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🔬 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.

Allowed Tools​

Read, Write, Edit, WebSearch, WebFetch, mcp__firecrawl__firecrawl_search, mcp__firecrawl__firecrawl_scrape, mcp__brave-search__brave_web_search, mcp__SequentialThinking__sequentialthinking

Tags​

network superconnectors influence graph-theory hr

🤝 Pairs Great With​

References​

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)​

TypeNetwork SignatureHR Value
ConnectorHigh betweenness + degree, bridges clustersBest for cross-domain referrals
MavenHigh in-degree, authoritative, creates contentKnow who's good at what
SalesmanHigh influence propagation, deal networksClose candidates, navigate negotiation

Full theory: See references/network-theory.md

Centrality Metrics (Quick Reference)​

MetricMeaningWhen to Use
BetweennessControls information flowFinding gatekeepers, brokers
DegreeRaw connection countMaximizing referral reach
EigenvectorQuality over quantityAccess to power, rising stars
PageRankEndorsed by important othersThought leaders
ClosenessCan reach anyone quicklyInformation 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​

SourceSignal StrengthWhat to Extract
Co-authorshipVery strongPublication collaborations
Conference co-panelStrongSpeaking relationships
GitHub co-repoMedium-strongCode collaboration
LinkedIn connectionMediumProfessional links
Twitter mutualWeakSocial 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​

IssueCauseFix
Can't find dataDomain small/privateSnowball sampling, surveys, adjacent communities
False edgesOver-weighting weak signalsRequire multiple signals, threshold weights
Too largeUnconstrained boundaryK-core filtering, high-weight only
Entity resolutionSame person, different namesUnique 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