Week 7: Networks

1. Week 7 Overview
2. Week 7 Reading and Discussion
3. Week 7 Lab: Relational data and relationships in networks
4. Collaborative Data Week 7 NAME REDACTED

Week 7 Overview

Networks and Relational Data

Like maps, exploring networks of human connection isn’t a new task for historians, but the theoretical approaches that underlie Actor-Network Theory and the statistical approach to networks that comes with Social Network Analysis (SNA) are both newer.

Reading: Our independent reading will include a light read on a heavy topic (Actor Network Theory) to make it easier for us to pull apart the ideas that work for historians using networks and the ones that don’t. We’ll also look at “factoids”, the small piecemeal data points that go into creating network data. Finally, we’ll look at how to use network data in a historical argument.

Lab: No Net.Create this week. Sorry. We’ll use Gephi, so that you’re prepared to understand when to use Net.Create and when to use Gephi, for your own work. See Week 7 Lab: Relational data and relationships in networks. This week’s lab is designed for you to explore at home and troubleshoot/discuss in class. Note that the reading is shorter to make time for that.

Collaborative data management: none

Week 7 Reading and Discussion

Network vocabulary: Scott Weingart, “Demystifying Networks I II”, Journal of Digital Humanities , 1.1 (Winter 2011), https://journalofdigitalhumanities.org/1-1/demystifying-networks-by-scott- weingart/

**

Actor-Network Theory Basics: Charlotte Nickerson, Latour’s Actor-Network Theory, https://www.simplypsychology.org/actor-network-theory.html in Simply Pyschology web site. Read “2, Components”, “3, Principles” and “5, Critiques”. Skim sections 1 and 4. Ignore exemplars.

Methods exemplar: Robert Michael Morrisey, “Archives of Connection: ‘Whole Network’ Analysis and Social History.” Historical Methods: A Journal of Quantitative and Interdisciplinary History , 48(2), 67–79. https://doi- org.proxyiub.uits.iu.edu/10.1080/01615440.2014.962208 We Links to an external siteLinks to an external sit

Optional

• advanced data reading: Matthew Hammond, “From Digital Prosopography to Social Network Analysis: Medieval People in the Twenty-First Century.” Medieval People: Social Bonds, Kinship, and Networks , vol. 36, 2021, pp. 235–62. JSTOR , https://www.jstor.org/stable/27206823
• advanced theory reading: https://link.springer.com/article/10.1007/s10816-012-9133-8

**

Discussion :

• How does ANT help frame or obscure the ways that different kinds of centrality function in a historical-actor network?
• What do we lose and gain as historians when we treat people/things/places as data about actors in a network instead of historical subjects about which we close-read in a source?
• What aspects of historical relationships can network analysis make more clear? What is it likely to obscure or hide?

Week 7 Lab: Relational data and relationships in networks

Lab background

This week, we need to understand relational databases, since network data is often structured not as a single table (or spreadsheet) of data but as several tables that work together.

Relational data

When you shop online, you might select 3 items and have them shipped to a single address. The online retailer does NOT replicate your address for each of the 3 items you have shipped. Instead, the online retailer has one dataset for customers, one for items for sale, and one for orders. Each of these datasets is stored together in a “database” and each dataset is called a “table”. Each table has a primary key–a field that has a unique value for each row of data in the field that can be used to look up that row of information from any other table. Here’s an example, color coded. The primary key for each table is bolded and color coded. The Lookup Table at the very end demonstrates which tables are being used to look up information to build a full order.

Have a look at this Google Sheet to see this data, broken out into tables with actual lookup formulas. If something changes in the Customer table, the OrderTracker information will automatically update. Look at the formula in column G3 in the OrderTracker tab for a specific example of how powerful relational databases can be: https://docs.google.com/spreadsheets/d/1DOG7jZ7inqq4uRBHGxIKe6erG37iLXYS3XBtaSlmhes/edit?usp=sharing

Network relational data

In networks, we are working with nodes and edges. Nodes are nouns. Edges are a verb. For edges to work, we need a noun-object and a noun-subject. To represent that in network data, our Nouns are in one table with a primary key. Our edges are in another table. The edge table contains, at minimum, a Source column with the primary-key value for our subject-node and a Target column with the primary-key value for our object-node.

Nodes: 1, Kalani 2, Joe 3, Marcus | Edges 1-2 3-2 2-3 —|—

Lab

• Download Gephi at https://gephi.org/
• Use this tutorial to work with Gephi data: Frank W. Takes, Gephi Tutorial for Graph/Network Visualization [online], https://github.com/franktakes/gephi-tutorial, 2024.

Further resources on networks

• Dr. Jennifer Golbeck’s YouTube channel and its Gephi tutorials: https://www.youtube.com/@jengolbeck
• Alex Brey, “Temporal Network Analysis with R,” Programming Historian 7 (2018), https://doi.org/10.46430/phen0080 .

Collaborative Data Week 7 Aristeguieta

NB: This class was claimed by a student who worked with the class to digitize several physical books with the goal of image-cleaning and file-naming