How do some  webpages on the Internet become so ubiquitous that we are rarely ever less than two short links away from viewing that page? How have some webpages become so popular among the hundreds of billions of webpages that are on the Internet currently, not to mention the new ones that pop up almost every second? More importantly how do certain pages become the centre for web activity and lead us to other pages within their network? Albert-Lázló Barabási explored these questions about networks and the organization/disorganization of interconnectivity within his Chapter titled Hubs and Connectors .

Barabási begins the piece by explaining both the power and importance of links within the web. Quite simply the larger number of incoming links that direct visitors to your webpage then the higher the number of visitors you will have, which in turn increases your overall profile on the web. This sets your page apart from others, allowing for greater visibility and secures your place within the hiearchy as a destination site online. The perception of networks, its growth and mechanisms has traditionally followed the random worldview theory of networks as defined by Erdos-Rényi. Within this theory the interior of networks are connected at random, by nodes (this can be thought of as webpages online) and each node within the network is not distinguishable from another, meaning that they uniformly connect to the other nodes within the network the same number of times. (This example is presented in the previous post.) Hence the distribution of the links is equal between all the nodes thus abolishing any distinguishable character that one node may have over another. This distinguishable character within a network typically infers that there is “a few highly connected nodes, or hubs.” (58) According to the random network theory hubs could not exist because all nodes share the same amount of links to other nodes. Additionally the total number of nodes is static over time, leaving no room for growth in terms of the number of nodes existing within the network. Typically this theory has been represented by bell curves; in which various nodes are dispersed randomly and all connect to each other an equal amount of times, thus creating a peak within the middle of the graph. Accordingly within this random network theory all webpages would have an equal opportunity to be seen and heard. Barabási observes that this theory of networks is quite simply not true, and holds no ‘real world’ application and/or example.

He contests that the Erdos-Rényi theory of networks, which consist of nodes that are randomly connected with “roughly the same number of incoming links”, is not applicable at all to the hierarchal pattern of popular webpages prevalent within the web today. Barabási states that some webpages such as Amazon.com, Yahoo! etc. have an exceptional amount of incoming links and thus dominate the web through the sheer enormity and range of their incoming links from various networks across the web. Webpages like Amazon inherently become the hubs of the Internet. Additionally Barabási notes that just like actors within Hollywood (as observed within the Kevin Bacon phenomena, in which Bacon is only 2.79 degrees removed from everyone within the Hollywood network) the strength of these Internet hubs do not rely on the sheer size of the network but the range. This means that the links these nodes make to other various distant networks adds range to the network that the hubs possess. This adds variety and brings dissimilar things together. In contrast to hubs, Barabási points out that his own webpage is so insignificant within the web, due to the near infinite range of the Internet network, that the chance of a person viewing it is about forty in a billion. Within this contrast lies the main concerning question of the Internet and it’s network; how do some pages become these hubs of the Internet, in comparison to the majority of pages that exists in near obscurity?

Barabási claims that the theory and research of networks as represented by Erdos-Rényi random network theory fails to answer this question because of it’s fundamental underlying assumptions; ¹ Static: the amount of nodes in a Erdos-Rényi network are set, meaning there is no room for growth, which is in exact opposition to the nature of the web itself and ² Equality (Random Interconnectivity): each node within the network contains an equal amount of links to the other nodes thus making each node indistinguishable from the last, hence the nodes are linked randomly and are equal (there can be no room for hub webpages such as Google!). Within the random network’s nodes are considered a “characteristic scale[,] embodied by the average node and fixed by the peak degree of distribution.” (pg70)

 Barabási dismisses this entire method of perceiving networks, and states that the actual distribution, connection and dominance (or lack thereof) certain webpages within the Internet is determined by inverting the two random network assumptions. The new foundations of network theory are ruled by the following;

“¹Growth: For each give period of time we add a new node to the network. This step underscores the fact that networks are assembled one node at a time

² Preferential Attachment: We assume that each new node connects to the existing nodes with two links. The probability that it will choose a given node is proportional to the number of links the chosen node has. That is, given the choice between two nodes, one with twice as may link as the other; it is twice as likely that the new node will connect to the more connected node”.  (pg 86)

These foundations are further represented not in bell curves but by the Italian Economist Pareto’s 80/20 Rule and the power law phenomena it produces. Within a power law ruled network the majority of nodes have only a few links (incoming and outgoing) and these tiny nodes co-exist with a few big hubs that are highly connected with many nodes that traverse various differing networks. The power law exemplifies Pareto’s Rule, which states that 80% of effects are derived only from 20% of causes. The approximate 80/20 rule applied to the web means that “80% of the links on the web point to only 15% of total webpges”. (66)

Pareto’s 80/20 Rule and the power law graph that is supports provides us with a very different perspective on networks, especially according to how Barabási perceives networks in the Internet. The power law graph shows a slope that rises and then declines running horizontally parallel out to infinity.  The nodes located with this horizontal section account for the 80% of wepages and the peak of the slope which is located on the left hand side of the graph closest to the vertical line is the 15% of webpages that account for hubs. This unequal distribution of incoming links as presented within the power law graph is a more accurate description of the current state of the Internet. 

