Social Network Analysis

Overview

Social network analysis examines relationships (ties) between actors (nodes) to reveal structure invisible in org charts. It identifies who really holds influence, where information bottlenecks exist, and how ideas spread through a community.

Framework

IRON LAW: Structure Determines Influence, Not Just Position

A mid-level manager who bridges two disconnected departments may have more
real influence than a VP who sits in a dense, well-connected cluster.
Network position (centrality, brokerage) determines influence more than
formal hierarchy.

Core Concepts

Concept	Definition	Why It Matters
Node	An actor (person, org, entity)	Who's in the network
Tie	A relationship between nodes	How nodes are connected
Strong tie	Frequent, emotional, reciprocal relationship	Trust, support, reliable info
Weak tie (Granovetter)	Infrequent, casual, bridging relationship	Access to NEW information and opportunities
Degree centrality	Number of direct connections	Popularity, activity
Betweenness centrality	How often a node sits on shortest paths between others	Brokerage, gatekeeping, information control
Closeness centrality	Average distance to all other nodes	Speed of information reach
Structural hole (Burt)	Gap between two clusters, bridged by a broker	Source of competitive advantage — the broker controls information flow

Analysis Steps

1. Define the network: Who are the nodes? What constitutes a tie? (communication, trust, advice, collaboration)
2. Collect data: Surveys ("who do you go to for advice?"), email/Slack data, meeting co-attendance
3. Map the network: Visualize nodes and ties
4. Calculate centrality metrics: Degree, betweenness, closeness for each node
5. Identify structural patterns: Clusters, bridges, isolates, structural holes
6. Interpret for action: Who are the key connectors? Where are the bottlenecks?

Output Format

# Network Analysis: {Context}

## Network Definition
- Nodes: {who} (N = {count})
- Tie definition: {what constitutes a connection}
- Data source: {survey / communication data / observation}

## Key Metrics
| Node | Degree | Betweenness | Role |
|------|--------|-------------|------|
| {person} | {N connections} | {score} | Hub / Bridge / Isolate |

## Structural Findings
- Clusters: {identified groups}
- Bridges: {who connects clusters}
- Structural holes: {where gaps exist}
- Isolates: {disconnected nodes}

## Implications
1. {finding → action}

Examples

Correct Application

Scenario: Advice network in a 50-person startup

• Node with highest betweenness centrality: Product Manager (not the CEO) — she bridges engineering, design, and business teams
• Structural hole: Marketing team has zero direct ties to engineering — all communication goes through PM
• Implication: If PM leaves, information flow between 3 teams collapses. Need to create direct cross-functional ties ✓

Incorrect Application

• "The CEO has the most connections, so he's the most influential" → CEO has high degree centrality (many ties) but may have low betweenness (everyone also connects to each other without needing the CEO). Violates Iron Law: structure determines influence, not just connection count.

Gotchas

• Granovetter's strength of weak ties: Weak ties are MORE valuable for accessing new information and opportunities because they bridge different social circles. Strong ties share redundant information.
• Network data is sensitive: Mapping who talks to whom can feel like surveillance. Be transparent about purpose and anonymize where possible.
• Networks change: Relationships evolve. A network map is a snapshot. Remeasure periodically.
• Centrality is context-dependent: High centrality in the advice network ≠ high centrality in the friendship network. Define the tie type carefully.
• Don't confuse correlation with causation: Central people may perform better because of their position, OR they may be central because they perform well. Disentangling is hard.

References

• For network visualization tools and methods, see references/network-tools.md