Sincrético

It’s been hard to find time to write about anything nowadays…

Today I’m using the blog truly as a way of organizing my thoughts. I am having a bit of a hassle to organize my notes about networks and the spread of information. There are a couple of dimensions to cover. First, the technical aspects starting with graphs and their evolution to the modern manifestations of small-world networks and its subsequent variations such as — if I am correct — scale-free networks, including their inner structure, the connectedness of their nodes, their order and randomness and other technicalities.

Secondly, when specifically talking about social networks, there are issues such as the actors within them and the roles they play such as hubs and authorities, as well as the so-called “influentials” and their apparently marginal advantage in comparison to “normal” nodes in their capacity to spread information more efficiently (cf. Watts). One must not oversee the functions of strong and weak ties (cf. Granovetter), the former being part of tightly knit clusters formed of intimate connections and the latter functioning as bridges between different clusters on a network. Weak ties, according to Granovetter’s highly influential theory (1973), play an important role as bridges between different communities. Other social roles must be also taken in consideration, such as those of opinion leaders (Katz, Lazarsfeld 1955), innovators and early adopters in the diffusion of innovations (Rogers 2003) and more popular incarnations of “influentials” like those described by Gladwell (2000), among others.

Thirdly, with the social roles accounted for, we must, or I must :-), consider the spread of information and the dissemination of knowledge across networks, taking in considerations the several concepts and models that have been established in the analysis of social networks and in the “new” science of networks (Watts 2004). There are theories such as the SIR model (Susceptible, Infected, Recovered) which look at the spread of information from an epidemiological perspective with variants that take in consideration the “resistance” of nodes to contagion (thresholds). Other approaches include information cascades, percolation theories, the diffusion of innovation model of Rogers, among others. But their all share a similar aspect, that nodes can in way influence each other, depending on their ability to convince their neighborhood and the latter’s resistance these ideas, and also how connected nodes are to one another. If nodes are too tightly connected, cascades stay local, if they are too loosely connected to random parts of the networks cascades may disperse; there should be a compromise between looseness and density. Virtue lies in the mean as our good friend Aristotle would already say back in the day.

Anyway, this post has got much bigger than I expected. It however helped me in putting a mess to my thoughts. I’m starting to appreciate this blog thing… that is, if one is able to find the time to do it

Work referenced in this post:

Gladwell, M. (2000). The Tipping Point: How Little Things Can Make a Big Difference. New York: Little, Brown and Company.

Granovetter, M. (1973). The Strength of Weak Ties. American Journal of Sociology , 78 (6), 1360.

Rogers, E. (2003). Diffusion of Innovations. Simon and Schuster.

Thomson, J. (1955). The Nicomachean Ethics. Penguin Books: London.

WATTS, D. (2004). The New science of networks. Annual review of sociology , 30 ,
243–270.