 As Barabási describes;

“The power law distribution thus forces us to abandon the idea of scale, or a characteristic node. In a continuous hierarchy there is no single node which we could pick out and claim to be characteristic of all the nodes. There is no intrinsic scale in these networks. This is the reason my research group started to describe networks with power-law degree distribution as scale-free.” (pg70)

This principle that describes the web and its interconnectivity is not a phenomenon distinctive to the web but applies to a variety of situations within human life.

The effect of power laws and the study of its presence is not a new phenomenon at all. In the last few years the existence of the Internet has dramatically changed how some view the various power laws at work within out world. The Long Tail as it was coined by Chris Anderson has become a staple of what some believe will be the future for commerce, specifically within the music industry. Anderson felt that the hubs within the music industry were an effect of the old 20^th century system of hits culture which encompassed production, manufacturing and the distribution of music. These hubs within music could be seen as various major record labels, major publishing companies, big name acts, and their hit repertoire. For many years the music industry was ruled by power laws, with only 20% (termed by Anderson as the ‘head’ of the power law) of the products (the term products primarily refers to music recordings like CD’s. LP vinyl etc) accounted for 80%( termed by Anderson as the ‘tail’ of the power law) of the sales.  The 20%, of  the ‘head’, are comprised of the goliaths of the music industry such as Michael Jackson’s classic ‘Thriller’ or by more recent releases like Rihanna’s album ‘A Good Girl Gone Bad’. The other 80% of niche genre music (such as instead of the main genre of Rock, you want Acid Rock) was virtually inaccessible, because the justification of producing, manufacturing, distributing and stocking this niche music  would not be meet via mass profits…until now.

Anderson proposed that with the democratization of distribution via Internet retail sites like Amazon, anyone could find and purchase a piece of music that they enjoyed. By riding the tail of the power law to the outer reaches music lovers could find music that was deeper within the niche of music that they always wanted to own, and/or discover new music. Additionally if we are to take a look at Barabási’s notion of networks within the Internet, the hubs of music such as Jay-Z could refer music lovers to other musicians within that rap/hip-hop genre of music like Nas, and the links from the Nas node could lead to other lesser known acts.

The sheer enormity of information and access that the Internet provides would allow for music lovers (or a consumer of any product/service) to be able to find exactly what they want, and be able to obtain that item. Through the Long Tail Anderson argues that we could begin to focus more on the niche products (or in this case music genres) that for so long have been relegated to the sidelines as the hit parade came into town. In fact Anderson argues that in terms of commerce businesses could focus on selling ‘alot of a little’. For example Anderson starts with just exploring the ‘A’ section of music genres and discovers a niche of Afro-Cuban Jazz. Although this isn’t a big genre within the ‘A’ section, in it there are hubs  like artist Tito Puente. You could focus as a retailer on having Tito Puente in addition to other artists within the same genre, like the Buena Vista Social Club. You may only sell a few copies of each artist, but the total of that entire sale of those artists in combination would equate to a profit either equal or similar to that amount if you might have sold if you were just focusing on the ‘head. Accordingly this would mark the end of hits, or is it?

Kanye, Lil’Wayne, Miley Cyrus, Pink, Kings of Leon etc, are all still selling millions of copies of their albums (or at the very least a solid half a million). These are mass marketed and mass produced artists, and more importantly as hubs of the music industry they still influence alot of other artists that are developing. Although in our preferences we might become very niche, the power and influence of hubs still remains. Hence although the playing field has been leveled, and as consumers we can have access to music like never before, we still need some sort of guidance. Anderson calls this ‘filtering’, which sites like Amazon with its Recommendations technology and Google through its search engine provide by offering a means of shifting through all of the products available.

 However, once again he is missing the bigger point of networks and the position of hubs within that network. For many music lovers finding new music is a journey that starts with a hub like John Mayer and then through the connections that artist makes as a hub whether it’s through genre, featured artists on his album or label mates, music lovers had to start at the ‘head’ of the power law and then ride to the ‘tail’ to find that music. If those hubs didn’t exist then discovery would be highly difficult. The  Erdos-Rényi random network theory was disproved because of this very thing;  if all the nodes have scale and there are no distinguishable characteristics making any one node different from another than the same information is passed through that type of network. Within the music industry this would mean that no new music would be able to be found, and by only focusing on the niches the bigger picture of music trends and tastes would be ignored. Hubs need niches and niches need hubs. Anderson’s continued crusade against hubs or ‘hits’ destabilizes the entire network theory; and its applications within music, Internet and life in general. Anderson fails to realize that as within any network; when a node has a choice between linking between two other nodes it will most likely choose the node that has the higher amount of links. The same can be said for music fans and webpages alike; hits are still hits.

