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