Summer Intensive

SI. is the initiative by Christine De Smedt co-curated by Myriam Van Imschoot and produced by Les Ballets C de la B to host artists from various backgrounds to meet for exchange and develop (a part of) a project in a brief amount of time.

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— @summerintensive on Twitter.

Tagged network:

Based on the interviews with Summer Intensive participants made by Dmitry Paranyushkin with ThisIsLike the visualizations above were created in Gephi. They represent the field of interests, concerns, and research for each participant that came up during the interviews.

The terms / concepts that are bigger within the network are sort of “junctures” through which most of the other concepts are realized, sort of important passageways for the meaning (in terms of network analysis they have high “betweenness centrality”). These are not necessarily the most frequently mentioned ones, 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 the color of the nodes) are comprised of the nodes that are very well interconnected between each other, more so than with the rest of the network. 

The table at the bottom gives an insight about some main parameters of each participant’s network of interests. Those that have low power law distribution are the ones where the importance is distributed more or less equally between the concepts. While the ones with the high power law distribution indicate the networks where one or two concepts have much higher significance than the rest. The clustering coefficient indicates how embedded the nodes are into their neighborhood. When it is low it indicates a network that has more sparse connections, has more branches on the periphery, and could be more open to learning.

To see the network of the whole group of artists from Summer Intensive click here.
Aug 28

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).

Navigate the network in real time
Aug 26
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). Navigate the network in real time

Summer Intensive

Posted on Saturday August 28th 2010 at 05:55pm. Its tags are listed below.

Based on the interviews with Summer Intensive participants made by Dmitry Paranyushkin with ThisIsLike the visualizations above were created in Gephi. They represent the field of interests, concerns, and research for each participant that came up during the interviews.

The terms / concepts that are bigger within the network are sort of “junctures” through which most of the other concepts are realized, sort of important passageways for the meaning (in terms of network analysis they have high “betweenness centrality”). These are not necessarily the most frequently mentioned ones, 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 the color of the nodes) are comprised of the nodes that are very well interconnected between each other, more so than with the rest of the network. 

The table at the bottom gives an insight about some main parameters of each participant’s network of interests. Those that have low power law distribution are the ones where the importance is distributed more or less equally between the concepts. While the ones with the high power law distribution indicate the networks where one or two concepts have much higher significance than the rest. The clustering coefficient indicates how embedded the nodes are into their neighborhood. When it is low it indicates a network that has more sparse connections, has more branches on the periphery, and could be more open to learning.

To see the network of the whole group of artists from Summer Intensive click here.

Summer Intensive

Posted on Thursday August 26th 2010 at 07:14pm. Its tags are listed below.

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). Navigate the network in real time
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). Navigate the network in real time

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).

Navigate the network in real time

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