Knowledge Graph

How your knowledge connects and compounds

From Files to Networks

Traditional knowledge management is hierarchical:

Folders → Subfolders → Files → Content

ATLAS knowledge management is networked:

Concepts ↔ Concepts ↔ Content ↔ Insights ↔ Actions

The graph structure reflects how ideas actually relate — not in neat folders, but in messy, beautiful webs of connection.

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Graph Structure

Nodes (Entities)

Node Type	Description	Example
Content	Original source material	A tweet, note, or article
Concept	Key idea or topic	"Antifragility", "Network Effects"
Insight	Actionable learning	"Systems fail at boundaries"
Action	Task or exploration	"Research agent architectures"
Person	Referenced individual	Authors, experts, contacts
Source	Origin platform	Twitter, Apple Notes, etc.

Edges (Relationships)

Relationship	Meaning
CONTAINS	Content contains Concept
RELATES_TO	Concept relates to Concept
IMPLIES	Insight implies Action
REFERENCES	Content references Person
DERIVED_FROM	Entity derived from Content

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Graph Benefits

1. Connection Discovery

When you search for a concept, you don't just get direct matches. You see:

• Related concepts (1 hop away)

• Supporting content

• Derived insights

• Suggested actions

• Connected people

Example Search: "Network Effects"

Direct Results:
├── 12 content items mentioning network effects
├── 8 related concepts (viral growth, marketplaces, winner-take-all)
├── 5 insights about network effects
└── 3 action items

Graph Exploration:
├── "Platform Strategy" (strongly related)
├── "Aggregation Theory" (moderately related)
├── "Ben Thompson" (referenced author)
└── "Research: Metcalfe's Law applications" (suggested action)

2. Serendipitous Retrieval

The graph surfaces connections you didn't explicitly create:

"I searched for 'writing systems' and ATLAS showed me a connection to a tweet about Zettelkasten I saved two years ago. I'd completely forgotten about it."

3. Knowledge Density Maps

Some areas of your knowledge are denser than others. The graph reveals:

• Where your expertise concentrates

• Knowledge gaps to fill

• Emerging interest clusters

4. Temporal Evolution

Watch your knowledge grow over time:

• When did you first encounter an idea?

• How has your understanding evolved?

• Which concepts are you actively building on?

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Visualization

Interactive Graph View

The ATLAS dashboard includes a D3.js-powered graph visualization:

• Zoom and pan through your knowledge network

• Click nodes to explore connections

• Filter by type (concepts, content, insights)

• Search highlights matching nodes

• Cluster detection shows knowledge domains

Graph Metrics

Metric	What It Shows
Node Count	Size of your knowledge base
Edge Count	Connectedness of ideas
Average Degree	How linked each concept is
Clustering Coefficient	How ideas cluster together
Central Concepts	Most connected ideas

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Technical Architecture

Storage Layer

SQLite + FTS5
├── Full-text search with relevance ranking
├── Efficient graph traversal via indexes
└── Portable, single-file database

Graph Schema

-- Nodes
content(id, source, text, metadata, created)
concepts(id, name, description, importance, category)
insights(id, text, concept_id, importance)
actions(id, text, status, priority)

-- Edges
relationships(source_id, target_id, type, weight)

Query Capabilities

ATLAS supports multiple query patterns:

Full-Text Search

"Find all content mentioning 'machine learning'"

Graph Traversal

"Find concepts related to 'productivity' within 2 hops"

Aggregation

"Show my top 10 concepts by importance score"

Temporal

"Show concepts added in the last 30 days"

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The Network Effect of Knowledge

Here's the key insight:

Individual notes have linear value. Connected knowledge has exponential value.

Notes	Isolated Value	Connected Value
100	100	100 + connections
1,000	1,000	1,000 + 10x connections
10,000	10,000	10,000 + 100x connections

As your graph grows, each new piece of knowledge:

• Enriches existing nodes

• Creates new connection possibilities

• Increases the value of everything already there

Your knowledge graph exhibits network effects.