Network Analysis
A practical introduction to network analysis for social scientists. Part 1 covers the core concepts; Part 2 walks through a published paper step by step. Designed for a 15–20 minute presentation.
Graduate students across disciplines. No network analysis background assumed; basic familiarity with regression is helpful.
Why Study Relationships, Not Just Individuals?
Most methods treat observations as independent rows. But many social phenomena are relational — who asks whom for study help reveals hidden learning communities that GPA alone cannot explain. Network analysis shifts the question from "what are this person's attributes?" to "what is their position in a web of relationships?"
The Language of Networks
A network has two building blocks: nodes (actors) and edges (relationships). Mathematically, a network is a graph G = (V, E) where V is the vertex set and E the edge set. Everything else builds on these two. Three key distinctions:
Who Is "Important"? Three Answers
"Importance" depends on what you mean. Click each measure below — watch the node sizes change and read why.
Finding Groups: Community Detection
Real networks are cliquish — dense within groups, sparse between them. Community detection algorithms discover these groups automatically from connections alone, no labels needed.
Intuition: find groups where members are more connected to each other than to outsiders — like identifying lunch tables in a high school cafeteria. The algorithm doesn't need labels; it discovers structure purely from the pattern of connections. This is powerful because the groups it finds often reveal real social divisions that surveys miss.
From Description to Inference
Network methods fall into two layers. Descriptive methods tell you what the network looks like; inferential models test why it looks that way.
Descriptive: What Does the Network Look Like?
Inferential: Why Does the Network Look This Way?
Should You Use Network Analysis?
If you can answer "yes" to all three, network analysis is likely the right tool:
From Idea to Paper: A Network Analysis Workflow
Paper Walkthrough: Expertise & Advice Networks
Köhler, Rausch, Biemann & Büchsenschuss (2024). "Expertise and specialization in organizations: a social network analysis." European Journal of Work and Organizational Psychology, 34(2), 282–297. Open Access
Core question: Are you sought for advice because you're truly skilled — or simply because you're well-known?
Research Question & Core Concepts
This paper asks: What drives the formation of advice-seeking ties in an organizational network? Two competing explanations:
Expert (Expertise): sustained superior performance — both breadth and depth, developed through long-term accumulation. Like a senior underwriter who masters all insurance products. In the network, experts are central — many people ask them about many topics.
Specialist (Specialization): mid-to-high competence on tasks others don't do — from division of labor, not necessarily long-term accumulation. Like the only person who knows the legacy claims system. In the network, specialists are peripheral — only people with that specific problem come to them.
From Raw Data to a Network
Sample: N = 344 employees, 78% response rate
Demographics: average age 47, average tenure 11 years, roughly gender-balanced
— 18 core insurance skills
— 7 software skills
— 4 social skills
Self-identity scales:
— "I consider myself an expert"
— "I consider myself a specialist"
Network nomination:
— "Who do you ask for advice?" (up to 5 names × 3 skill areas)
— Age
— Gender
— Tenure (years)
— Job level
— Leadership responsibility (yes/no)
Method: Three Studies in One Paper
The paper uses three studies that build on each other: first discover a pattern, then explain what drives it, then validate whether theoretical categories hold up.
| Study 1: Spearman Correlations | Study 2: ERGM | Study 3: Optimal Matching | |
|---|---|---|---|
| Question | Is being asked for advice tied to your real skill, or just to being popular? | What actually drives advice ties — after removing the network's own patterns? | Do real skill profiles match theoretical expert/specialist templates? |
| Method | Compute in-degree in 3 skill areas; correlate with self-rated skills (Spearman); compare correlations (Fisher z-test). | Build models in 3 steps: (1) baseline (edges + reciprocity); (2) network effects (in-degree, brokerage); (3) personal traits (expertise, skills, tenure). Compare via AIC. R ergm. |
Sort each person's 29 skills high→low; compute "distance" to ideal profiles (R TraMineR); correlate with self-ratings and centrality. |
| Plain English | Count who asks you in each area, then see if that number matches your actual ability — or just your popularity elsewhere. | A logistic regression for networks — test which factors make two people more likely to form a "who asks whom" tie. | Draw a template for "perfect expert" and "perfect specialist," then measure how far each person is from those templates. |
Finding 1: The Halo Effect
Result: Cross-domain in-degree correlations are significantly stronger than within-domain skill–in-degree correlations (Fisher z, p < .05). Exception: core insurance skills, where actual competence also matters.
i.e.: Once colleagues see you as a go-to person for insurance, they also come to you for software and social problems — even if you're not actually good at those. Being asked reflects reputation, not skill-by-skill competence.
Finding 2: What Drives Advice Ties (ERGM)
Core & software skill level — higher skill averages attract more ties.
Expertise identity — small positive effect.
Tenure — longer-tenured get asked more.
Similar tenure — joined around the same time → ask each other.
Same leadership level — managers ask managers.
Social skills — no effect.
Brokerage — negative; people skip middlemen.
i.e.: The network rewards "you helped me before" (reciprocity) and "you're genuinely skilled" (skill level). People ask those who are similar to themselves. But being a "specialist" or having social skills doesn't attract advice ties.
Finding 3: Skill Profiles vs. Theory
i.e.: People who call themselves "experts" really do have broad, high skill sets — the label matches reality. But "specialist" means different things: some are the only lawyer in the office (broad within their niche), others handle one narrow task among peers. The theoretical template only captured the second type, so the match failed.