Visualizing Margaret Cavendish’s Social Network

Below is a visualization of Margaret Cavendish’s social network, and a few textual networks. Using the data visualization software Gephi, Cavendish’s network is visualized by using data from four sources: Katie Whitaker’s Mad Madge; Douglas Grant’s Margaret the First; and the Oxford Dictionary of National Biography section on Margaret Cavendish written by Dr. James Fitzmaurice. The visualization, thus far, only includes information up to 1663, and is still in the process of being updated. The circular nodes represent individuals, and in a few cases particular texts, while the linear edges represent the connections between the individuals. I have isolated three nodes, Margaret Cavendish, William Cavendish, and Sir Charles Cavendish to better highlight the individual edges.

Social Network

A social network visualization of Margaret Cavendish. The layout of the visualization is based on the ForceAtlas layout and the modularity statistic.

A closer look at the networks:

Close up of William Cavendish's node and edges.

Close up of William Cavendish’s node and edges.

Edges are converging.

Edges are converging.

A close-up of Charles Cavendish's node and edges.

A close-up of Charles Cavendish’s node and edges.

A close-up of Margaret Cavendish's node and edges.

A close-up of Margaret Cavendish’s node and edges.

Margaret (zoomed) 2 CavendishNetwork (6) CavendishNetwork (7)

The very first visualization of Cavendish's networks. This was transformed into the larger visualization at the top of the page.

The very first visualization of Cavendish’s networks. This was transformed into the larger visualization at the top of the page.

The Digital Cavendish Project has continued to update the data related to this project. While these early visualizations were helpful in better understanding communities of networks, we wanted to also look at the geographical layout of the communities to better understand how geography affected the networks at play. Below we have posted a few updates that are still currently being tested and updated. We have also included a few examples and an early look at the raw data. Please feel free to download the Nodes and Edges table, along with the Gephi file if you’re interested in working with our data.


This early image of one visualization uses the Geolayout in order to space communities based on geographical location. In making the data sets we attempted to carefully note where Margaret Cavendish would have had first contact with any other node. We did the same for William Cavendish and his brother Sir Charles Cavendish. This early image shows the nodes as not overlapping, so the largest cluster representing England is easier to see, but the original data would show a significantly larger, but harder to read cluster around different areas on England like London, Colchester, Oxford, etc. As you can see from this image, three significant clusters appear right away around London, Paris, and Antwerp (as expected).


Here’s an image of the early network in Gephi. We have finished building the node data set, but we are currently working on the edges data set.

The goal will be to visualize the networks on to a map to pinpoint the geographical layouts of the networks and communities. Then we will attempt to create a moving network, one that not only tracks geographical movements, but does so in time, so we can track when the networks are created in place and in time. Thus, we can track Margaret’s movements and networks as they build and as they corrode throughout her life.

If you’re interested in the raw data, please feel free to download them here:



Cavendish Network Gephi File

[Note: all work on the data and visualizations has been performed by Shawn W. Moore for his dissertation on Margaret Cavendish. If you do use any of the work present here, please indicate where the work originated and please give credit to the original author.]

Suggested citation:

Moore Shawn. “Visualizing Margaret Cavendish’s Social Network.” Digital Cavendish Project. 30 March 2018.