December 25th, 2008. I went to Istanbul for doing a 3 day workshop on networked information visualization, which was kindly supported by Istanbul Bilgi University. Worked with a diverse group of participants from various schools with backgrounds from visual arts to computer science. We covered basic network structure, network topologies, and clustering. We did hand drawn diagrams. Collected data by hand and by programmatic methods. Visualized relations using templates. As the final project, we worked on the database of the national elections in Turkey from 1960 to 2007. Turkey generally has 10 to 20 political parties per election. We looked at how the same representatives elected from different parties at each election. Above image shows the network of all the political parties between 1961 and 2007 (some parties do not exist today). The diagram is created by Mumin Aydin. Line thickness shows the amount of transfers between the parties. I didn’t know that politicians can change their ideology this much. Participants created network of political parties as well as cities connected by representatives. We concluded the workshop with a mini exhibition on the corridor, which was ironically overlapping with a conference titled Marxism 2008. Full documentation will be up sooner than later.
December 27th, 2008. Did a performance at the Gozel Geceler party, which was a technical fiasco. After the disastrous NYC Minitek Festival, this was the second time I had to go on the stage without sound / image check. Never recommended.
January 6th, 2009. New year’s first lecture at Bogazici University Complex Systems Research Lab. First, presented the principles for what I do, how I use the network structures and dynamics in my thinking and the network itself as the medium in my practice. Second, showed examples of work from 2005 to 2008. Discussed large scale networks and creative processes with Chris Stephenson, Haluk Bingol, Suzan Uskudarli, Onur Gungor and students from the Bogazici University.
January 28th, 2009.Basak Senova kindly invited me to do a presentation at the Upgrade! Istanbul meeting. I organized the talk around the criticism of micro labor in social network services. Started with a recipe on how to create a social network service. Followed with the measurability of the contemporary social environment, and discussed how even physical activities can easily be measured and are part of the digital cloud. Showed instances from the MYPOCKET project. Described the relations between a platform owner, an application developer, and a user in terms of the social web services. Showed instances from the Meta-Markets project.
January 28th, 2009. After the Upgrade! Istanbul meeting did a Meta-Control performance with Klaustro’s music at Peyote. A fascinating Istanbul night of electronic music and live computer visuals. Video above by Devrim Kadirbeyoglu.
February 05th, 2008. Did a 2 day Networked Information Visualization Workshop in Kayitdisi Events at Yildiz Technical University, Istanbul. Participants studied the structure of networks and did basic network visualizations based on hand picked data.
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.
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.
This description connects the books at Amazon.com. For each book, Amazon shows other books which are also bought by the customers who bought the book you are looking at. It sounds like a word game, in fact this is a simple description of a consumption network.
After reading The Social Life of Book, a visual analysis of the Amazon book network by the SNA researcher Valdis Krebs, I decided to do look at the surrounding network of the books that I am interested in.
I did the analysis on the two books that had big impact on me: “Design By Numbers” by John Maeda and more recently “The Shock Doctrine” by Naomi Klein. I started to trace the network out one and two steps from the focus book. As Krebs asked in his analysis, I was interested in the questions “How do these books relate?”, “What themes would I see in the surrounding network?”, “Will the book end up in the center of one massively interconnected cluster?”, “Will it end up linking together otherwise disconnected communities of interest?”
After manually collecting the data from Amazon, I added them into an XML structure and then in Processing wrote a basic force-directed graph algorithm to layout the network. Nodes pull or push in relation to each other, highly connected ones are stronger. I added some features like incoming connection count, which affects the pulling force (circle size represents the number of incoming connections). Click on the images below to enlarge.
