In my last post, I discussed the processes involved in constructing a social network. This entry will build on this by looking at how I analysed the network. I will firstly introduce some of the methods used to do this, before discussing what they reveal about social relations in Ostrogothic Italy. Most of what will be discussed here ended up forming the basis of my the first chapter of my dissertation and so this entry will give some insight into how I came to my conclusions.
The software used for analysing the network was Gephi, which aids Social Network Analysis (SNA) in two ways. Firstly, by visualising the network, allowing the researcher to identify patterns. An image of part of my network, as visualised in Gephi, can be seen below. To aid my investigations for the first chapter, I colour-coded the different nodes based on their 'ethnic' label. Red nodes were Roman, whereas purple ones were Gothic. Likewise, blue nodes represented individuals that are classified as 'Unknown' and green ones represent individuals classified under 'Other', such as Visigoths or Franks. In this image, lines between people mean they are connected at least once. A visual representation such as this was useful, as it allowed me to analyse the network on a smaller scale. For example, by looking at a representation, I was able to identify a clique based around the Roman Senate going against the norm of the network established by metrical means.
Part of the network, colour-coded for my analysis.
I will now move on to the aforementioned ability to use metrics in Gephi. Social Network Analysis often involves using a range of formula which help the researcher to identify patterns. These can be calculated for the network as a whole. For example, global density helps to identify how complete or connected the entirety of the network is. This is calculated by expressing the number of lines in the network as a proportion of the maximum number of lines. It was therefore useful for identifying the divided nature of my network. The average clustering coefficient is another metric calculated globally and is used to understand the tendency of individuals to form into clusters within the network.
There are also a number of metrics that assign scores to individuals, rather than on a global-level. Closeness centrality calculates the shortest route through which a node tends to be connected to other nodes in the network, whether this is directly or through an intermediary. Eigenvector centrality is similar to this, except it is based on how far a node is connected to the most central nodes. Metrics such as these can be useful for finding out who the most important people in the network are. However, they are also useful for finding out averages for different node types. For example, it was possible to work out that Goths and Romans were generally equal in importance in terms of their place in the network. As I used a range of technical vocabulary, like the ones described here, throughout the first chapter, I decided to restrict definitions to the appendices of my dissertation. This is to prevent detraction from my argument in the main body of the text.
I will now discuss some of my findings from applying these forms of analyses for the first chapter of my dissertation. The first and most significant finding was that connections between individuals did not tend to be made in the context of ethnicity. Most modern scholarship accepts that ethnicity does not have an unchanging biological basis. However, historians have still tended to emphasise the importance of ethnicity, even it its ideological or constructed form, when understanding Ostrogothic society. My research has questioned this assumption in multiple ways. Firstly, as mentioned, average centrality scores for individuals in the network tended to be the same for Goths and Romans. They were unable to be distinguished in this way. Secondly, based on a visual analysis of my network I noticed that while nodes did tend to coalesce into groups, there was no basis to use ethnicity as an explanation for this. Nodes of different ethnic labels tended to connect with each other more often than not. The final statistic that supported this point were the percentages at which Goths and Romans connected with other 'ethnic' labels. Based on a 20% sample of all Goths in the network, I found out that Goths tended to connect more with Romans rather than their fellow Goths.
The second finding of my research was that the military/civilian divide that has characterised discussion around Ostrogothic Italy is not supported by a statistical analysis. Goths have often been seen as 'soldiers' and Romans as 'civilians'. Nearly every single title within my study was either civilian or straggled the apparent divide. For example, many titles such as Dukes, had civilian and military duties. However, some titles did seem to be associated with particular 'ethnic' labels. For example the 17 Sajones in the data were all Gothic. Sajones were experienced soldiers, who usually held judicial roles. However, this was not a suitable foundation to argue for a military/civilian divide, the holders of this title still tended to associate with Romans more than Goth, in spite of being Gothic themselves.
This entry has established the different methodologies I used to carry out an analysis of social relations in Ostrogothic Italy and pointed towards how I came to the conclusions established in the first chapter of my dissertation. After dismissing the traditional ethnic interpretations, the rest of the analyses of my network will focus on establishing a new model for understanding society in early sixth-century Italy.