The reading for this week, Linked by Albert-Laszlo Barabasi disproves the idea of randomness in networks and champions the idea of hubs and connectors that function to bring together society. The idea behind linked and being connected to others is not new nor is it phenomenal: it is in our blood. This is our history and thus, inevitable. The Bible clears demonstrates this connection as in the beginning, there was Adam, then Eve, then Cain and Able. After that, humanity arises and creates towns to settle and we develop. Thus, we are all connected or linked in some way. If one is agnostic or believes in science, then think of evolution. People evolved as running from animals to hunting them in groups. Once in groups, they became nomads, and then quite surely, someone decided to settle down in a location, therefore forming a town. Thus a hub is created and with it, streets, other towns, etc…

It is divided into 3 chapters. I will discuss each chapter separately by its part and tie it together at the end. They are all connected; rather, they are components of a larger truth.

The Fifth Link: Malcolm Gladwell’s The Tipping Point notices an interesting phenomenon: “sprinkled among every walk of life…are a handful of people with a truly extraordinary knack of making friends and acquaintances. They are connectors” (55). This brings in the idea of connectors as highly relevant components of our social lives. Another way to define connectors is by calling them hubs. These hubs have a large number of links. One can create the analogy of a large city, such as Los Angeles and the suburbs that surrounds it. Many highways enter and go out of Los Angeles, connecting many different cities along the way. This discovery has basically disproven Erdos-Renyi’s theory of random worldview and Watts and Strogatz’s simple circle network model. Social networks exhibit clusters and hubs, both points that demonstrate a worldly rule that applies.

We can also apply the theory of links and connectors/hubs to cyberspace. The World Wide Web is the ultimate space for freedom, an environment that represents and defines the meaning of space without limits, boundaries, nor rules. Anyone can publish their work online and allow anyone to see it. Problem is, there are billions of websites. The question that arises is visibility. On the Web, the “measure of visibility is the number of links. The more incoming links pointing to your Webpage, the more visible it is” (57). As such, only websites that become hubs are highly visible (Amazon, Google). These are hubs: connectors with enormous links to other nodes.

I can create a website, thus it becomes a node. Google has grown, thus it has become a hub. Without a link, both websites exist and move in different worlds, or just space. The internet is as big as the universe, each node moves on its own, until it makes contact. This is the link. My website can function in its own galaxy as networks tend to form a cluster. Clusters are “nodes that are linked only to nodes in their subculture or genre” (61).  As a cluster, it is easier to find a common connection to a hub. For example, if my website is about knee pains, then I would like to create some link to a popular website such as WebMD. This in turn, can allow me to link with Google. In two links, my website has escaped randomness and can be visible.

The Sixth Link: Vilfredo Pareto may be a well known Italian economist, but I feel that his thought process behind the 80/20 Rule is sheer brilliance. It is all around us, and yet, he is the first to notice that it applies to the world. This is Simple Genius: understanding a law within the world that is so visible, and yet, invisible to the eye. 80/20 Rule applies to many things, but is generally regarded as 20 own rest of the 80. This also applies to network as well. It can be proven through a mathematical expression called a power law. In contrast to a bell curve, which is a “distribution rather similar to the peaked distribution characterizing random networks,” (67) the power law is by definition a special kind of mathematical relationship between two quantities. The exact definition is as follows: if one quantity is the frequency of an event, the other is the size of the event, then the relationship has a power law distribution when the frequency of the event decreases at a greater rate than the size increases. In layman terms, think of 20% of the population ruling 80% of the world.

Power laws basically functions to prove mathematically the fact that in most “real networks the majority of nodes have only a few links and that these numerous tiny nodes coexist with a few big hubs, nodes with an anomalously high number of links” (70). Again, this relates to Gladwell’s idea of connectors, but proving quantitatively that it exists within the realm of the World Wide Web. It demonstrates that real networks are not random at all, but exist under the power law. The interesting idea that Barabasi proposes is that in the beginning, nodes tend to be chaotic and without any form or order. However, through time, this disorder turned into order through self-organization. Under the theory of phase transitions, real networks demonstrate self creation from disorder into the 80/20 Rule. Barabasi’s idea is highly compelling, as he demonstrates quantitative analysis and by using the Web, a blank space which is like a universe unto itself, to explain a simple law that applies to the world. Everyone is intricately linked, because we are social beings. Networking is only a function of humanity, which always existed, but has now been proven through the power law. A question that arises is why do hubs and links form and how does it form in such a manner that to the naked eye is so random and chaotic, and yet, has order.

