hr-network-analyst
Professional network graph analyst identifying Gladwellian superconnectors, mavens, and influence brokers using betweenness centrality, structural holes theory, and multi-source network reconstruction. Activate on 'superconnectors', 'network analysis', 'who knows who', 'professional network', 'influence mapping', 'betweenness centrality'. NOT for surveillance, discrimination, stalking, privacy violation, or speculation without data.
When & Why to Use This Skill
The HR Network Analyst skill leverages advanced graph theory and network science to map professional ecosystems and identify key influence brokers. By analyzing betweenness centrality and structural holes, it uncovers 'Gladwellian' superconnectors and mavens who drive information flow. This skill is essential for professionals looking to optimize recruitment strategies, understand organizational dynamics, and identify high-value networking targets through data-driven relationship mapping.
Use Cases
- Strategic Recruitment: Identifying 'superconnectors' within niche industries (e.g., AI safety or biotech) to find high-quality candidates through optimal referral paths.
- Organizational Network Analysis (ONA): Mapping informal influence structures and communication pathways within a company to identify key knowledge brokers and bridge structural holes.
- Market Influence Mapping: Visualizing how influence flows between research institutions, companies, and individual thought leaders to understand the competitive landscape.
- Networking Optimization: Finding the most efficient 'shortest paths' to connect with industry leaders by identifying individuals who bridge disparate professional clusters.
- Talent Intelligence: Analyzing co-authorship, conference participation, and project collaborations to rank experts based on their actual network impact rather than just popularity.
| name | hr-network-analyst |
|---|---|
| description | Professional network graph analyst identifying Gladwellian superconnectors, mavens, and influence brokers using betweenness centrality, structural holes theory, and multi-source network reconstruction. Activate on 'superconnectors', 'network analysis', 'who knows who', 'professional network', 'influence mapping', 'betweenness centrality'. NOT for surveillance, discrimination, stalking, privacy violation, or speculation without data. |
| allowed-tools | Read,Write,Edit,WebSearch,WebFetch,mcp__firecrawl__firecrawl_search,mcp__firecrawl__firecrawl_scrape,mcp__brave-search__brave_web_search,mcp__SequentialThinking__sequentialthinking |
| category | Research & Analysis |
| - skill | competitive-cartographer |
| reason | Map competitive professional landscape |
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 classificationreferences/graph-databases.md- Neo4j, Neptune, TigerGraph, ArangoDB query examplesreferences/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