New work, Ergenekon.tc will be exhibited by Deluks on February 18th 2009 Wednesday. Official announcement below:
Deluks 01:
Burak Arikan – Ergenekon.tc
Music: Baris K.
Opening: 18.02.2009 Wednesday 19.00
You can visit the exhibition on Thu – Fri – Sat between 17.00 – 20.00.
“Every society has its diagram(s)”*
Ergenekon.tc shows the diagram of the society depicted in the 2455 page Ergenekon bill of indictment. Ergenekon is an illegal covert network found in Turkey, the lawsuit is in progress. Connections between the actors of Ergenekon including people, institutions, groups, places, theories, ideologies, and beliefs, together form a crack in Turkey. The complexity of this crack can not be explained with a single leader nor with a complete hierarchy. Ergenekon does not have a center, it is a decentralized network. Ergenekon network existed because it was able to diffuse in to hierarchical structures such as government and military organizations.
The Ergenekon.tc project does not look for a meaning in the complexity depicted by the Ergenekon bill of indictment, but signifies its complexity.
Ergenekon.tc combines two computer programs. The first program reads the 2455 page bill of indictment document, filters the nouns, and connects them based on their distance in the text. The second program visualizes this networked structure. In the resulting map, the size of fonts represents the frequency of the names, the relative distance represents relationship weights, dark colored areas show the centers formed by high connectivity.
The inspiration that triggered the Ergenekon.tc project is the digital illiteracy of the way bill of indictment presented by the Turkish court. The bill of indictment was first written in a digital form, then printed, scanned as images, made a PDF file, and distributed in this digitally illiberal form.
Burak Arikan is an artist and researcher who focuses on creating networked systems that evolve with the interactions of people and machines. His work confronts issues ranging from cultural sustainability to micro labor and politics in networked environments. He shows the instances of these systems online and onsite through diverse media including prints, animation, software, electronics, and physical materials. His work has presented and performed internationally at institutions including Venice Biennale, MoMA, Ars Electronica, Neuberger Museum of Art, Sonar Festival, DEMF and at independent venues such as Art Interactive, Turbulence, Upgrade! International, and Hafriyat. He has lectured at institutions including Massachusetts Institute of Technology, Rhode Island School of Design, New York University, Istanbul Technical University, and Istanbul Bilgi University. Arikan is also a contributing author at dugumkume.org. In 2006 Arikan completed his master’s degree at the MIT Media Laboratory in the Physical Language Workshop (PLW) led by John Maeda.
Delüks aims to generate ‘intellectual capital’ by converting the surplus time, place, and labor that fails to yield ‘financial capital’. We suppose that it’s possible to invest this capital in any medium that interests us. Since we are a distributed, un-hierarchical network of individuals from various backgrounds, our set of instruments extend into a suite that includes art, activism, cultural studies, criticism, etc. As demonstrated in ‘Ergenekon.tc’, a Delüks activity might consist of getting involved and providing support for anyone with a fresh perspective we trust.
A network diagram, or a graph, can be represented as text in many ways. We want it to be a structured text so that it can be read by computers. A graph is a set of objects called nodes or vertices connected by links called lines or edges depending on the context (physics, computer science, sociology etc.). The diagrams above are different examples of graphs. A basic representation of links between nodes could be written in this way:
john -> brent
brent -> amber
amber -> john
As you can tell this is a three person social triangle. The widely used graph visualization tool Graphviz uses this syntax, called the DOT language, to represent the basic network data (with semicolons at the end of each line). The DOT language can get quite complex for representing more detailed attributes of graphs.
In our Creative Networking course we started to draw imaginary network diagrams first by hand (see images from earlier workshops), then this week, we will translate them to structured text. We will use an XML file format called GraphML to represent the graph in text. We use this XML structure because it is web friendly, emerged as a standard by many contributions, easy to share, aka the ultimate man-machine readable data format.
GraphML is an easy-to-use XML format for graphs. It consists of a language core to describe the structural properties of a graph. Since it is XML, quite flexible for your application-specific needs. Its main features include support for directed, undirected, hierarchical and mixed graphs, also references to external data. Here is the graph above in basic GraphML:
We use XML-attributes (source=”something”, id=”1″, directed=”true” etc.) to declare graph properties such as directed, undirected, weight, or ID. To store extra data in the nodes and edges we use nested XML. Extra data declarations are called GraphML-attributes, which are defined with the <edge> tag. Also we can declare parser info for optimized parsing. You can learn more about GraphML, see how to, and examples here in GraphML Primer and GraphML Specification.
This week at the Creative Networking class we will study network topologies. That is the study of the arrangement or mapping of the elements (nodes, edges, etc.) of a network. I manually prepared GraphML data for each type of network topology that we will cover in the class. These images below are prepared by the Processing program that I prepared as a template for this class (will post it tomorrow after I clean it up the code). You can also grab the printable PDF version of the diagrams below.
