How the Topical Map Generator and Editor work — in plain English

A topical map is a structured blueprint of every content entity your site needs to establish authority on a subject. It is not a keyword list or a sitemap. It is a graph — a set of nodes (pages, sections, FAQs) and edges (links and relationships) that defines:

A concept map is an entity relationship graph that shows how the key concepts, entities, conditions, processes, and attributes related to a seed topic connect to each other. Where a topical map answers "what pages do I need to build?", a concept map answers "what does Google need to understand about this subject?"

Every entity in a topical map is one of six node types. Each type has a fixed position in the hierarchy, its own color in the editor, and a default URL role.

Concept map nodes are classified into seven types based on the role the entity plays in the subject domain. Node size scales with label length so all text fits inside the circle. Color is consistent with the type regardless of which layout is active.

Concept edge relationship types

Every edge in a concept map carries one of eleven relationship types. Each type has its own color so relationships are visually distinguishable at a glance.

Every topical node is classified with a URL role that determines whether it gets its own page in the site architecture.

Edges are the relationships between nodes. Every edge carries metadata that drives the internal linking plan in topical mode and semantic relationships in concept mode.

Every topical edge also carries: recommended anchor text, alternate anchor phrases, placement hint, weight (0–1), and required vs optional status. Concept edges carry a relationship label and relationship type rendered visually on the connecting line.

The pipeline begins by normalizing the macro and seed inputs into a consistent internal format before any topic discovery begins.

The core discovery stage. The pipeline expands the seed into a broad set of candidate topics using multiple data sources.

The raw candidate list from Stage 2 contains duplicates, near-duplicates, and wording variants. Canonicalization resolves them into a clean, non-redundant topic set.

Each canonical topic is classified into one or more content roles.

Each classified node is placed in the hierarchy and assigned a tier. Canonical URLs are then generated for all Standalone nodes.

The internal linking stage creates all edges in the graph. Every edge represents a real internal link that should exist on the published site.

All nodes and edges are assembled into the final graph output. The structure is graph-ready — directly importable into Neo4j, D3.js, or any graph database.

The editor operates in two modes. The active mode is detected automatically from the MapType field when a JSON file is loaded. You can switch mode at any time using the Mode dropdown in the toolbar. Switching mode does not clear the graph — it changes how nodes and edges are rendered and which fields appear in the Properties panel.

The editor renders the node-edge graph in four layouts. All four show the same data from a different perspective. Press F at any time to fit the full map in the viewport.

Node style options

Every node carries a full data profile. All fields are editable through the Node Properties panel. The editor shows topical or concept fields depending on the active mode and node type.

Shared fields — both modes

Topical-only fields

Concept-only fields

View options

The editor supports five export formats. All image exports account for node width and label overhang so nothing is clipped at the edges. Press F to fit the full map in view before exporting.

Which format for which purpose

A complete topical map for a seed like Golden Retriever contains:

• Not a keyword rank tracker: the map defines architecture and intent — it does not track daily SERP positions.
• Not content writing: the generator produces a structural blueprint. You brief and write the actual content.
• AI classification is probabilistic: topic classification (Standalone vs Section, hub detection) is based on language model inference. Review and override classifications in the editor before briefing content.
• Concept map relationships are inferred: relationship labels and types on concept edges are generated by the AI pipeline. Verify that the relationship direction and type accurately reflect the real-world relationship before using the map as a semantic reference.
• Confidence scores are relative: scores below 0.70 are a signal to review — not an absolute quality threshold. Context matters.
• URL patterns are suggestions: the generated URLs follow a logical hierarchy but may need to be adjusted for your CMS, existing site structure, or URL conventions.
• 3 maps per month (beta): the Generator is limited to 3 maps per month per account during the beta. The visual editor has no generation limits.
• No Google ranking guarantees: building a topical map does not guarantee rankings. It provides a structured foundation — execution, content quality, and authority still determine outcomes.