Networks and Ethnogenesis 101idea because it comes into contact with so many others; or if subject to attack, then
the destruction of the hub is central to the disintegration of an entire system. “Such
a specific topology has direct implications for the processes underlying it, like the trans-
portation of materials, the spread of religious ideas or the enforcement of political power.
These processes would largely take place between the highly connected nodes, and they
would only reach the larger number of less connected nodes through these vertices”
(Brughmans 2010: 4).
In archaeological application, Bentley discovered a scale-free network and a power law
configuration in the length of long barrows in Neolithic Europe and southern England.
When he plotted the lengths of the barrows as a mathematical graph, they followed a
“normal distribution” (i.e., they formed a bell curve, indicating that most barrows clus-
tered around a mean average length)—however, the curve had a characteristic tail of
a power law, meaning that a small number of the barrows were much larger than the
average, which Bentley used to identify the emergence of hierarchy. He argues that this
shows that “over time, the rich seem to become richer with these Neolithic long barrows,
which suggest that status in Neolithic Wessex accumulated within a scale-free network”
(Bentley and Maschner 2003: 41). Sindbæk also used the framework in his examination
of the hierarchy of towns and villages in early Viking Scandinavia. In his data, he saw a
few towns that participated in long-distance trade, with the majority of villages operat-
ing at a localized market level. This he characterized as a scale-free network (Sindbæk
2007)—which places emphasis on the importance of hub nodes and geographical hier-
archy in the network; however, Brughmans has argued that Sindbæk’s data could also
be considered a small world (Brughmans 2010: 3), which would emphasize the weak
ties in an otherwise fairly egalitarian network. The point that Brughmans makes is that,
although the network metaphor is useful and often revealing, caution must be exercised
with the models that are adopted, and we must remain critical of our data and the results
we observe.
Centrality, Texts, and Relational Space
Networks have been used to calculate centrality.Closenessandbetweennesscentrality are
defined, respectively, as the “ease with which a node can reach, or be reached by, any other
node on the network. It is an index of how easily accessible a node is to all the other nodes
in the network” and as “the probability that a node will be passed by traffic traveling along
the shortest route between two other nodes on the network. The index indicates, not
how easy it is to reach other nodes, but the likelihood of it being en route when taking the
shortest path between other vectors” (Isaksen 2008: 10). High betweenness centrality
indicates a bottleneck or focal point in a system, a node which may exert control over the
network; whereas high closeness centrality means that node will be connected by short
paths to many other nodes—making them very important in the spread of information,
and possibly hubs.