Centralized, Decentralized
Centralized that is all nodes connect to a single node, a hub. It is hierarchical. Single authority. No cross-linking on the periphery nodes. Decentralized network is the multiplication of centralized networks. Many hubs, each with its own dependent nodes.
A distributed network has no central hubs. It is a mesh. Every node is autonomous. Multiple routes to go from one node to the other. A tree is obviously hierarchical, each node has multiple children, but only one parent. No cross-linking between branches.
Core-periphery networks are highly interconnected in the middle and sparse on the edges, few connections from periphery to the core. In a fully connected all the nodes connected to every other node. My friend Ali Miharbi once said that a football team’s 11 members during a match can be considered as a fully connected network.
Social network analyst Stanley Milgram coined the term Small World to describe tight clusters connected to other clusters with a few bridges. Scale-free network as defined by Albert-László Barabási, is the network whose degree distribution follows a power law. In such networks few nodes have large number of connections, some nodes have moderate connectivity, and many nodes have very few connections.
The relationship between form and text in art today somehow inherit in the relationship between data and code. When the data is relational so that it makes a graph, how do we approach it in the context of the arts? This is what we study in the Creative Networking course.
This fall I am teaching a course called Creative Networking at the New York University Interactive Telecommunications Program (ITP). It is an introduction to complex networks within the context of the arts. It focuses on understanding the structure and dynamics of large-scale networks and expanding the individual’s thinking about the network as a creative medium.
The course has 2 phases: Network Structure and Network Dynamics. The first phase focuses on networks as static entities. It is based on the Graph Theory and concerned with the structure. The second phase focuses on the processes taking place in the networks. It is concerned with time, interaction, and multiple characteristics of the network elements.
Twitter connections change over time. We tend to follow more people as we go, but we also remove connections depending our interest and attention span. At least I do. Since I look at the whole network activity from a very thin slice (a list) I prefer to cure my network, I remove some people to be able to keep up with others.
I decided to look at what kind of interest groups emerge as I cure my Twitter social graph. Do my Twitter friends have always growing interconnections? How do people relate? Do I have friends who link together otherwise disconnected communities of interest? Do my Twitter clusters expand or contract over time?
Like the Amazon book network research I did earlier, I was inspired by Valdis Krebs’s network analysis research. Particularly in his recent post, Krebs did an analysis of his Twitter map, where he compared a low-res information visualization work TwitterWheel, with his diagram created in InFlow network analysis software. InFlow diagram is simple but rich, you understand more at a glance. Whereas TwitterWheel stands as a good example of info-viz-kitsch.
I used the graph layout program I developed earlier for the Amazon book network research. But this time no manual data collection, I grabbed all the friends data from the Twitter API. I collected my friends data at three different times to be able to compare the diagrams in time. Click on the images below to enlarge.
My Twitter Graph Week 1
Three weeks ago I was following 80 people. Laying out only the interconnections within the friends (removing my self), we see 6 clusters. By looking at the people I know here, I label them as “MIT”, “silicon valley”, “web programming”, “generative art”, “Istanbul”, and “web business tr (Turkey)”. The silicon valley cluster is large and dense compared to others. The MIT cluster is almost like a clique (every person connected to every other). Generative art is quite close to Silicon Valley, mostly bridged through the user neb. Obviously the Turkish web business cluster has many connections to the Silicon Valley, techcrunch being a major bridge here. The web programming cluster is very small, surprisingly it is connected to Silicon Valley only through the user al3x, who works at Twitter. After this first diagram, I decided to weave more web programmers, generative artists, and people I know from Istanbul. Also I removed neb, al3x, Scobleizer, TechCrunch to have less Silicon Valley weather news.
My Twitter Graph Week 2
Second week, I follow 118 people and the diagram is more hairy. Generative art, web programming, and the MIT clusters are now larger. Istanbul intensifies because of the growing interconnections and it pulls the web business tr after it lost a few Silicon Valley bridges. Now Silicon Valley has a little child called SNA (Social Network Analysis), a separate cluster emerged after I started to follow judithd. Also jeresig between web programming and Silicon Valley, and darita between generative art and Istanbul emerge as major bridges. After the second diagram I decided to weave more people from the field of network analysis, and decided to find more “net art” people.
My Twitter Graph Week 3
Third week, I follow 158 people, more bridges, more dense clusters. The new SNA cluster becomes larger, zephoria being at the center, and stands slightly away from Silicon Valley. A new cluster emerges on the top left, I call it “media/net art”. It is loosely interconnected, and the major magnets weaving the network are manovich, twhid, netwurker, and artfagcity. Media/net art cluster emerged from the extensions of MIT, Silicon Valley, and generative art. Also now between MIT, generative art, and Istanbul there are more bridges, and they are getting closer. toxi keeps his position at the center of the generative art. Interestingly serial_consign emerged as a major bridge between generative art and the new “media/net art” clusters. Silicon Valley and generative art are more away from each other.
