Epistolary

Her friends form a map of her world
Form a map of her world
You can see she’s a popular girl
She’s a popular girl
Everyone around her wants to pick up pen and write
Their choice of correspondent sheds a little bit of light on things they share
Her ego-network is beyond compare 1

Epistolary (epistolary)
Networks: in or out degree? 2
Epistolary (epistolary)
Networks: What’s the best centrality? 3
(Epistolary)
Networks: in or out degree?
Epistolary (epistolary)
Networks: What’s the best centrality?

Her friends’ friends are all round the world
Friends are all round the world
And they read she’s a popular girl
She’s a popular girl
She’s one node in their network of superlative degree
Reciprocal connections make her part of multiple communities
Their density approaches unity 4

Epistolary (epistolary)
Networks: in or out degree?
Epistolary (epistolary)
Networks: What’s the best centrality?
(Epistolary)
Networks: in or out degree?
Epistolary (epistolary)
Networks: What’s the best centrality?

And she’s part of the core 5
But not part of a clique 6
Yet she’s got a high, very high, betweenness centrality 7
Like Magliabechi she’s a knowledge broker 8
A knowledge broker yeah
She shares with A, she learns from B
She gatekeeps C, yeah, yeah

(Epistolary)
She opts to share with A, to learn from B
To gatekeep C, yeah, yeah
(repeat)

Epistolary (epistolary)
Networks: in or out degree?
Epistolary (epistolary)
Networks: What’s the best centrality?
(epistolary)
Networks: in or out degree?
Epistolary (epistolary)
Networks: What’s the best centrality?

Notes

1. “Ego-centric networks (or shortened to ‘ego’ networks) are a particular type of network which specifically maps the connections of and from the perspective of a single person (an ‘ego’).” Omar Lizardo & Isaac Jilbert (2021), “Social Networks: An introduction”, https://bookdown.org/omarlizardo/_main/2-10-ego-centric-networks.html, accessed 5 April 2023. Back to text
2. The “degree” of a node in a network is the number of other nodes connected to it. In a directed network one can also distinguish between indegree (number of inbound links) and outdegree (number of outbound links). In an epistolary network, the indegree of a person would be the number of letters received, and outdegree would be the number of letters sent. See e.g. Martin Grandjean and Mathieu Jacomy (2019), “Translating Networks: Assessing correspondence between network visualisation and analytics”, Digital Humanities 2019, https://serval.unil.ch/resource/serval:BIB_31E33B306611.P001/REF, accessed 5 April 2023 Back to text
3. There are several ways of measuring how central a node is to a network:
   • Degree centrality: number of connections to other nodes
   • Betweenness centrality: number of times on the shortest route between two nodes
   • Closeness centrality: average length of the shortest routes to all other nodes
   • Eigenvector or PageRank centrality: proximity to well-connected nodes
   See e.g. Grandjean and Jacomy (above, note 2). Back to text
4. The density of a network is the number of edges (connections between nodes) divided by the number of possible edges. A network with a density of one means every node is connected to every other node. See e.g. Omar Lizardo & Isaac Jilbert (2021), “Social Networks: An introduction”, https://bookdown.org/omarlizardo/_main/2-9-density.html, accessed 5 April 2023. Back to text
5. A network may be divided into a “core and a periphery, such that there are dense connections within the core and sparse connections within the periphery.” Andrew Elliott, Angus Chiu, Marya Bazzi, Gesine Reinert and Mihai Cucuringu (2020) “Core–periphery structure in directed networks”, Proceedings of the Royal Society A. 476: 20190783, https://doi.org/10.1098/rspa.2019.0783, accessed 5 April 2023. Back to text
6. A clique is the largest subset of a network in which all the members are connected to all the other members. See e.g. Robert A. Hanneman (2005), “Introduction to social network methods”, https://faculty.ucr.edu/~hanneman/nettext/C11_Cliques.html, accessed 5 April 2023. Back to text
7. A node with high betweenness centrality “[connects] otherwise separated groups of nodes.” Martin Grandjean and Mathieu Jacomy (2019), “Translating Networks: Assessing correspondence between network visualisation and analytics”, Digital Humanities 2019, https://serval.unil.ch/resource/serval:BIB_31E33B306611.P001/REF, p.8, accessed 5 April 2023. Back to text
8. Florentine librarian Antonio Magliabechi (1633–1714) was a key information broker in the Republic of Letters. See Ingeborg van Vugt (2022), “Networking in the Republic of Letters: Magliabechi and the Dutch Republic”, The Journal of Interdisciplinary History 53(1): 117–141, https://doi.org/10.1162/jinh_a_01800, accessed 5 April 2023. Back to text