hr-network-analyst

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)

Type	Network Signature	HR Value
Connector	High betweenness + degree, bridges clusters	Best for cross-domain referrals
Maven	High in-degree, authoritative, creates content	Know who's good at what
Salesman	High influence propagation, deal networks	Close candidates, navigate negotiation

Full theory: See references/network-theory.md

Centrality Metrics (Quick Reference)

Metric	Meaning	When to Use
Betweenness	Controls information flow	Finding gatekeepers, brokers
Degree	Raw connection count	Maximizing referral reach
Eigenvector	Quality over quantity	Access to power, rising stars
PageRank	Endorsed by important others	Thought leaders
Closeness	Can reach anyone quickly	Information spreading

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

Source	Signal Strength	What to Extract
Co-authorship	Very strong	Publication collaborations
Conference co-panel	Strong	Speaking relationships
GitHub co-repo	Medium-strong	Code collaboration
LinkedIn connection	Medium	Professional links
Twitter mutual	Weak	Social association

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

Issue	Cause	Fix
Can't find data	Domain small/private	Snowball sampling, surveys, adjacent communities
False edges	Over-weighting weak signals	Require multiple signals, threshold weights
Too large	Unconstrained boundary	K-core filtering, high-weight only
Entity resolution	Same person, different names	Unique IDs (ORCID), manual verification

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

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