The Seventh Link: Erdos and Renyi’s model of networks rely on two principles. First is the idea that all nodes are fixed and remains unchanged throughout the network’s life. Second is that all nodes are equivalent. However, this is obviously not the case as there exists hubs/connectors, links, and change from disorder to order through self-organization. To understand how it does this, we must understand that the web is constantly growing. It is changing and growing. This is quite self-explanatory. There has been exponential growth of websites on the World Wide Web. As there is growth, we can safely disprove the static nature of Erdos-Renyi’s model of networks. We also do not randomly decide on which websites to link. We choose based upon our knowledge and social upbringing. We prefer certain websites over others because we are comfortable and familiar with that certain product. Thus, rises the concept of hubs. Barabasi brings up the idea of preferential attachment: “when choosing between two pages, one with twice as many links as the other, about twice as many people link to the more connected page. While our individual choices are highly unpredictable, as a group we follow strict patterns” (85). In many sense, this is true. We are all sheep and follow the leader.

In truth, randomness does not really exist unless it is the role of a die. Linking between networks is not random. Though unmentioned as third criteria, I believe that popularity and attractiveness plays an important role in the addition of links and creation of hubs. Webpages that have more links are more likely to be “linked to again” (86). Thus, there exists first person advantage. Older nodes have greater chances to become bigger and eventually rise as a hub. As a senior member within a link, this node has greater links and more nodes want to be linked to that certain node. Barabasi has given compelling evidence that real networks are not random, but constantly evolving and growing, attracting more links through the legitimacy of the power law.

We are living in a complex world and yet, guided by the invisible hand or law that is, in my opinion, inexplicable. The idea of real networks applies not only to websites, but to humanity in large. I believe that Barabasi’s point is that humans are social beings and we are followers. There are different people: some are leaders and some are followers. This difference guides the principle of 80/20 Rule as well as the power law. Barabasi has done something quite remarkable: take a simple, obvious, and yet invisible rule, and proved it scientifically and gave it a name. Kudos.

Our latest reading is Linked written by Albert-Laszlo Barabasi. The reading centers on research and developments regarding the “network” through time.

• The reading describes how network analysis originally grew from graph theory. Leonhard Euler, was a Swiss mathematician who first examined graph theory. Euler addressed an issue in a small town called Konigsberg where locals wanted to discover if there was anyway one could, “walk across the seven bridges and never cross the same on twice?” Euler discovered that such a method did not exist by constructing a graph based on the bridges of Konigsberg, he made the various bridges nodes and connected them by links and thus the “network” was born.
• Linked illustrates a network with the example given on page 14, if you gather a group of 100 strangers, then eventually as human beings have a natural desire for companionship, they’ll begin to form small groups of acquaintances. Then by introducing a small detail to the group, such as a rare wine, the news will rapidly spread by individuals or nodes that bridge the information from one group to another. Types of networks include the society, the internet, a cell, or the brain. And all these networks can be illustrated by graphs.
• Paul Erdos and Alfred Renyi, two mathematicians who studied the wine simulation suggested the best way to form a network is to connect nodes randomly. By randomly connecting nodes, one will be eventually left with one giant cluster. Where in, one can navigate to anyone else by the navigating the links of the nodes. Similarly, one can say that all humans are apart of a worldwide social cluster, where we can navigate to one another through various connections. Nevertheless, Erdos and Renyi’s theory only takes into account random networks, where all nodes are equal. On the contrary, in the actual society web, networks have distinct class systems such as governments and social hierarchies.
• Later on, Stanley Milgram, would reveal research that suggests that nodes in a network are less random than Erdos and Renyi thought them out to be. Milgram sought to find the “distance” between any two people in the United States and thus formulated an experiment where he sought to bridge two people by sending letters to randomly chosen residents. If you didn’t know the actually recipient, you would resend the letter to someone who you think might be closer to the recipient. Milgram’s research concluded the famous, “6 degrees of separation.” Similar to six degrees of separation, social networking websites such as facebook, and Myspace list mutual friend lists that illustrate this exact theory. When becoming friends with a NYU student from a different hometown then you, sometimes you’ll find that you’re already connected by a mutual friend. I just discovered that Emma now dorms with a girl who lived down the hall from me last year.
• A current event that illustrates the six degrees of separation model is the new addition of “lists” to Twitter. Twitter, a social networking website has recently launched Twitter Lists which allows you to cluster users who have similar tweeting topics. Thus one can create, an athlete list, a movie star list, etc. Then people can subscribe to your lists to access the people you follow. So if one is looking for a very funny twitter user whom they don’t know, they might access their friends “Hilarious People List” and continue on the chain until they find exactly who they’re looking for. The new development was designed specifically for people who seek to make Twitter more organized. http://blog.twitter.com/2009/09/soon-to-launch-lists.html
• Moving along, in 1980 Tim Berneres-Lee sought to make the information in every computer accessible to everyone else, Tim envisioned a virtual network where we all bridge to one another, and this network eventually became the World Wide Web. This made me recall a previous post on our blog called, “The Power of Words,” (http://idm09.wordpress.com/2009/10/04/the-power-of-words/) the post discusses how society has taken technologies from the past and built upon them to apply them to the needs of today. It’s easy to see how networks have grown from groups of immediate neighbors, to long-distance neighbors through postage, to the telephone network, to the internet. Through today’s World Wide Web, we’re allowed to stretch our networks farther than ever before because we correspond to virtually anyone in a matter of seconds. The post also describes how society needs links to help bind and restrict data, because too much data at one time would be overwhelming. Similarly, we need networks to better organize the people we know. Networks help us narrow down the people we know because taking on everyone can be overwhelming. Precisely what the new Twitter Lists are trying to accomplish.
• Linked later goes on to describe how “six degrees” is a product of our modern society that we have designed to keep in touch or in other words communicate over long distances. In Mark Granovetter’s Social World he describes a network where small fully connected circles are connected by strong ties. Granovetter’s theory suggests that certain nodes can group together that bridge to another group by one link. When thinking about connections like that I recall, Zoom, the picture book by Istvan Banyai. In the book, the pictures unfold as if one is zooming in onto a specific image. Though certain pictures are connected very tightly, they eventually connect to a transition picture that connects them to the next scene.

