# Assortativity

> Assortativity measures whether well-connected people tie to each other or to the less connected. Newman's coefficient, how to read it, and an example.

Source: https://netgraf.isik.co/learn/assortativity
Updated: 2026-09-24

Assortativity measures whether people tend to be tied to others like themselves. Degree assortativity, the usual form, asks whether well-connected people link to other well-connected people (positive) or to people with few ties (negative). It runs from -1 to 1.

## How it's calculated

```
r = correlation between the degrees at the two ends of every tie
```

Newman's coefficient is the Pearson correlation of degree across tie ends. It is undefined when everyone has the same degree.

## Reading it

Mark Newman found in 2002 that social networks tend to be assortative, with popular people knowing each other, while technological and biological networks tend to be disassortative, with hubs linking to many small nodes. Small groups built around one or two leaders often come out strongly negative: in [Zachary's karate club](https://netgraf.isik.co/m/zachary-karate-club) the two leaders are tied to many members who have few other ties.

## In Netgraf

The **Shape** card in the Insights panel gives the coefficient and a sentence: "well connected people tie to each other", "well connected people tie to quiet ones", or "no pattern between the busy and the quiet".

## Questions

### Can assortativity be measured on attributes other than degree?

Yes. The same idea applies to any attribute: do people tie to others of the same team, age or party? That is often called homophily, and it is one of the most consistent findings in social science.

## Example maps

- https://netgraf.isik.co/m/zachary-karate-club

## References

- Newman, M. E. J. (2002). Assortative mixing in networks. Physical Review Letters, 89(20), 208701. https://doi.org/10.1103/PhysRevLett.89.208701