Thesis Diary

This blog is a form of digital diary for my second year thesis development process at the Master of Fine Arts - Design and Technology (MFADT) program at Parsons School of Design

Tuesday, August 31, 2004

Conference

I just returned from Aveiro, the northern Portuguese city that held the CNET 2004 International Conference on the â??Science of Complex Networks: from Biology, to the Internet and WWWâ??. Albert Barabasi was the first lecturer and besides being always interesting to put a face and a voice to someone whoâ??s work Iâ??ve been analyzing for so long, most of his lecture was a mere resume of his latest book. However, he did mention, in the last third of his lecture, an interesting approach to the visual study of complex networks. Barabasi has lately been researching, mapping and calculating the number of subgraphs in complex networks and how they relate to each other. By knowing that subgraphs do not exist in isolation and they must aggregate into subgraph clusters, Barabasi has been mapping all possible clusters that are an integral part of the network. These visual Motifs are subgraphs that have significantly higher density in the real network than in the randomized version of it.

In my next entry I will come back to the Motifs subject because it is extremely interesting. Besides Barabasi there were many engaging lectures that reached many topics, from food web networks, to packet switching networks, evolutionary design of functional networks, protein interaction network maps, traffic networks, gene networks, emergence of social beliefs in human networks, communities emergence, molecular motors and predicting epidemics development in small world models. A sentence by Alexander Mikhailov that kept in my mind the all time: â??Network structure determines the performance of the network".

Conference Homepage

Thursday, August 26, 2004

Predators and Trophic Species

Most food webs in nature have proved to have a scale-free network topology where some species have a much larger number of dependencies and interactions than all the others. Because of this inherent structure, food webs suffer from the two main paradigms of scale-free networks:

â?? They are resilient to random failures (in this case to the deletion/extinction of species)
â?? They are sensible to the removal of most connected vertices/nodes (species)

The correlation between network hubs and species in food webs is held mostly by lower trophic species â?? functional groups that contain organisms that appear to eat and be eaten by the exact same species within a food web (Cohen and Briand 1984). These lower level species usually have a high number of links by feeding several species and are normally photosynthesizers or alike. In the opposite side are the predators, higher trophic species on the top of the food chain with a smaller number of interactions.

The next food web models are screenshots from Dr. Joseph Luczkovichâ??s Java Application developed at the Biology Department at East Carolina University. These models are extremely interesting, appealing and functional. One can select the different species and elements to analyze, compare their interactions and zoom extensively in and out of the digitally produced food web. For more information on Dr. Luczkovichâ??s Food Web Visualizations click here.

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Copyright Dr. Joseph Luczkovich. For more information on Dr. Luczkovichâ??s Food Web Visualizations click here.

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Copyright Dr. Joseph Luczkovich. For more information on Dr. Luczkovichâ??s Food Web Visualizations click here.

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Copyright Dr. Joseph Luczkovich. For more information on Dr. Luczkovichâ??s Food Web Visualizations click here.

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Copyright Dr. Joseph Luczkovich. For more information on Dr. Luczkovichâ??s Food Web Visualizations click here.

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Copyright Dr. Joseph Luczkovich. For more information on Dr. Luczkovichâ??s Food Web Visualizations click here.

Pinnacles Food Web Model

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Copyright USGS. This image shows the qualitative food web model for reef fishes of the Pinnacles Reef Tract constructed from stomach content analysis. Position on the food web model is based on relative proportion of pelagic prey (x axis) and trophic position (y axis).

Lake Erie

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Copyright USGS. This image maps the food web of the west basin of Lake Erie as represented in the trophic transfer model.

Monday, August 23, 2004

Cod Food Web

I was again amazed with the infinite ramifications of scale-free networks and the applications of this recent knowledge. Led by a few short citations in the books Iâ??ve been reading, I started researching complex networks in the context of food webs, particularly marine food webs â?? binary feeding relationships between the species in a community. Until recently, biologists, government officials and even environmentalists had a very simplistic view of nature and looked at animal species as a scattered web of nodes with a short number of dependencies. However, most links among components of food webs are not so simple and may involve the interaction of hundreds of organisms.

