The network visualization of Summer Intensive participants interviews by Dmitry Paranyushkin.
During the interviews the key concepts, terms, and names were notated using ThisIsLike.Com and related to one another. After that the data was exported from ThisIsLike into Gephi and the resulting network was visualized.
The nodes which play an important role in connecting the network into one entity (higher betweenness centrality) are larger on the image. These are not necessarily the most frequently mentioned terms, but more the ones without which the network would not be able to exist and be one connected entity. In other words, these “connecting” concepts were often evoked by participants to describe their field of interest or knowledge that usually involved other concepts. Also, these are the terms which connect different fields of interests to one another, sort of “points of encounter” which have the most potential for the production of activity within the group.
Following this logic, “Image”, “Object” and “Reenactment” are the most important terms in bringing the network together. Also “Pieter Ampe” (because he’s introducing important peripheral information into the network) as well as “collaboration”, ”dramaturgy”, “counterpoint”, “subjectless subjectivity”, “real-time improvisation”, and “space”.
In contrast, the most frequently mentioned terms in the interviews were “performance”, and “image”.
Different “communities” of terms are shown in distinct colors, based on their interconnection. Those terms which are closely related to one another (within the context of the interviews) have the same color. The most prominent community is comprised of “space”, “performance”, “affect”, “network” and “diagram”. The second most prominent community is comprised of “object”, “subject”, “body”, “agency” and “sound”.
The network also has high power law distribution (4.717). This points to the fact that it has a few very well connected (frequently mentioned) terms and that the interest is unequally distributed among the terms (in other words, a few terms have significant “power” in the network). At the same time the clustering coefficient is not too high (0.158), the density is low (0.021) and the diameter is quite high (the maximum distance of travel from one node to another is 10). This indicates that the network is generally quite receptive to new information and the average number of steps that need to be taken to reach any concept from any starting point is 4.341 (so information readily propagates within the network, but takes time to assimilate).
