Welcome to the Visualizing Historical Networks Website. The projects featured here map the way people in the past interacted with each other and their surroundings. This site reflects the idea that the social networks of earlier times were not entirely unlike those of today and it endeavors to model and study them with contemporary tools.

 


 
  • METHOD

    Thinking about past individuals as existing within a densely woven matrix of social relations is hardly new. Latinists, Medievalists, and Byzantinists have long sought to build encyclopedic prosopographies of their subjects (see here, here, and here). These great compilations of relationships were conceived to be like dictionaries: resources that other scholars might use as references of the way people connected to one another, to institutions, and to ideas.

    With the rise of the Internet and the so-called digital age, networks have become a subject of renewed interest across a variety of disciplines. Computer science has contributed greatly to the popular understanding of the networked society. Economists, sociologists, historians, and literary scholars have used networks in their own research, developing methods that have inspired this website and from which we have much to learn.

    In the humanities, network visualization software is becoming an increasingly popular tool.  Applicable to a range of historical sources, network visualization presents a way of conceptualizing relationships and the transmission of ideas in historical communities. Using Gephi, a program developed by a French team in 2008, we depict both classic and recent, ongoing scholarship.

    Gephi has been used by scientists for a host of projects and by historians to map large macro trends. It has, for instance, been used in conjunction with Google's page-rank algorithm to identify the most influential novels of the 19th century. Gephi has also proved a powerful tool for the analysis of smaller, local, personal networks including the Republic of Letters. Our ongoing projects further demonstrate the wide range of applicability of social network visualization not just to very large data sets suited for macro history, but for micro history as well.

    Visualizing or graphing such networks serves two overlapping ends. First, it provides a visible prosopography – a searchable reference of connections far easier to read and to use than any of the classic examples in the field. Though the networks which we are concerned with are not massive, they are still too big to comfortably hold in one's mind all at once. Second, the graphical mapping of networks makes some patterns in the data much more obvious. Such patterns may be already well understood; in these cases, a network map may serve as an ideal teaching device. Yet these patterns may be new or newly discovered. In this way, Gephi, and social network mapping more generally, can prove to be a highly useful research tool.

    This site is designed with these functions in mind. It is meant as a resource and as a tool for further inquiry. However, first and foremost, it is intended to serve as an intellectual springboard. These projects should evoke and provoke. They are examples of a burgeoning field, one which crosses traditional disciplinary lines and offers new inroads into the study of the past.

  • USING THIS SITE

    This site features a series of stand-alone projects. Each project page features background on the project, an explanation on some of the editorial choices, and a set of links to a variety of graph pages. These pages feature the graphs themselves (about which more will be said later), as well as preliminary suggestions on how to interpret the map, explanations of how data was collected and used, and brief statements on historical context. All of this information can be found at the bottom right of the page.

    At the bottom right of the page are the options to download static .pdfs of the graph as well as .csv files of the data itself. The provision of this data is in line with our desire to make the site and any conclusions it draws as transparent as possible. It also allows visitors to replicate our results and to create social maps of their own, perhaps using the datasets we have started compiling as a launching point.

    The graphs on this site are presented in two ways. The large graph visible in the center of the screen is semi-interactive. You can zoom in and out. Clicking on individual nodes will display more information about the individual or entity. Additionally, you can use the search bar at the top of the screen to find particular individuals on the map. Graphs are also available for download in .pdf format. Though not searchable, these downloadable images provide more information on the specific nature of the connections (or edges) between nodes.

  • TECHNICAL CONSIDERATIONS

    Every graph built in Gephi consists of two elements: nodes and edges. Nodes represent individuals or other entities like organizations or places. Edges are the connections between nodes.

    From a visual standpoint, nodes can be differentiated from one another in a total of four ways. They can be of varying color, of varying size, of varying location, and they can have attached to them a textual label. Nodes can be ranked or partitioned – and this ranking or partitioning can be demonstrated either through color or size.

    Like nodes, edges can be differentiated from one another (visually) in four ways. They can be of varying color and of varying "weight," that is, thickness (a close connection would be represented by a weighty edge). Edges are also either directed or undirected, meaning that they either move from a source node to a target node or that they do not move unidirectionally. Directed edges are useful for showing how ideas diffuse or for designating generational lines. Edges also can bear a textual label.

    Edges and nodes can be arranged manually or by running certain algorithms which position nodes in relation to each other based on their importance to the overall data set, the strength (weight) of the edges between them and other factors.

    Additionally, Gephi allows the creation of dynamic graphs. Gephi can animate the evolution of a social network, assuming each edge and/or node is designated with a "start point" and "end point."

    One significant shortcoming of Gephi is that it does not allow multiple edges to connect the same two nodes. A result of this is that though individuals may be connected to each other in a host of ways, in a host of contexts, only one single link can be drawn between them directly. This means that it is very difficult to demonstrate nuance in the relationships presented in a Gephi graph. To present nuance, a variety of graphs must be produced.

    Gephi lends itself very well to highlighting a single genre of connection at a time. In labeling and weighting our edges, we have endeavored to differentiate according to non-conflicting criteria. We have generated many graphs for many different kinds of connections, not one that tries to aggregate and capture the multiplicity of network connections.

  • MORE INFORMATION ON HOW TO USE GEPHI

    Those new to Gephi might consider reviewing the online tutorials. These provide a brief introduction to the program's capabilities. Information about importing geographic coordinate data can be found here.

    Our own data is available on .csv files and can be imported to Gephi for use in your own explorations. Instructions on importing can be found here. In the downloadable dataset, neither nodes nor edges have ID numbers. We recommend adding them. To do this,

    • 1. Create a new column in the "nodes" file labeled ID. Fill the first three cells in this column with numbers 1, 2, and 3 respectively.
      2. Highlighting all three cells, use the fill handle (the little square at the bottom right of the selection) to fill the rest of the column.
      3. Now, in the "edges" file, create a new column labeled "Source" and one labeled "Target."
      4. Use the "vlookup" function to fill these columns with the corresponding node ID numbers. A more in depth tutorial of VLOOKUP can be found here.