The importance of fully understanding the dynamics of scale-free networks as been recognized by the cod fishery industry in the worst way. â??The collapse of the Northwest Atlantic cod fishery has become a metaphor for ecological catastrophe and is universally cited as an example of failed management of a natural resourceâ?? (MacKenzie 1995). Peter Meisenheimer in his paper â??Seals, Cod, Ecology and Mythologyâ?? collects an incisive list of six hypotheses that might have led to the demise of the once abundant cod stock:

1. Canadian elected officials and Department of Fisheries and Oceans (DFO) staff have stated that the culling of seals will benefit the recovery of Northwest Atlantic cod stocks.

2. In contrast, published reports in scientific journals, including those authored by DFO biologists, unequivocally conclude that seals are having no demonstrable impact on cod recovery.

3. â??Common senseâ?? arguments that culling seals will â??obviouslyâ?? benefit the fishery are premised on a mythological view of predators that is unsubstantiated by most scientific evidence.

4. Research conducted in other fisheries has indicated that the complexity of marine food webs, and the diversity of seal diets mean increased seal numbers can sometimes lead to positive effects on commercial fish stocks.

5. Consistently, recent research in terrestrial systems indicates that top predators can have a significant positive impact on numbers of herbivores by reducing numbers of smaller predators.

6. The Canadian political agenda for dealing with the collapse of the cod stocks has evolved to include a subsidized seal cull, and suppression of internal reports contradicting the â??common senseâ?? position adopted by the political leadership.

As Meisenheimer says, the use of seals as scapegoats for the failings of Canadian fisheries management is an example of a global problem in the management of fisheries and wildlife. Whether the system is aquatic or terrestrial, tropical or arctic, the predators of the world are seen as problems to be controlled, not as integral parts of a functioning ecosystem. Whenever I think of food webs I instantly recall those simple and infantile diagrams showing the carrot, rabbit and fox. Although this example is intently exaggerated I believe most of us think of a food web of any particular species as an isolated set of interactions, not having more than a few links. Of course we couldnâ??t be more wrong.

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Prof. David Lavigne, a zoolog ist researcher sponsored by the Natural Sciences and Engineering Research Council and the International Marine Management Association is a leading force in combating this miscomprehension of food webs. Regarding the cod stock decrease, he also claims that seals are being used as scapegoats because government scientists are failing to look at the problem in a macro level, the way any network should to be analyzed. The image below is Lavigneâ??s effort to understand the complex map of interactions in a food web. This astonishing work shows the Cod food web displaying some trophic interactions for part of the Northwest Atla ntic.

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Copyright David Lavigne. For a larger version of this image click here.

Wednesday, August 18, 2004

Protect the Hubs

Getting too Big

From a conversation with a friend of mine about the role of hubs in scale-free networks we were intrigued if too large hubs were indeed good for a network since they control and process most of the information flowing through the network. This is particularly alarming in the context of economic networks. Contemporary economic globalization is leading us to a star-topology (1) pattern in our leading economic networks. The hubs of global economy are growing as each new major merger reaches the news. The major hubs are distancing themselves from the following nodes at an ever increasing rhythm. Economist pioneer Vilfedo Paretoâ??s 80/20 Rule is becoming more 10/90 or 5/95 in several market segments, where 5% of the companies control 95% of the market. Examples of mergers like AOL and TimeWarner, Glaxo Wellcome and SmithKline Beecham, or even WorldCom and MCI, remind us daily of the behavior of giants and what a star-topology structure means for the common citizen in terms of economic networks. My interests in this matter are not to discuss the ethical values and consequences of contemporary globalization, they are based in pure scientific curiosity in the study of complex networks. However, if you are interested in this subject, you can find more information here or in the FreePress Organization website.

(1) Star topology network

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I personally believe that thereâ??s a severe danger when hubs congregate an excessive number of links in any network, since the whole structure becomes extremely dependent on them and their sustainability. This problem increases even more if the lack of diversity becomes a common ground among major hubs. Hubs need to maintain themselves closer to their neighbors. Robustness is only achievable with certain precautions. It is known that under random failures/attacks scale-free networks (2) are more stable than randomly distributed networks (3), since the hubs, because of their small number, are rarely disturbed. But under a precise/intentional attack a scale-free network is highly vulnerable due to their high dependence on the vital hubs, which, if destroyed, can easily crash the entire network. Itâ??s somehow the ambiguity of scale-free networks, what makes them stronger is responsible for their main vulnerability.

I know the equilibrium point in this ambiguity is hard to unveil but I also believe is something one should pursue. This leads me to a question that has been bordering my mind lately: In terms of robustness/security can we calculate the right balance between large hubs close r to a star-topology structure and smaller nodes closer to a randomly distributed network structure? In any approach the balance will naturally be somewhere in between, since we know both extremes are inconsistent with a scale-free network. Therefore I believe hubs should never get to a point where the network itself becomes overly dependent on them. Hubs should be closer to their smaller cousins.

