation counts, graph-based rank resists manipulation. Buying or generating thousands of low-quality inbound links from link farms has minimal impact because those sources themselves lack authority. The algorithm naturally penalizes disconnected clusters of spam.

• Spam mass detection: Identifies nodes whose rank derives disproportionately from low-trust subgraphs.
• TrustRank seeding: A set of manually vetted, high-authority seed nodes can be used to initialize the graph, making manipulation exponentially harder.
• Island penalty: Sources that only cite each other in a closed loop receive diminished rank.

Static graph rank is insufficient for AI citation integrity. Modern implementations incorporate temporal decay functions that model the half-life of authority. A citation from a source that was authoritative a decade ago but is now obsolete should carry less weight.

• Edge age weighting: Recent citations contribute more to current authority than older ones.
• Node activity decay: Sources that stop publishing gradually lose rank, preventing dead-source authority persistence.
• Recency-aware PageRank: Variants like TimedPageRank distribute authority proportional to the freshness of the linking relationship.

A naive citation graph treats all links equally. A sophisticated Citation Graph Rank system constructs heterogeneous graphs with typed nodes and edges to capture nuanced authority signals.

• Node types: Author, Institution, Domain, Publication Venue, Individual Document.
• Edge types: authored_by, published_in, cites, retracts, supports, refutes.
• Meta-path analysis: Authority can flow along defined paths, such as Author → Document → Cited Document → Author, enabling co-citation authority and bibliographic coupling to influence rank.

Global authority is insufficient for verifying specific claims. Personalized PageRank variants compute authority relative to a topic vector or a set of trusted seed sources. A source authoritative on quantum physics may have low rank for medical claims.

• Topic-sensitive rank: Pre-computes authority distributions for different knowledge domains.
• Query-time biasing: Adjusts rank based on the semantic similarity between the source and the claim being verified.
• Seed-set personalization: An enterprise can define its own set of trusted foundational sources, creating a bespoke authority graph aligned with organizational truth.

Citation Graph Rank is not a standalone metric. It serves as the network layer within a composite trust scoring architecture. The graph-derived authority is combined with content-based signals to produce a final Source Credibility Score.

• Signal fusion: Graph rank is multiplied by Factual Entailment Ratio and Peer-Review Validation Flag.
• Retraction propagation: When a node is added to the Retracted Source Blacklist, its outgoing authority is zeroed, and its inbound links are devalued.
• Predatory journal demotion: Sources from journals flagged by a Predatory Journal Filter have their node weight artificially suppressed before rank computation.

A dynamic, interconnected model representing entities (authors, institutions, domains) and their trust relationships. Unlike PageRank-style link analysis alone, this graph propagates authority scores across a heterogeneous network where nodes represent different entity types and edges represent verified relationships such as authorship, institutional affiliation, and citation.

• Captures transitive trust: a researcher's authority boosts their institution's score
• Enables multi-hop reasoning about source credibility
• Updates dynamically as new citations and relationships emerge

A method for assessing the relationship between two sources by measuring how often they are cited together by subsequent works. High co-citation frequency indicates a shared thematic or evidentiary context, strengthening the credibility of both sources.

• Used to identify invisible colleges of research
• Surfaces corroborating evidence clusters
• Helps detect citation cartels when co-citation patterns appear artificially inflated

Measures similarity between two sources based on the number of shared references they cite. Unlike co-citation (which looks forward), bibliographic coupling looks backward at overlapping reference lists.

• Strong coupling suggests shared intellectual foundations
• Useful for identifying corroborating evidence from independent authors
• Coupling strength thresholds can trigger automated verification checks

A recursive verification method that traces a citation back through its own references to the original primary source. This validates the evidence chain and detects misrepresentation or citation distortion.

• Each hop in the chain is scored for semantic fidelity
• Detects when secondary sources mischaracterize primary findings
• Broken chains or circular references trigger integrity flags

A hierarchical categorization system that ranks sources into tiers based on editorial rigor and authority:

• Tier 1: Peer-reviewed primary research, official government datasets
• Tier 2: Established journalism, institutional white papers
• Tier 3: Personal blogs, social media, self-published content
• Tier 4: Known disinformation or retracted sources

Citation Graph Rank incorporates tier weights as a prior probability before link-based authority propagation.

A composite measure of the completeness and logical validity of the path from an AI's output claim back through its citations to foundational, verifiable data. High integrity requires:

• Every factual claim linked to at least one source
• No broken or circular citation paths
• Semantic consistency at each link in the chain
• Primary source reachable within a reasonable hop distance

Low integrity scores trigger hallucination risk alerts.