  

  
  

• The last network discussed is the Circle Network by Duncan Watts and Steven Strogratz. Their networks illustrates how nodes are connected to immediate neighbors which are eventually connected through long range links. Unlike the previous networks, the Circle Network is not based on random bonds. It has the most structure, it’s very much like a classic character web in literature. Especially in Shakespearian literature, the characters have very distinct connections and relationships that play into a plot. This also shows how a “network” is an integral part in telling a narrative similar to how we’ve made other connections between narratives and media.

The reading for this week, Linked by Albert-Laszlo Barabasi, began to outline the concept of networks by highlighting several network theories.

The idea of networks first originated with a Swiss mathematician named Euler.  He lived near a town named Konigsberg which had seven bridges.

The people of the town had always tried to cross all the bridges only once, but Euler offered a proof that it was impossible to cross the seven bridges of Konigsberg without crossing one more than once by laying out vertices at common points.  This spurred the idea of graph theory, which includes “a collection of nodes connected by links” (11).  His graph had nodes that were pieces of land and links that were bridges.  Nodes with an odd number of links must either be the start or end of the journey, and since the graph had more than 2 nodes with an odd number of links, there was no way to only cross each bridge once.

This graph theory spurred several theories about the structure of networks:

1.  Paul Erdos and Alfred Renyi had a theory that social webs form rapidly and randomly.  They “equated complexity with randomness” (23), assuming that all social connections are “fundamentally random” (17).   Despite the links’ random placement, “all nodes will have approximately the same number of links” (22), thus most people have roughly the same amount of friends and acquaintances, no matter how random they may be.

2.  Another theorist was Mark Granovetter, who focused on the “importance of weak social ties in our lives” (41).  He thought that networks were structured in highly connected clusters (close-knit circles of friends) in which everybody knows everybody else along with a few external links connecting these clusters to keep them from being isolated.

3.  A final theory discussed in the reading was one developed by Duncan Watts and Steven Strogatz.  They envisioned that we “live in a circle where everybody knows their immediate neighbor” with random links connecting different people (51).

Which graph theory are you more inclined to believe?  Do you think that our links depend more on randomization or the specific circles we may run in?  Or do you perhaps have your own model in mind?

Another interesting point brought up in the reading is the idea of 6 degrees of separation.  In other words, “people are linked by at most five links” (27).  Anyone in the world can be connected with anyone else through our dense social network.  For example, I will now connect myself with Obama.

1. I went to hebrew school with 2 girls

2. The girls were the daughters of  Lawrence Summers

3. Lawrence Summers was Secretary of the Treasury under the Clinton administration

4. Bill Clinton enjoys lunches with Obama from time to time

Voila!  I’m connected to Obama in 4 links!  I encourage you to see who you can be linked to and in how many steps (George Bush? Kevin Bacon?)