Secondary Sources:
The software used for analysing the network was Gephi, which aids Social Network Analysis (SNA) in two ways. Firstly, by visualising the network, allowing the researcher to identify patterns. An image of part of my network, as visualised in Gephi, can be seen below. To aid my investigations for the first chapter, I colour-coded the different nodes based on their 'ethnic' label. Red nodes were Roman, whereas purple ones were Gothic. Likewise, blue nodes represented individuals that are classified as 'Unknown' and green ones represent individuals classified under 'Other', such as Visigoths or Franks. In this image, lines between people mean they are connected at least once. A visual representation such as this was useful, as it allowed me to analyse the network on a smaller scale. For example, by looking at a representation, I was able to identify a clique based around the Roman Senate going against the norm of the network established by metrical means.
I will now move on to the aforementioned ability to use metrics in Gephi. Social Network Analysis often involves using a range of formula which help the researcher to identify patterns. These can be calculated for the network as a whole. For example, global density helps to identify how complete or connected the entirety of the network is. This is calculated by expressing the number of lines in the network as a proportion of the maximum number of lines. It was therefore useful for identifying the divided nature of my network. The average clustering coefficient is another metric calculated globally and is used to understand the tendency of individuals to form into clusters within the network.
There are also a number of metrics that assign scores to individuals, rather than on a global-level. Closeness centrality calculates the shortest route through which a node tends to be connected to other nodes in the network, whether this is directly or through an intermediary. Eigenvector centrality is similar to this, except it is based on how far a node is connected to the most central nodes. Metrics such as these can be useful for finding out who the most important people in the network are. However, they are also useful for finding out averages for different node types. For example, it was possible to work out that Goths and Romans were generally equal in importance in terms of their place in the network. As I used a range of technical vocabulary, like the ones described here, throughout the first chapter, I decided to restrict definitions to the appendices of my dissertation. This is to prevent detraction from my argument in the main body of the text.
I will now discuss some of my findings from applying these forms of analyses for the first chapter of my dissertation. The first and most significant finding was that connections between individuals did not tend to be made in the context of ethnicity. Most modern scholarship accepts that ethnicity does not have an unchanging biological basis. However, historians have still tended to emphasise the importance of ethnicity, even it its ideological or constructed form, when understanding Ostrogothic society. My research has questioned this assumption in multiple ways. Firstly, as mentioned, average centrality scores for individuals in the network tended to be the same for Goths and Romans. They were unable to be distinguished in this way. Secondly, based on a visual analysis of my network I noticed that while nodes did tend to coalesce into groups, there was no basis to use ethnicity as an explanation for this. Nodes of different ethnic labels tended to connect with each other more often than not. The final statistic that supported this point were the percentages at which Goths and Romans connected with other 'ethnic' labels. Based on a 20% sample of all Goths in the network, I found out that Goths tended to connect more with Romans rather than their fellow Goths.
The second finding of my research was that the military/civilian divide that has characterised discussion around Ostrogothic Italy is not supported by a statistical analysis. Goths have often been seen as 'soldiers' and Romans as 'civilians'. Nearly every single title within my study was either civilian or straggled the apparent divide. For example, many titles such as Dukes, had civilian and military duties. However, some titles did seem to be associated with particular 'ethnic' labels. For example the 17 Sajones in the data were all Gothic. Sajones were experienced soldiers, who usually held judicial roles. However, this was not a suitable foundation to argue for a military/civilian divide, the holders of this title still tended to associate with Romans more than Goth, in spite of being Gothic themselves.
This entry has established the different methodologies I used to carry out an analysis of social relations in Ostrogothic Italy and pointed towards how I came to the conclusions established in the first chapter of my dissertation. After dismissing the traditional ethnic interpretations, the rest of the analyses of my network will focus on establishing a new model for understanding society in early sixth-century Italy.
Secondary Sources:
Amory, Patrick. People and Identity in Ostrogothic Italy, 489-554. Cambridge; New York: Cambridge University Press, 1997.
Heather, Peter. "Merely an Ideology? Gothic Identity in Ostrogothic Italy." In The Ostrogoths from the Migration Period to the Sixth Century: An Ethnographic Perspective, edited by Sam Barnish and Federico Marazzi, 31-80. Woodbridge, Suffolk: Boydell Press, 2007.
Knoke, David and Song Yang. Social Network Analysis. Thousand Oaks: SAGE Publications, 2000.
Scott, John. Social Network Analysis: A Handbook. 2017 ed. Thousand Oaks, California: SAGE Publications, 1991.
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