(2) Scale-free Network

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(3) Randomly Distributed Network

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(4) If you want to understand more the importance of hubs in scale-free networks, you can read this article about error and attack tolerance in complex systems.


The element of Diversity

Hubs must be diverse in order to maintain network safety (5). Distinct hubs offer different ways of dealing and resisting to problems and difficulties. Diversity is the key element of survival in Nature, is what makes us stronger and fitter. Diversity is the key factor of genetic evolution, is what sells products as synonym of innovation and differentiation. Diversity is a central element of social, cultural and economic networks. This WIRED magazine article offers an interesting point of view about OS monoculture, the opposite condition to genetic diversity in biology. Adding to the articleâ??s argument I will quote Duncan Watts from his book Six Degrees: â??Universal software compatibility clearly confers some significant benefits on individual users. But from the perspective of system vulnerability, when everybody has the same software, everybody also has the same weaknessâ??. There will never be security or robustness in contemporary computer networks unless we reach a balanced level of diversity. Hubs should share the least common number of flaws in order to secure the network stability. Here comes my second element in the equation of Hubs Protection. In any scale-free network, hubs must be protected at all cost, in order to do so we must:

- Maintain Hubs closer to their smaller cousins (factor discussed previously)
- Maintain Hubs as diverse as possible.

(5) Spreading Virus

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Copyright OrgNet. For a larger version of this image click here.

This image demonstrates how the spread of viruses, in a scale-free network, is aided by hubs â?? once a hub gets a virus it can pass it on to a very large num ber of nodes. This particular image is a case of airborne contagion, such as SARS or TB, for more information about this image follow this link.

Sunday, August 15, 2004

Book Network


Copyright OrgNet. For a larger view of the book network follow this here.

During my research I found this interesting example of Information Architecture. What a better way to research complex networks than finding the inherited similarities between books Iâ??ve been reading displayed under a network structure. The web shows several titles Iâ??ve already read and others currently in my wish list. This could be an interesting approach to the people-who-bought-this-book-also-bought-these Amazon style approach to the problem. This solution can obviously be improved but it demonstrates how easy it is to visually grasp the shared links between similar books.

For more visual examples of a particular social network analysis software go to the orgnet.com website.

Tuesday, August 10, 2004

Other Network Approaches

Social Network Analysis â?? 9/11 Terrorist Network

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Copyright OrgNet. For a larger view of the image click here.

For more information about this particular study follow this link.


Biotech Industry Network

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Copyright Douglas White.

â??Biotechnology as a knowledge-based in dustry involves extensive reliance on organizational learning. This occurs through networks of dense collaborative ties among organizations.â?? This model shows the â??emergence of the industry network of contractual collaborations from 1988-99 in relation to both firm-level organizational and financial changes.â??

For more information about this project follow this link.

Citations Network

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For a larger view of the image click here.

This concept was initially led by Sid Redner, from Boston University, who showed that the network of scientific papers, connected by citations, has a power law degree distribution. In a test of this concept papers from the Mathematical Physics Archive were examined for reciprocal citation of the authors, yielding the preceding graph, where colors indicate areas of obvious curvature that were then checked for content. The strongest curvature is the area in red, statistical mechanics, which is reasonable given the nature of the database.


The Worm Brain

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Copyright Eckmann/Moses. For a larger view of the image click here.

Eckmann and Moses used a curvature analysis as a test in known biological systems to explore if it gave meaningful results. In C. elegans worm they plotted the reciprocal connections between neurons in the worm. The height is proportional to curvature. The red nodes are amphid cells, the yellow nodes are other sensory neurons of the head, and blue nodes are motor neurons of the nerve ring. Only co-links are shown, and triangles are enhanced.

Sunday, August 08, 2004

Key Thinkers

This is just a temporary ever-changing list of Key Thinkers Iâ??ve been reading and researching during the last couple of months. Soon I will create an improved list divided by scientific areas and interests.