This idea of 6 degrees of separation also carries over to one of the biggest networks in the world; the world wide web. There are billions of pages, with the net growing at an exponential rate. Even so, the connection between any 2 pages on the web is anywhere from 2 to 19 clicks, with an average around 11.

This whole idea of degrees of separation reminded me a lot of Tylan’s posting:

http://idm09.wordpress.com/2009/10/18/space-race/

He talks about something called the wikirace, where people are challenged to find the least amount of clicks to get from one specific topic to the next.  It further illustrates the high level of connectivity between networks, this specifically demonstrating the network of wikipedia pages (which are all interwoven with their many hyperlinks that lead to vast numbers of other pages).

Fuente: Kris Davos (Flickr)

Viene de la Parte 4

Fuente: The Edge

Fuente: Kris Davos (Flickr)

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Fuente: Kris Davos (Flickr)

Viene de la Parte 3

… Continua en la Parte 5

Fuente: The Edge

Fuente: Kris Davos (Flickr)

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Fuente: Fractal Recursions

La primera parte de este conjunto de interesantes leyes, se puede consultar aquí

(Esto no es pesimismo, sino realismo, una analogía ampliada de la segunda ley de la termodinámica.)

Por ejemplo:
Cerebro-Mente
Mente-Cuerpo
Emoción-Razón
Natural-Adquirido
Nosotros-Ellos
Recuerda siempre que es fácil estar en posesión de alguna explicación, pero extraordinariamente dificil conocer la verdad.


… Continúa en la Parte 3…

Fuente: The Edge

Fuente: Flickr, KrisDavos

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I recently came across Nexus, an application that can map out the connections in and amongst all of your collected friends on Facebook.  The result is, I think, pretty neat.

Network of friends from Facebook, listed by decreasing density - MBA, Regina, Vancouver (the two central networks), Family

As you can see, my friends form some interesting smaller networks, through which they are all connected to each other (at the very least) through me.  It is possible there are other connections which have not been ‘formalized’ through facebook, but at present this is how it stands.

The lower right collection of nodes are mostly representative of my life in Regina before I moved to Vancouver.  They are friends from elementary and high school, earlier university days and from my year there after I returned from travelling but before I moved out here.

The lower left area (two diagonal lines) consists mainly of my family – or at least, those of whom are on facebook – and (strangely) all of the friends I remain in contact with from Terra Breads, where I worked for two years upon arriving in Vancouver.

The nodes which are relatively connection free (mostly on the right side) are people who I met while travelling and working in Australia and Europe, from 2001-2003.  Not surprisingly, they know few of my friends from either my past or present.

The middle node is, to me at least, the most interesting.  It has in it my girlfriend, my friends who I lived with, and all of my closest friends here in Vancouver.  That entire network grew out of a chance encounter I had when I was looking for a second place to live after not really liking my first place or my roommates after arriving here.  In a sense, it is the reason I stayed put, and I am very glad of that because out of it came all of the happiness that I have today.

The secondary node just above it is why I started writing this post today as I reflected on a book I recently read – Linked (How Everything is Connected to Everything Else and what it means for Business, Science and Everyday Life) by Albert-László Barabási.  That book would probably describe that network as being fairly strongly connected internally but with a lot of weak ties radiating away from it – and it is because of that fact, and specifically that secondary node, that I: a) became a teacher/tutor for two years, whereby I; b) re-ignited my passion for learning and the world, which led me to; c) apply for grad school whereby I; d)  became a part of the very tightly connected upper network, which is a physical representation of all of my friends/aquaintances from the UBC MBA program.  Many of these people are now close friends who I will almost certainly know for most of the rest of my life.  The important thing to note is that each node (a person) is connected pretty well to every other node within the network.

Tightly-knit MBA network

Strangely (on the surface at least), it will probably be that central network, with its many weak branching ties to other networks, that gets me my next job and not the very tightly knit MBA network.  This is due in part to concept of The Strength of Weak Ties – a research paper written by sociologist Mark Grannovetter that was published in 1973 – which of course was described in Linked and which resulted in a, b, c and d in my own life.   In other words, when you’re looking for a job, as I and many of my fellow MBAs are these days, it is best to look amongst large networks with many weak, branching ties; in that way, you are more likely to come across information that is not already mutually known, for example, the job board we all look at called COOL, which stands for Career Options On Line (I call weak sauce on the name, by the way; looking for jobs is not ‘cool’ – looking for jobs sucks, and takes time, and is generally demoralizaing.  But anyway, I digress.)  Which is not to say that this MBA network is not worth something – in fact, I think the opposite is true – it will be extremely valuable in the future (to each of us) as we all branch out in the different directions our lives take us and remain, importantly, weakly connected to one another.

