Knowledge Graph Basics

2026-02-12· 3 min read· 472 words

Understanding how knowledge graphs help connect and surface ideas.

#pkm#graph#networked-thinking#theory

Knowledge Graph Basics

A knowledge graph represents information as a network of interconnected ideas. Unlike hierarchical folder structures, graphs let ideas live in multiple contexts simultaneously.

Core Concepts

Nodes

Each note, article, or piece of content is a node in the graph. Nodes contain:

  • A unique identifier (the slug)
  • Content (text, images, code)
  • Metadata (title, date, tags)

In Rhizome, every MDX file becomes a node.

Edges

Links between notes create edges that show relationships. Edges can represent:

  • Reference - "See also this related concept"
  • Elaboration - "Here's more detail about this idea"
  • Contrast - "Here's an opposing viewpoint"
  • Application - "Here's how this applies in practice"

Wiki-links create edges automatically. When you link from note A to note B, you create a directed edge A → B.

Backlinks show incoming edges - which notes link to the current note. They answer: "What points here?"

ℹ️

Backlinks often reveal connections you didn't plan. A note about "feedback" might link to notes about "learning," "communication," and "system design" - showing how the concept bridges domains.

Why Graphs Matter

Emergent Structure

Traditional hierarchies force you to decide where something "belongs." Graphs let structure emerge from connections.

A note about "Decision Making" might connect to:

  • Cognitive biases
  • Team dynamics
  • Data analysis
  • Risk assessment

Each connection is a potential path to discovery.

Context Preservation

When you follow a link, you see not just the destination but how you got there. The link context (surrounding text) explains the relationship.

Serendipitous Discovery

The graph view reveals:

  • Clusters - Groups of densely connected notes
  • Bridges - Notes that connect different clusters
  • Orphans - Isolated notes that might need connections
  • Hubs - Notes with many connections (often core concepts)

Graph Theory Basics

Degree

The number of connections a node has:

  • In-degree: Incoming links (backlinks)
  • Out-degree: Outgoing links

High-degree nodes are often important concepts in your knowledge base.

Paths

A sequence of edges connecting two nodes. The "distance" between notes in your graph affects how easily you can traverse from one idea to another.

Clustering

Nodes tend to cluster around topics. Strong clustering indicates a well-developed domain of knowledge.

💡

If your graph is too clustered, you may be creating silos. Try creating "bridge notes" that connect different topic areas.

Building Better Graphs

When writing, ask:

  • What other notes relate to this?
  • What background would help understand this?
  • Where might someone want to go next?

Use Descriptive Link Text

Instead of: "See Knowledge Graph Basics for more."

Try: "Understanding Knowledge Graph Basics helps you see how connections emerge naturally."

Create "Hub" Notes

Some notes serve as navigational hubs:

  • MOCs (Maps of Content) for major topics
  • Index notes for projects
  • Overview notes for domains

Review the Graph

Regular graph review reveals:

  • Orphan notes that need connections
  • Dense clusters that might be separate domains
  • Gaps in your knowledge

Rhizome's Graph View

The /graph page visualizes your knowledge network:

  • Node size reflects connection count
  • Hover shows note details
  • Click navigates to the note
  • Dark mode changes diagram colors

Backlinks