Web 2.0: A “Sociological” Point of View

Complex SystemsNo Comments

A Web 2.0 “Sociological” point of view is probably the most interesting analysis, mainly because it can be really considered as the very network revolution. Web 1.0 could rely only on a kind of link: the one obtained from <a …> tags; its purpose was (and still it is) to allow the connection between two different documents according to their content or other criteria (they’re often marketing-oriented). Thus the resultant network was made up of several (pseudo-)static or slowly changing nodes which connectivity was always bound by lots of limitations (first of all a spare knowledge about similar resources). The pure hypertextual technology was certainly great, but its weaker point was that of hiding the human beings, with their mutable nature, who were always behind the origin and development of each document (or web page). Web 1.0 was just like a geographical network (like a city map, for example) where human action […]

Time for Emergence

Artificial Intelligence, Complex Systems, Computational NeuroscienceNo Comments

When Sir Tim Berners-Lee had his stroke of genius and invented the World Wide Web, he didn’t surely think about its extraordinary present developments, just like a father normally hopes his children’s wellbeing, but he can’t seldom figure out every particular detail of their future. Such a behavior strikes everybody as a strange kind of myopia, like a bewitched gold-digger who stops in front of a slight glow and forgets its source. Even if there could be a strong temptation to believe it, several studies showed that its deep nature is quite different and probably no suitable attitude may avoid it. The first time I studied the concept of Emergence, I was attending a Complex Systems course at university, and my very first thought was that of a pure “scientific skepticism”: a sparkling description for an idea that had soon gone up in smoke… When I realized its immense strength, by then lots of its […]

Small Worlds… A little bit larger

Complex SystemsNo Comments

Yesterday I tried to answer a question on LinkedIn about the value we can assign to high-level connections (3rd or greater): a quite good question from several points of views. I think that the main problem of “Small Worlds” theory is brought about the huge amount of information needed in order to get a fast propagation through a certain social network. A world can be really small, but such a property may be easily tested by its members only if there’s a global and widespread knowledge about both the structure and the nature of the majority of nodes which a network is made up of. Thus the value of a 3rd level connection has to be considered not only from an intrinsic point of view (a person we can contact directly just like other “nearer” friends) but also as an additional set of information that we can exploit in order […]

From Computer to Business Operating Systems

Complex Systems, Software, Software EngineeringNo Comments

Many years ago Arnold Zuboff wrote a novel (republished into The Mind’s I) which “main character” was a biological brain; but not a normal, compact one: it had been split more and more times, from two big parts since billions of smaller cells. Of course every single neuron was connected to other ones just like in a whole brain, so its complete functionality was preserved and the result of all interactions could be still considered as a real personality. In a period of monolithic applications the concept of global distribution was surely treated as a possible but irrational reality, however, even if with a bit of humour, such a novel does explains something that is currently happening with different characters but the same identical roles and functions. Network society is now so rooted in our life that almost nobody is able to “feel” its daily action; maybe the majority of us are careless, […]

Are there isles inside LinkedIn?

Complex SystemsNo Comments

It has been proved that social networks are based on a scale-free model: there are few hubs with lots of connections and several small nodes with few links. However, I wonder what is the level of global connectivity inside a well-known network like LinkedIn. In other words I’d like to know if there are small or large isles made up of a group of isolated nodes; it’s not so easy to answer this question and maybe the only valid result may come from a direct analysis of network data, however it’s possible to assume some hypothesis in order to simplify our problem: The distribution of hubs in a scale-free network is homogeneous in a context defined by geography, jobs, common interests, etc. We exclude every smaller isle (they can be considered as isolated nodes). The critical dimension can be estimated according to the total number of nodes. A lower value always assures a better precision, but it […]