HR Network Analyst

Applies graph theory and network science to professional relationship mapping. Identifies hidden superconnectors, influence brokers, and knowledge mavens that drive professional ecosystems.

Integrations

Works with: career-biographer, competitive-cartographer, research-analyst, cv-creator

Core Questions Answered

• Who should I know? (optimal networking targets)
• Who knows everyone? (superconnectors for referrals)
• Who bridges worlds? (cross-domain brokers)
• How does influence flow? (information/opportunity pathways)
• Where are structural holes? (untapped connection opportunities)

Quick Start

User: "Who are the key connectors in AI safety research?"

Process:
1. Define boundary: AI safety researchers, 2020-2024
2. Identify sources: arXiv, NeurIPS workshops, Twitter clusters
3. Compute centrality: betweenness (bridges), eigenvector (influence)
4. Classify by archetype: Connector, Maven, Broker
5. Output: Ranked list with network position rationale

Key principle: Most valuable people aren't always most famous—they connect otherwise disconnected worlds.

Gladwellian Archetypes (Quick Reference)

Full theory: See references/network-theory.md

Centrality Metrics (Quick Reference)

Analysis Workflows

1. Find Superconnectors for Referrals

• Define target domain → Seed network → Expand → Compute betweenness + degree → Rank

2. Map Domain Influence

• Define boundaries → Multi-source construction → Community detection → Identify brokers

3. Optimize Personal Networking

• Map current network → Map target domain → Find shortest paths → Identify structural holes

4. Organizational Network Analysis (ONA)

• Collect data (surveys, Slack metadata) → Construct graph → Find informal vs formal structure

Detailed workflows: See references/data-sources-implementation.md

Data Sources

Multi-source fusion: Weight and combine signals for robust network

When NOT to Use

• Surveillance: Tracking individuals without consent
• Discrimination: Using network position to exclude
• Manipulation: Engineering social influence for harm
• Privacy violation: Accessing non-public data
• Speculation without data: Guessing network structure

Anti-Patterns

Anti-Pattern: Degree Obsession

What it looks like: Only looking at who has most connections Why wrong: High degree often = noise; connectors differ from popular Instead: Use betweenness for bridging, eigenvector for influence quality

Anti-Pattern: Static Network Assumption

What it looks like: Treating 5-year-old connections as current Why wrong: Networks evolve; old edges may be dead Instead: Recency-weight edges, verify currency

Anti-Pattern: Single-Source Reliance

What it looks like: Using only LinkedIn data Why wrong: Missing relationships not on LinkedIn Instead: Multi-source fusion with source-appropriate weighting

Anti-Pattern: Ignoring Context

What it looks like: High betweenness = valuable, regardless of domain Why wrong: Bridging irrelevant communities isn't useful Instead: Constrain analysis to relevant domain boundaries

Ethical Guidelines

Acceptable:

• Analyzing public data (conference speakers, publications)
• Aggregate pattern analysis
• Opt-in organizational analysis
• Academic research with proper IRB

NOT Acceptable:

• Scraping private profiles without consent
• Building surveillance systems
• Selling individual data
• Discrimination based on network position

Troubleshooting

Reference Files

• references/algorithms.md - NetworkX code patterns, centrality formulas, Gladwell classification
• references/graph-databases.md - Neo4j, Neptune, TigerGraph, ArangoDB query examples
• references/data-sources.md - LinkedIn network data acquisition strategies, APIs, scraping, legal considerations

Core insight: Advantage comes from bridging otherwise disconnected groups, not from connections within dense clusters. — Ron Burt, Structural Holes Theory