# What is social network analysis?

> Social network analysis studies the ties between people rather than the people alone. The core ideas, the key measures, and three classic examples you can open.

Source: https://netgraf.isik.co/learn/social-network-analysis
Updated: 2026-09-24

Social network analysis (SNA) is the study of the ties between people rather than the people themselves. It treats a group as a graph of nodes and edges and asks structural questions: who is central, who connects separate groups, how tightly knit the group is, and where it would split.

## The core idea

Most analysis looks at individuals and their attributes: age, role, income. Network analysis looks at the relationships between them, on the idea that where a person sits in a web of ties shapes what they can see and do. Two people with the same job title can hold very different amounts of influence because one of them is the only link between two departments.

The field grew out of Moreno's [sociograms](https://netgraf.isik.co/learn/sociogram) in the 1930s, took its mathematical form from graph theory in the 1950s to 1970s, and now runs through sociology, organisational research, epidemiology, journalism and intelligence work.

## The measures, in plain words

| Measure | The question it answers |
| --- | --- |
| [Degree centrality](https://netgraf.isik.co/learn/degree-centrality) | Who has the most ties? |
| [Betweenness centrality](https://netgraf.isik.co/learn/betweenness-centrality) | Who sits on the paths between everyone else? |
| [Closeness centrality](https://netgraf.isik.co/learn/closeness-centrality) | Who can reach everyone in the fewest steps? |
| [Eigenvector centrality and PageRank](https://netgraf.isik.co/learn/eigenvector-centrality) | Who is tied to other well-tied people? |
| [Density](https://netgraf.isik.co/learn/network-density) | How many of the possible ties exist? |
| [Clustering coefficient](https://netgraf.isik.co/learn/clustering-coefficient) | Do my contacts know each other? |
| [Communities](https://netgraf.isik.co/learn/community-detection) | Which groups are tighter inside than out? |
| [Centralization](https://netgraf.isik.co/learn/network-centralization) | Is the network run through one hub? |
| [Brokers and cut points](https://netgraf.isik.co/learn/brokers-and-bridges) | Whose removal would split the group? |
| [Assortativity](https://netgraf.isik.co/learn/assortativity) | Do the well connected stick together? |

## Three classic studies

- **Zachary's karate club (1977)**: 34 members of a university club that split in two after a dispute. The network drawn before the split predicts, almost exactly, who went with which leader.
- **The Florentine families (Padgett and Ansell, 1993)**: marriage ties among Renaissance Florence's elite. The Medici were not the richest family, but they sat between families that had no ties to each other.
- **Krackhardt's kite (1990)**: a ten-person network built to show that "most central" has at least three right answers depending on the measure.

All three are open as live maps below. Open one and press Insights to see its measures.

## Doing SNA without code

Serious network research usually runs in Gephi, R (igraph, statnet) or Python (NetworkX). For a group of tens or a few hundred people, most of the measures above are also one click away in Netgraf's insights panel, and exports carry them as columns in the CSV and GraphML so the work can continue in those tools. See [Netgraf vs Gephi](https://netgraf.isik.co/compare/gephi) for where each fits.

## Questions

### What data do I need for social network analysis?

At minimum an edge list: two columns naming who is tied to whom. A second table of people with attributes such as role or team makes the results easier to read. Surveys, email metadata, co-authorship and public records are common sources.

### Which social network analysis measure should I use?

It depends on the question. Use degree for popularity or activity, betweenness for brokerage and control over flow, closeness for speed of reach, and eigenvector or PageRank for influence through well-connected contacts. Reporting two or three together is usually more honest than picking one.

## Example maps

- https://netgraf.isik.co/m/zachary-karate-club
- https://netgraf.isik.co/m/florentine-families
- https://netgraf.isik.co/m/krackhardt-kite

## References

- Wasserman, S., & Faust, K. (1994). Social Network Analysis: Methods and Applications. Cambridge University Press.
- Zachary, W. W. (1977). An information flow model for conflict and fission in small groups. Journal of Anthropological Research, 33(4), 452-473. https://doi.org/10.1086/jar.33.4.3629752
- Padgett, J. F., & Ansell, C. K. (1993). Robust action and the rise of the Medici, 1400-1434. American Journal of Sociology, 98(6), 1259-1319. https://doi.org/10.1086/230190
- Krackhardt, D. (1990). Assessing the political landscape: Structure, cognition, and power in organizations. Administrative Science Quarterly, 35(2), 342-369. https://doi.org/10.2307/2393394