The important central network with (relatively) many weak and branching ties

In any case, I encourage you to have a look at the nexus friend application and examine how all of your friends and aquaintances are connected to you and each other.  You can find it all here.

Linked by Albert-Laszlo Barabasi has opened up an incredible range of knowledge regarding the laws of networks.  Albert goes far beyond the work of Duncan Watts in Six Degrees to explain the existence of many of the observed properties of complex networks and consequently the behaviour of complex systems.  The random graphs, clustering, power curves, hubs, network growth,  preferential attatchment, fitness, Bose-Einstein condensates are all introduced to the reader.  Ultimately the book is an introduction to the discovery of scale-free networks and reveals that all the past models based on random networks are wrong.

I highly recommend this book to anyone wanting to learn the current understanding of networks and the implications.

Parasitic Computing is a scheme in which standard communication protocols (such as TCP/IP) can be exploited to perform computation with the communication infrastructure. This scheme may allow a parasite computer to “steal” CPU cycles from another computer, thus gaining access to potentially massive amounts of computational power at “zero” cost. This computing paradigm was introduced in a paper which was submitted to Nature by a team at the University of Notre Dame:

• Parasitic computing (PDF – 143KB), Vincent Freeh, Albert-László Barabási, Hawoong Jeong and Jay Brockman, Nature, August 2001.

Here’s a schematic diagram from the paper depicting a parasite node (in green) targeting web servers (in blue):

[ diagram courtesy of Vincent Freeh et alia ]

where ALU stands for Arithmetic Logic Unit, and NIF stands for Network InterFace. More detailed information can be found on the Notre Dame’s page on Parasitic Computing:

• Supplementary Material (PDF – 57KB)

• Parasitic Computing Example (PDF – 29KB)
  

• Frequently asked questions about parasitic computing

From Notre Dame’s page on Parasitic Computing, here’s an overview:

On the Internet, reliable communication is guaranteed by a standard set of protocols, used by all computers. These protocols can be exploited to compute with the communication infrastructure, transforming the Internet into a distributed computer in which servers unwittingly perform computation on behalf of a remote node. In this model, one machine forces target computers to solve a piece of a complex computational problem merely by engaging them in standard communication.

Just brilliant! But, is this scheme even legal? Why did the authors publish such a potentially “evil” discovery?

Parasitic computing raises important questions about the ownership of the resources connected to the Internet and challenges current computing paradigms. The purpose of our work it to raise awareness of the existence of these issues, before they could be exploited. By publishing our work we wish to bring the Internet’s various existing vulnerabilities to the attention of both the scientific community and the society at large, so that the ethical, legal and scientific ramifications raised by it can be resolved.

I think this idea is conceptually simple. Note, however, that an idea that is conceptually simple may not be that easy to implement. Regarding implementation issues, here’s what the Notre Dame team has to say:

Our implementation of parasitic computing is not efficient. If it is made efficient, it could offer unlimited computational power. How should it be dealt with then? Should it be allowed under controlled circumstances? These are issues that the community must address shortly. On our side we are currently developing SNORT rules to catch such activity.

Related:

• Anatomy of a parasitic computer

Every book marketer dreams of having a title go viral – when a product catches on and is purchased by a huge numbers of customers in a relatively short period of time.  Such events are rare, but have captured the marketing imagination. 

Duncan Watts

Scientists, like Duncan Watts and Albert Laszlo Barabasi, who study such contagions in social networks call these events global cascades.  These cascades are made possible by the presence of what they term a “percolating cluster.”  A percolating cluster is made up of nodes in the network (read prospective customers) whose decision threshold is low and where the average degree (or number of other influencers) is not too high.  The good news is that we can characterize the conditions that will allow a new book to go viral.  The bad news is that we have no way to find these percolating clusters.

That hasn’t stopped marketers from giving advice on how to use viral marketing strategies.  Ralph F. Wilson of Frugal Marketing is a representative example.  He provides 6 tips for an effective viral marketing campaign, including:

• Gives away products or services
• Provide for effortless transfer to others
• Scale easily from small to very large
• Exploit common motivations and behaviors
• Utilize existing communication networks
• Take advantage of others’ resources

Vahid Chaychi offers some additional tricks in his online article Viral Marketing Strategies – Learn How to Spread the Word for Free! including affiliate programs, recommendation campaigns.  Keith Gloster, of the Concept Marketing Group puts a special focus on using e-books to create viral buzz in his article, 10 High-Impact Viral Marketing Strategies.  He contends that free e-books or downloadable items make good viral accelerants.  The highest profile champion of viral marketing is probably David Meerman Scott (see picture at right), whose PDF, The New Rules of Viral Marketing, became its own viral hit.  Bob Bly offers some refinements on the Scott model by using the free e-book to feed an e-list campaign.  For books, one can now add videos and widgets – those ubiquitous creatures of the cut & paste web -to the viral toolbox

percolating cluster

Despite the cornucopia of advice, those percolating clusters remain elusive.  Many marketers hope that early adopters will ignite a viral stampede.  But early adopters are not the key to viral success.  It is the receptivity of the network of potential customers to the new idea.  This receptivity is determined by network parameters that have little to do with being a highly connected (influential) early adopter.  And this receptivity is both dynamic and not something we can easily measure.  So – if network science is right – most viral campaigns will wind up like the proverbial seeds that fall on barren earth; they will not bear the desired fruit.

