Name,Historical Significance,Gender,Birthdate,Deathdate,ID Each Quaker node also has a number of associated attributes including historical significance, gender, birth/death dates, and SDFB ID-a unique numerical identifier that will enable you to cross-reference nodes in this dataset with the original Six Degrees of Francis Bacon dataset, if desired. When you open the node file in the program of your choice, you will see that each Quaker is primarily identified by their name. For more on the general structure of network datasets, see this tutorial. It will be extremely helpful to familiarize yourself with the structure of the dataset before continuing.
To download these files, simply right-click on the links and select “Save Link As…”. The file quakers_nodelist.csv is a list of early modern Quakers (nodes) and the file quakers_edgelist.csv is a list of relationships between those Quakers (edges).
Data Prep and NetworkX Installationīefore beginning this tutorial, you will need to download two files that together constitute our network dataset. This dataset is derived from the Oxford Dictionary of National Biography and from the ongoing work of the Six Degrees of Francis Bacon project, which is reconstructing the social networks of early modern Britain (1500-1700). Since scholars have long linked Quakers’ growth and endurance to the effectiveness of their networks, the data used in this tutorial is a list of names and relationships among the earliest seventeenth-century Quakers. Quakers’ numbers grew rapidly in the mid- to late-seventeenth century and their members spread through the British Isles, Europe, and the New World colonies-especially Pennsylvania, founded by Quaker leader William Penn and the home of your four authors. Founded in England in the mid-seventeenth century, the Quakers were Protestant Christians who dissented from the official Church of England and promoted broad religious toleration, preferring Christians’ supposed “inner light” and consciences to state-enforced orthodoxy.
What are the subgroups and communities in the network?īefore there were Facebook friends, there was the Society of Friends, known as the Quakers. Who are the important people, or hubs, in the network?. What is the overall structure of the network?. This tutorial will help you answer questions such as: Insofar as even the most perceptive of scholars has difficulty perceiving, say, the overall shape of a network (its network “topology”) and identifying the nodes most significant for connecting groups, quantitative network analysis offers scholars a way to move relatively fluidly between the large scale social object (the “graph”) and the minute particularities of people and social ties. Factors such as their structural relation to further people and whether those additional people were themselves connected to one another have decisive influence on events. As sociologist Mark Granovetter pointed out in his important 1973 article “ The Strength of Weak Ties,” it’s rarely enough to notice that two people were connected with one another. Networks have long interested researchers in the humanities, but many recent scholars have progressed from a largely qualitative and metaphoric interest in links and connections to a more formal suite of quantitative tools for studying mediators, hubs (important nodes), and inter-connected structures. Check out the Programming Historian tutorials on installing Python and working with pip for more information. For this reason, when accessing Python 3 you will often have to explicitly declare it by typing python3 and pip3 instead of simply python and pip. It’s possible to have two versions of Python (2 and 3) installed on your computer at one time. Installed Python 3, not the Python 2 that is installed natively in Unix-based operating systems such as Macs (If you need assistance installing Python 3, check out the Hitchhiker’s Guide to Python) and. a basic familiarity with networks and/or have read “From Hermeneutics to Data to Networks: Data Extraction and Network Visualization of Historical Sources” by Martin Düring here on Programming Historian. You’ll likely want a combination of visualization and network metrics in your own project, and so we recommend this article as a companion to this earlier Programming Historian tutorial. We will therefore focus on ways to analyze, and draw conclusions from, networks without visualizing them. 
N.b.: This is a tutorial for exploring network statistics and metrics.
To analyze humanities network data to find:. To use the NetworkX package for working with network data in Python and. Advanced NetworkX: Community detection with modularity. What might you learn from network data?.
0 kommentar(er)
