curiositech 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