Implicit None

To me, the main question when modeling a process in the social sciences is "when does emulation count as explanation?" — John Myles White (@johnmyleswhite) March 25, 2013 Interesting question. I guess the problem is that sometimes in science a model does not pretend to make any prediction, neither is supported by any relevant data. It simply emulates reality.

Authors: Giovanna Miritello, Esteban Moro, Rubén Lara, Rocío Martínez-López, Sam G. B. Roberts, Robin I. M. Dunbar Journal: Social Networks 35, 89 (2013) LINK | arXiv Abstract: We used a large database of 9 billion calls from 20 million mobile users to examine the relationships between aggregated time spent on the phone, personal network size, tiestrength and the way in which users distributed their limited time across their network (disparity). Compared to those with smaller networks, those with large networks did not devote proportionally more time to communication and had on average weaker ties (as measured by time spent communicating). ...

**UPDATE**: the version of the R code in this post does not work with newer versions of the igraph package (\ 1.0). I have posted an updated version of this post here: [Temporal networks with R and igraph (updated)](/post/2015-12-21-temporal-networks-with-r-and-igraph-updated/). Please visit the new post to use the new code and follow the discussion there. In my last post about how a twitter conversation unfolds in time on Twitter, the dynamical nature of information diffusion in twitter was illustrated with a video of the temporal network of interactions (RTs) between accounts. ...

Preferential attachment is a key process governing the dynamics of many economic, social and biological process. It is the “The rich get richer” mechanism by which a quantity is distributed among individuals according to how much they already have. It also happens in social networks and the ones that have more social connectivity (the “hubs”) receive more new connections than the poorly connected. In a famous paper, Laszlo Barabási and Reka Albert encoded this mechanics in the so called Barabasi-Albert model to generate random scale free-networks. ...

Millions of tweets, retweets and mentions are exchanged in Twitter everyday about very different subjects, events, opinions, etc. While aggregating this data over a time window might help to understand some properties of those processes in online social networks, the speed of information diffusion around particular time-bound events requires a temporal analysis of them. To show that (and with the help of the Text & Opinion Mining Group at IIC) we collected all tweets (750k) of the vibrant conversation around the disputed subject of the general strike of March 29th in Spain. ...

Yesterday I gave a talk in the 6th IIC Technology Conference about how Social Contagion can be leveraged for marketing purposes. The motto of the conference was about the need of using Algorithms in nowadays business process. With the availability of more and more complex data the use of algorithms that can detect and reduce complexity is of paramount importance. Big data is not only about volume (TeraBytes of data), it is about huge complex data and reducing that complexity can only be achieved by modeling, simulating and analyzing the patterns we observe in the data. ...