Here is a movie showing the force-directed-graph layout program and me interacting to find who is who. Sorry for the crappy compression of Vimeo.
Over the three weeks I intentionally separated strategic bridges to the Silicon Valley cluster. I was curious. The question is why do generative art, SNA, and media/net art clusters are highly pulled by the Silicon Valley? Another question, a meta question is, do these people mind about what these diagrams reveal about their privacy, while all the data is public?
Of course these clusters are my interpretation, they can be more crsytallized, more generalized, or named differently. Networks as static structures do not tell us much and never reflect the reality, however networks as systems evolving over time is quite helpful to understand the dynamics of living systems. Overall these diagrams help me cure my Twitter network better.
Today I am doing a 1 day RSS Ramadan. No feed reading until the sun goes down. I don’t know what it means, but I feel like I need it for the health of my mind and soul. Although some other interfaces may interrupt my information diet, this is no problem since the deal is between me and my “internet god”.
This weak leads to two Creative Networking Workshops, tomorrow (March 19) at the MIT Visual Arts Program (VAP) and Thursday (March 20) at RISD. With Amber Frid-Jimenez we will run the workshops during her course Participatory Networks.
Creative Networking Workshopsfocus on the the design of network protocols as a creative activity and expanding the individual’s thinking about the network medium. Emphasis on network elements, network topology, protocols, and information design. Participants learn the most through observing and creating many examples of networks, sketching diagrams, and authoring protocols. Networked systems in this workshop are not limited to the web or the Internet, but participants are required to design diagrams for running systems; running with animal power, social capital, radio waves or any other model depending on the participants’ concepts.
This workshop is designed based on the experimental work we’ve started to do in the Physical Language Workshop at MIT. We’ve been creating and running experimental infrastructures for the past three years. We extract best practices and best concepts, turn them into recipes and teach them in the workshops and courses. Our goal is to support the development of creative infrastructures, to flourish artist run systems, and to develop critical view on contemporary complex networks.
Jonah Brucker-Cohen just released a new art project: “Google Alert Loop“. It uses Google’s “Blogger” software and “Google Alerts” to create a blog that auto-publishes based on mentions of specific alert topics sent to the email address specified. He says:
The idea is to create a self-perpetuating blog that will publish repeatedly based on the incoming alert feed. The project attempts to question the utility of these automated systems such as “Google Alerts” and how they are being used to aggregate and polarize opinions on the Internet.
I wonder if other blogs can get in this loop by writing about it (such as this post) / using a trackback? Google Alert Loop has an amazing logo!
Update: The site seems down, Google probably didn’t like this. Jonah has a page for the project. Here is what it looked like when I saw it:
Well you’ve heard the story, Google launched the Social Graph API. Google searches and indexes all the social connections on the web and opens it up through an API, so that any web service can ask Google who you are and who you know. Like in Google search, a web crawler reads your web site, finds your public personal information, and stores it in a Google database. More specifically, when you add rel="me" to the links for your accounts in social web services and rel="friend" to the links for your friends’ website, you become a machine readable socialite.
This is definitely an industry cranking step, but it disappointing to see it as a centralized Google API rather than an open distributed system that anyone can host. Here I would like to look at what it means for different actors of the web:
Users – When you sign up to a new social web service, you will get a recommendation: “12 of your friends are also using this service, add them as friends.” No more search for who is in.
Service Providers – Provide more context to the new users, know what other things they use, what else they focus on. Monetize based on their interests.
Google – As the aggregator, store all the social connections between people and capitalize on the massive trust network (see the edge types and 50,000 queries per day limit).
I agree that Social Graph API has more potential for user control and empowerment than in Facebook like closed models, but here I think Google uses brute force. We know that all that public FOAF, XFN data are collectable and servable through an API (I guess mybloglog has been doing it), but it is a huge cost to index and serve all that data. Should we applause this informational brute force achievement or should we focus on open/distributed solutions for the same problem?
For me being exploited is not a big deal, I am against it, I experiment with it, I’ve already disclosed my financials to the whole world with the MYPOCKET project (see what I’ve bought yesterday and what might I buy tomorrow). So I’ve just added a list of MACHINE READABLES on the side bar of this blog to experiment with this new system.
Today I put up the documentation of ARB.This is an experiment for exploring the growth of a dynamic network. I started to work on ARB in the summer of 2006, it is an earlier version of TENSE. A dynamic spring network begins with a few nodes; simplicity at its best. Then I start to add more nodes and connections; it gets more complex. Then I continously drop some of the nodes and add more connections, pushing the network to a chaotic state. While the nodes pushing and pulling each other, strong forces create bright colors, weak forces create dark colors. Images are captured from those chaotic moments.