“Design By Numbers” graph
In this graph we see clusters with themes ranging from computational design to sustainability to contemporary art to network culture. Design By Numbers is at the center of highly interconnected cluster of computational design, which also includes books such as Designing Interactions (has the most incoming connections), Processing Handbook, Visualizing Data, Physical Computing, and John’s recent book The Laws of Simplicity (the second highly connected). In the periphery of this cluster we see Everyware and Shaping Things, which act as bridging ties to other clusters. Everyware is bridging both the network theory and the sustainability cluster to computational design. Bruce Sterling’s Shaping Things, not suprisignly, is in between computational design and sustainability. The sustainibility cluster is of course very close to the architecture cluster, in fact the bridging tie is Bruce Mau’s Massive Change. As you may know Bruce Mau collaborated with architect Rem Koolhaas in many projects including the rock solid SMLXL book. Koolhaas’s other collaborator is the building structure genius Cecil Balmond, who is the author of Informal. Well you can keep following these relationships forever. From the computational design and the architecture clusters, it is visible, and probably quite natural, that readers follow the professional and academic relationships between authors.
The recent MoMA Design and the Elastic Mind exhibition catalogue includes projects both in the field of computational design and architecture. Containing some Olafur Eliasson catalogues (names invisible), this group makes a little science-art cluster; and connects to Clay Shirky’s new book Here Comes Everybody in the network culture cluster. Here the central topic is complex networks, however the books are about different aspects of this complexity. We have books talking about the architecture of the networked society, economics, business, cultural and political critique. Interestingly between the network theory cluster and the computational design cluster there is an ironic book called Presentation Zen (name not visible, the one that connects Shirky’s book to Visualizing Data and Processing Handbook).
Contemporary art cluster at the bottom left has many artist and art theory books. The book Participation connects to The Exploit, Galloway and Thacker’s new book in the network theory. Together with Alex Galloway’s other book Protocol they are actually pulled by art theory. If we go deeper in the data collection we can probably see these two books getting more closer to the art theory cluster.
A few structural holes are interesting here. There seems to be no connection between contemporary art and computational design, at least in this collection. Any books you know that can connect these two clusters? Also there seems to be a structural hole between sustainability and science-art clusters. Don’t we have art about sustainability, sustainability art, or maybe “sustainable art”? MoMa exhibition seems like connecting architecture and network theory, but it is more than a few steps. I’d like to know more about possible weavers between architecture and network theory.
“The Shock Doctrine” graph
This graph ended up in two major clusters with themes globalization/finance and political/economic critique. The Shock Doctrine is in the larger cluster on the right. It has the most incoming connections followed by The End of America, The Conscience of a Liberal, and Broken Government. Together they make the center of this cluster, that is critical analysis. Whereas on the periphery, there are books that sound more like conspiracy theories (haven’t read though).
Globalization / finance cluster mostly contains neoliberal books as you can tell from the names. I started to read the recent best seller Fareed Zakaria’s The Post-American World and George Soros’s The New Paradigm of Financial Markets. These two books have more shared readers than the others in this group.
These two clusters are representing different political views, we can probably say neoliberals and leftists; and what bridges them is another ironic book called Bad Money…
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.
“Angling in Troubled Waters” map by cartographer Fred W. Rose shows the European geopolitical situation in 1899. If you look at the details (see the larger version) you will see some countries such as Russia, UK, and Ottoman Empire are fishing. What do they catch? Blogger Catholicgauze says:
“…their ‘catches’ are in fact colonial possessions.”
This map, found via Boran Güney, is a neat example of the micro macro visualization technique. You can read through human figures and symbols across national borders.
What about today?
Architect Rem Koolhaas and his office OMA/AMO make dadaist montages of todays sociopolitical scenes on timelines and maps and they align them with architectural and cultural products. OMA/AMO’s 2004 book Content has an excellent article, “An Autopsy“, which is an annotated spatial montage of events and figures on a timeline from 1989 to 2003. Here are some pictures from An Autopsy article (taken with my cellphone camera):
An Autopsy is authored by Theo Deutinger, Maja Borchers, Matthew Murphy, Nanne de Ru, Max Schwitalla, Sebastian Thomas.
