Intro to Networks

When looking at only 1734, Mary Butler seems to be well-connected with other nodes. Her connections grow stronger in 1735.  Once the data for 1740 is included, Mary remains the largest node, therefore the most connected, but the diagram grows increasingly complex. Once the data for every available year is included, Mary Butler is still the largest node.

When we observed the difference between the diagrams when nodes were sized based on betweenness centrality versus degree centrality, we noticed that, in both versions, Anne Clements still was represented by one of the largest nodes. However, the other large nodes represented different people in each version. When all data but that for 1734 is filtered out, William Sixbury seems like something of a central node in the diagram. However, his connections appear less prominent than those of John Wemp once the data for 1735 is introduced. By 1739, William Sixbury has re-entered the arena as one of the largest nodes. Once all the data up to 1745 is included, though, there is no longer an easily distinguished “center” of the group. The more data that is introduced, the more complex and varied the relationships become.

Clustering makes the connections shown on the diagram much more readable. Just changing the node size let us see some differentiation between the interconnectedness of the nodes, but it wasn’t quite as useful for interpreting the data. When we changed the node size based on centrality betweenness, the size of nodes that created the shortest paths between others was increased. By clustering the nodes, we are able to see the larger connections between nodes that span across the diagram. Nodes that were seemingly unimportant when considering centrality betweenness are now identified as major players. For example, the cluster of Aron Oseragighte comprises 14% of the total nodes, but we didn’t even notice his node before. Thus, the way data is organized in a diagram of this type makes an enormous impact on how the data will be interpreted. 

by Merissa Marthage and Clara Meyer

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