For this lab, I chose to conceptually analyze, in terms of relations between characters, the first scene of Shakespeare’s famous play _Hamlet. _The table I created contains five nodes and 24 edges. The nodes are labeled by the character names that appear in the scene (Bernardo, Francisco, Horatio, Marcellus, and “Ghost”) and assigned an identification number, one through five, for the order their names appear in the scene. In addition, I assigned each character an attribute number to indicate whether the character is an officer/watchman in the play (number one) or an non-officer/watchman (number two). For the edges, I recorded the interactions between the characters, specifically showing who spoke, and whom they spoke to, throughout the scene. Finally, I drew a graph with five circles for each of the five characters, and an arrow or arrows pointing to another or other character(s) to denote the edges, or character interactions.

I found network analysis to be particularly interesting for the snippet of literature I selected. The first scene of _Hamlet _doesn’t involve a lot of physical activity or action, but sets the basis of the plot more through the words of the characters. During the scene, Bernardo and Francisco, two officers/watchmen of a castle in Denmark, guard the castle while attempting to convince Horatio, a friend of Prince Hamlet, that they, on multiple occasions, have seen the ghost of King Hamlet. Because the scene is more centered on words than actions, I believe my table and graph could help readers better understand the importance of the characters in the first scene. For example, one can see that Horatio talks six times while being talked to nine times, and Marcellus also talks six times but is only talked to seven times. This type of information, and the format in which the information is displayed, allows one to easily understand which characters are most integral to a particular work based on their activity in the studied material.

The advantage of network analysis is that concrete information about characters and their dialogue or other relational aspects can be easily derived. There are challenges associated with this type of analysis, however. For example, electing to study relational aspects of characters might cause other attributes of the characters to get overlooked.

Network analysis like the one we conducted for this lab, but on a larger and more intensive scale, can really aid literary studies. For instance, if a literary theorist wanted to examine how the protagonists of different works relate to the communities around them in the works, and compare the results of his study against the claims of other theorists about protagonists, social network analysis might be an efficient and effective method for accomplishing that.