Find Connections

# Spreading activation search (recommended for connection discovery)
BRAIN_READ_SCOPE=core,Books,document-insights resources/local-brain-search/run_search.sh "query" --mode spreading --limit 10 --json

# Static search (for exact lookups)
BRAIN_READ_SCOPE=core,Books,document-insights resources/local-brain-search/run_search.sh "query" --limit 10 --json

# Force synthesis intent (maximum graph exploration)
BRAIN_READ_SCOPE=core,Books,document-insights resources/local-brain-search/run_search.sh "query" --mode spreading --intent synthesis --json

# Find connections
BRAIN_READ_SCOPE=core,Books,document-insights resources/local-brain-search/run_connections.sh "Note Name" --json

# Find hubs
resources/local-brain-search/run_connections.sh --hubs --json

# Find bridges
resources/local-brain-search/run_connections.sh --bridges --json

# Get stats
resources/local-brain-search/run_connections.sh --stats --json

# Connection Map: [Starting Note/Topic]

> 🤖 **AI-Discovered Connections**
> This connection analysis was generated by AI using semantic similarity algorithms.
> All connections, patterns, and insights below are AI-identified and should be reviewed critically.

## 🎯 Anchor Point
**Note:** [[Note Name]]
**Core Concept:** [1-sentence summary]
**Domain:** [Primary field/cluster]

---

## 🔗 Direct Connections (Layer 1)
[Top 5-7 notes with highest similarity]

| Note | Similarity | Connection Type | Why Connected | AI Confidence |
|------|-----------|-----------------|---------------|---------------|
| [[Note 1]] | 0.85 | Definitional | Explains core mechanism | High (>0.8) |
| [[Note 2]] | 0.82 | Application | Practical implementation | High (>0.8) |
| ... | ... | ... | ... | ... |

**Connection Types:** Definitional, Evidential, Application, Contrast, Analogy, Causal
**Note:** All connections are AI-inferred from semantic embeddings

---

## 🌉 Bridge Notes
[Notes that connect disparate clusters - these are key integrators]

### [[Bridge Note 1]]
- **Connects:** [Cluster A] ↔ [Cluster B]
- **Mechanism:** [How it bridges the concepts]
- **Significance:** [Why this connection matters]
- **AI Identification:** Detected through multi-hop semantic analysis

---

## 🕸️ Network Structure (3 Layers Deep)

> **Calibration (2026-09-02).** The layer bands below are **percentiles of an established-note
> population, not universal strength grades** — in that population (a note title queried against
> the whole vault) the median best neighbour is 0.724 and 43% of notes have a >=0.75 neighbour, so
> 0.75/0.65/0.60 sit at roughly the 60th/73rd/80th percentile. **Against a fresh ingestion session
> the same bands return nothing**: new external material queried against `core` maxes out at 0.560
> (median 0.474). When the anchor is a recently ingested note, shift the whole ladder down to
> **0.50 / 0.42 / 0.35** and rely on reading the notes, not on the number.
> Contract + live figures: `resources/local-brain-search/SIMILARITY-CALIBRATION.md`.
> Note also that the printed `similarity` from `run_search.sh` is a Q-adjusted ranking score,
> not raw cosine (Trap 1 there).

---

## 💡 Emergent Patterns
*🤖 AI-detected patterns based on semantic clustering*

### Pattern 1: [Pattern Name]
**Appears in:** [[Note A]], [[Note B]], [[Note C]]
**Description:** [What the pattern is]
**Insight:** [What this reveals about your thinking]
**AI Method:** Identified through cross-note thematic analysis

### Pattern 2: [Pattern Name]
...

---

## 🔍 Non-Obvious Connections
*🤖 AI-suggested connections requiring human validation*

### Surprising Link 1: [[Note X]] ↔ [[Note Y]]
- **Similarity:** 0.72
- **Surface difference:** [Why these seem unrelated]
- **Deep connection:** [The underlying shared principle]
- **Insight value:** [What you can learn from this connection]
- **Validation needed:** This is an AI hypothesis - verify if conceptually meaningful

---

## 🎨 Conceptual Clusters Identified

**Cluster 1: [Cluster Name]**
- Core notes: [[Note 1]], [[Note 2]], [[Note 3]]
- Theme: [Central idea]
- Density: [High/Medium/Low connectivity]

**Cluster 2: [Cluster Name]**
...

---

## 🔭 Knowledge Gaps & Opportunities

### Missing Connections
[Valuable notes that should be connected but aren't]

### Underdeveloped Themes
[Promising ideas that need more exploration]

### Potential Synthesis Opportunities
[Multiple notes that could be synthesized into an article/framework]

---

## 📊 Network Statistics

- **Direct connections:** [Number]
- **Total network size (3 layers):** [Number] notes
- **Strongest connection:** [[Note]] (similarity: 0.XX)
- **Most connected hub:** [[Note]] ([N] connections)
- **Clusters identified:** [Number]
- **Cross-cluster bridges:** [Number]

---

## 🎯 Actionable Insights

> ⚠️ **Human Review Required**
> These are AI-generated suggestions based on computational analysis.
> They should be validated against your actual understanding and goals.

1. **Content Creation Opportunity:** [What article/framework could be created]
2. **Connection to Make:** Link [[Note A]] to [[Note B]] because [reason]
3. **Deep Dive Suggested:** Explore [theme] further
4. **Synthesis Potential:** Combine insights from [cluster] into [output]

---

## 📝 Methodology Note

**How This Analysis Was Generated:**
- Semantic embeddings: all-MiniLM-L6-v2 (384 dimensions)
- Similarity algorithm: Cosine similarity between note embeddings
- Connection graph: Multi-hop traversal with threshold filtering
- **Spreading activation**: SYNAPSE-inspired graph traversal (when using `--mode spreading`)
- **Brain Dependency Graph**: Typed edges (derives-from, instantiates, references, associates, tension) via `resources/brain-graph/run_brain_graph.sh inspect "Note" --json`
- Pattern detection: AI interpretation of semantic clusters
- All findings are computational approximations requiring human validation
- Configuration: `resources/local-brain-search/memory_config.py`