Proposition 1: A Living Archive

The original conversations form part of a larger online network [2], which can be accessed, modified, and transformed by anyone, anytime, anywhere. This way, the original protagonists can always come back to the conversation, reflect on it, extend a certain topic, and elaborate further on a certain subject. This also means that the conversation is open to the others who can join in, read, and build on to the network. As the new nodes are added to the network, all the ones who’s been involved in the conversation receive a notification. This way, the participants do not only keep track of a certain topic, but also constantly expand their knowledge in a collaborative way. In this way, the process is similar to “sustained conversations” of Hans Ulrich Obrist: “the interviews recorded over a period of time, perhaps over the course of many years” [3]. However, here everyone can join in and take part in the conversation, which is not limited to the original participants.
Demo

Proposition 2: Sustained Evolution

One can start rewiring the nodes to produce different meanings and new choices, opening it up to various discourses in order to avoid standstill. For instance, the formation of certain motifs within a network is associated with the emergence of informational pathways: a more interconnected structure, which “makes sense” as a whole [4]. Alternatively, a network that is highly interconnected may come to a “gridlock” and stop producing differences from within. In this case, the possibility to rewire the existing connections or introduce new concepts into the conversation may help it evolve and avoid standstill [5].

Proposition 3: Conversation Profiling

Various tools from text network analysis can be applied to a conversation that is inscribed into a network. After visualizing the network in Gephi [6], we can find out the terms and concepts that function as important junctions for meaning circulation (in terms of network analysis they have high “betweenness centrality”). These are not necessarily the most frequently mentioned terms, but rather the ones without which the network as a whole could not function, the most influential nodes within the network. The communities (indicated with different colors) are comprised of the nodes that are very well interconnected between each other, more so than with the rest of the network. These indicate the concepts and terms that related to each other during the interview. [7]

For instance, here’s the visualizations and the comparison table for two conversations that took place during SI (data derived using Gephi software).





Conversation between Vladimir and Dmitry



Conversation between Lilia and Dmitry

The conversation with Lilia has a low power law distribution: this indicates that the importance is distributed more or less equally between the concepts. Such network is closer to random and random networks are known to synchronize better and harder to propagate information through [5]. They also communicate certain meaning as a whole, rather than several different messages at once [4] [5]. On the contrary, the conversation with Vladimir has a high power law distribution and indicates the network where one or two concepts have much higher significance than the rest (scale-free network). In terms of network analysis such networks may be easier to propagate information and accept new nodes more readily.

The clustering coefficient indicates how embedded the nodes are into their neighborhood. When it is low (as is the case with Vladimir and Dmitry) it indicates a network that has more sparse connections, has more branches on the periphery, and could be more open to receiving new information. In this case, the conversation with Vladimir seems to have more possibilities to evolve, because it is less “finished” than the conversation with Lilia. For instance, in Vladimir and Dmitry’s network we can make new relations between the “agent” and “subject” or between the “gesture” and “affect”.

It would be interesting to see how these structures compare to the other network structures that the person is involved with (for instance, their social network). Their similarity could indicate a certain behavioral pattern.

Proposition 4: Group Profiling

During the SI various individuals came together each with their own ideas, knowledge, and practices. Our interest was to see where the group comes together. What are the main “junctions” where the individuals meet in their practice and interests? What are the different topics they are interested in? In order to find it out, the graphs for each interaction were then put together to form one giant graph representing the terms and subjects relevant for the whole group.




The nodes which play an important role in connecting the network into one entity (higher betweenness centrality) are larger on the image. These “connecting” concepts were often evoked by participants to describe their field of interest or knowledge that usually involved other concepts. These are also 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).

Such analysis of the group’s interests can show where and how it comes together in a way that produces “inessential commonality, a solidarity that in no way concerns an essence” [8]. In other words, where do we come together as a group without producing a specific identity or representation and how specifically it may happen.

Resources

[1] Gilles Deleuze et Claire Pernet, “Dialogues” (Champ Essais, 2008)
[2] ThisIsLike – www.thisislike.com/mindplayer
[3] Hans Ulrich Obrist – www.artbooks.com
[4] Dmitry Paranyushkin, “On Evolution of Meaning Formations” (www.deemeetree.com, 2010)
[5] Dmitry Paranyushkin, “Inclusive Exclusivity” (www.deemeetree.com, 2010)
[6] Gephi – www.gephi.org
[7] Dmitry Paranyuhkin, “Text Network Analysis” (www.deemeetree.com, 2010)
[8] Giorgio Agamben, “The Coming Community” (University of Minnesota Press, 1993)

Summer Intensive residency, produced by Les Ballets C de la B, initiated by Christine de Smedt, co-curated by Myriam van Imschoot –
www.lesballetscdela.be

Photo by Johannes Wengel.

Bibliography

[1] Peter Sloterdijk, Sphären III – Schäume (Suhrkamp, 2004)
[2] Jacques Derrida, Writing and Difference (Routlege, 1978)
[3] Andrey Tarkovsky, Stalker (Mosfilm, 1979)
[4] Genesis 1:1
[5] Solomonoff and Rapoport, Connectivity of Random Nets (Bulletin of Mathematical Biophysics, 13, 1951)
[6] Erdos and Renyi, On the Evolution of Random Graphs (1960)
[7] Newman, Barabasi, Watts, The Structure and Dynamics of Networks (Princeton University Press, 2006)
[8] Max Wertheimer, Gestalt Theory (New York: Gestalt Journal Press, 1997)
[9] Milo et al, Network Motifs: Simple Building Blocks of Complex Networks (Science, 298, 2002)
[10] Aldous and Diaconis, Shuffling Cards and Stopping Times (The American Mathematical Monthly, 93, 1986)
[11] Gilles Deleuze, Difference and Repetition (New York: Continuum, 2009)
[12] Seth Lloyd, Use of Mutual Information to Decrease Entropy (Physical Review, 39-10, 1989)
[13] Alexander D. Bazykin, Non-Linear Dynamics of Interacting Populations (World Scientific Publishing, 1998)

Last image by Justin Palermo, “Covering is Revealing”, 2009.