- Leonhard Euler
- Albert Barabasi
- Steven Johnson
- Duncan Watts
- Stanley Milgram
- Reka Albert
- Hawoong Jeong
- Lee Giles
- Bernardo Huberman
- Mark Granovetter
- Zoltan Dezso
- Malcom Gladwell
- Paul Baran
- Jon Kleinberg
- Jose Mendes
- Lawrence Lessig
- Alessandro Vespignani
- Bill Cheswick
- Elihu Katz
- Zoltan Oltvai
- John Holland
- Richard Dawkins
- John Maynard

Monday, August 02, 2004

Gene Networks

Gene Disruption Networks - Biological meaning of neighbourhoods

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Copyright European Bioinformatics Institute. For a larger version of this image click here.

Neighbourhood of mating response genes. 20 genes in mating response (in red) were selected, and their immediate neighbours in the network work analysed. Many neighbouring genes are related. For more information click here.


Gene Regulatory Network

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Copyright Dr. Martin Stetter

A network structure that represents the dependences between the different genes. "We could think that important nodes (genes) are the ones that have more connections, but it turns out that the most important ones are the ones that have more load." For more information click here.


Computational Biology - Soft Clustering

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Copyright Computational Group. For a larger version of this image click here.

A soft clustering of genes in a subset of the compendium data set for S. cerevisiae of Hughes et.al. 1999. The lines connect genes or experiments that exhibit strong correlations (red more so than black lines). The placement of the points in the plane is chosen to put correlated points close to each other. The coloring of the points expresses their correlation to the selected point (red in the large cluster). For more information click here< /a>.

Sunday, August 01, 2004

Internet Mapping

CAIDA Internet Graph

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Copyright 2003 UC Regents. For a larger version of this image click here.

CAIDA (Cooperative Association for Internet Data Analysis) provides tools and analyses promoting the engineering and maintenance of a robust, scalable global Internet infrastructure. For more information click here.


Physical Structure - MBone Topology

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Copyright Elan Amir. For a larger version of this image click here.

A map of the MBone topology in August 1996. The map was produced by Elan Amir, Computer Science Division, University of California at Berkeley, USA

IP Mapping

The work of Stephen Coast in measuring and mapping the structure and performance of the global Internet. These are two of his early results:

Example
Copyright Stephen Coast. For a larger version of this image click here.

Map of the net, 32,000 nodes. The dense areas are the US upper left, uk lower middle. Countries dotted around. The orange branch top-right is germany etc. The mass of green are .net hosts. For more information click here.

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Copyright Stephen Coast. For a larger version of this image click here.

3-D renders (without electro charge). Radius of sphere proportional to number of edges it has. For more information click here.


The Opte Project

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Copyright Opte Project. For a larger version of this image click here.

The goal of the Opte Project, started by Barrett Lyon, is to use a single computer and single Internet connection to map the location of every single class C network on the Internet. For more information click here.


Internet Mapping Project - Major ISP's

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Copyright Lucent Technologies. For a larger version of this image click here.

The Internet Mapping Project was started at Bel l Labs in the summer of 1998. Its long-term goal is to acquire and save Internet topological data over a long period of time. This data has been used in the study of routing problems and changes, DDoS attacks, and graph theory. For more information click here.


Gnutella Network

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Copyright Martin Dodge. For a larger version of this image click here.

Snapshot of a local gnutella peer network in a particular neighbourhood.


MIDS Map of the Internet World - Data from July 1999

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Copyright John S. Quarterman. For a larger version of this image click her e.

MIDS (Matrix Internet and Directory Services) is a research consultancy of John S. Quarterman, based in Austin, Texas. Quarterman, writer of "The Matrix", published the first maps of the whole Internet, conducted the first Internet Demographic Survey and started the first continuing series of performance data about the entire Internet in 1993. For more information click here.

Transportation Routes

Continental Airlines Air Route

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For a larger version of this image click here.

This is an example to show that most destination maps of airline companies offer an interesting case of a Scale-free network, where major airports play the role of networks hubs due to their large number of links (air connections). These hubs are the most reliable elements for the network robustness and sustainability. Considering an airline route map as a network, where the nodes are the airports and the links are the air connections between them, we can easily understand how it fits the scale-free model by satisfying all its characteristics, such as: growth, preferential attachment (rich get richer), power law distribution and modularity.


Air / Road network structure

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By comparing Indiaâ??s road map and air route map we can easily grasp the structural differences between both networks. These topologies link to Paul Baranâ??s schematic of three possible network structures for the Internet. While Indiaâ??s road map, as most road networks, characterizes a Distributed network model, Indiaâ??s air route exemplifies Baranâ??s Decentralized model or Barabasiâ??s Scale-free network model.