The real global cascade here may be the dream of viral marketing success rather than the reality.

Related Posts

• Viral Book Video and Online Book Ads
• The End of Influence as We Know It
• Widgets, Nuggets and Tags on the Cut and Paste Web
• Don’t Know Much About Web Geography

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Ok, um dos pais da internet não veio ao país para participar da abertura do escritório brasileiro da W3C, órgão regulador da rede. Tim Berners-Lee passou seu recado aos presentes por videoconferência gravada e centrou sua fala em e-government, pincelando a web nos celulares.

Pesquisa – 100 mil pessoas foram rastreadas pelo celular, por meio dos sinais de ligações e envio e recepção de mensagens de texto. Resultado: o padrão do ser humano é previsível. A maioria se desloca para o trabalho e escola e volta para casa. Do total de pesquisados, praticamente 75% restringiram-se a um raio de 32 km.

Um dos resultados do estudo “Mobile phones demystify commuter rat race” publicado na Nature, segundo Albert-Laszló Barabási, da Northeastern University (EUA), pode ajudar epidemiologistas a prever como vírus podem se espalhar em populações, auxiliando urbanistas, por exemplo, a realocar recursos.

O estudo aponta padrões que podem parecer óbvios, disse Barabási, que coordenou a pesquisa. No site da Nature, ele afirma que “ao olhar para a população como um todo, não há como descrever os padrões. O problema ao responder a essa pergunta é que as pessoas normalmente não são rastreadas, mas hoje somos rastreados graças aos celulares que carregamos”.

O que o estudioso fez – Barabási e seus colegas conseguiram autorização de uma operadora de telefonia móvel, sob a condição de anonimato, monitorar chamadas e mensagens de textos de 100 mil pessoas ao longo de seis meses.

Privacidade – O estudo de Barabási enfrenta alguns desafios. Por questões contratuais, a pesquisa não pode divulgar em que país foi feita. Isso pode, no entanto, afetar os padrões de hábitos variados em países diferentes. Dirk Brockmann (Northwestern University – Illinois, ), que tentou analisar o movimento de mais de meio milhão de cédulas de um dólar durante cinco anos, diz que a questão agora é descobrir por que algo tão complexo como o movimento humano segue padrões tão consistentes. “Nenhum estudo pode responder a essa questão.”

Em tempo: se alguém quiser rastrear Barabási, é indispensável a leitura de “Linked: How Everything Is Connected to Everything Else and What It Means“. Foi publicado em 2000 e é atualíssimo.

Albert-Laszlo Barabasi (probablement l’un des meilleurs experts sur la question de la science des réseaux), à ajouter ce sous-titre à son livre, Linked—How Everything Is Connected to Everything Else and What It Means for Business, Science, and Everyday Life. Selon Barabasi, les 5 @ 7, les cellules terroristes, les anciennes bactéries, et les conglomerats internationaux sont tous des formes de réseaux. Un réseau est ni plus ni moins qu’une collection d’objets interreliés les uns aux autres d’une quelconque manière.

Les pros de l’info sont maîtres de leurs réseaux également et ils en ont souvent plus d’un. Ce sont habituellement de véritables agrégateurs de contenus, des gourous de l’info. Ils connectent individus et contenus.

Voici un extrait faisant référence à Malcolm Gladwell et sa théorie sur les connecteurs. En cliquant sur le texte vous aurez accès au contenu fort intéressant sur le sujet.

“Barabasi makes the point that networks from fields as diverse as ecology, molecular biology, computer science, and quantum physics have common characteristics and that these characteristics can inform us about any network, including online social networks and virtual communities. Two of these characteristics are clustering and power laws. The world is highly clustered. For example, many scientific papers are written by three or more authors. Power Laws say that most network nodes have only a few links and that highly connected hubs hold a network together. Social networks are based on people defined as “connectors” by author Malcolm Gladwell. Says Gladwell: “Sprinkled among every walk of life, are a handful of people with a truly extraordinary knack of making friends and acquaintances. These people are connectors.” They bring the most value to the social network.”