i attended netsci a couple of weeks ago. here is my stream of consciousness:<\/p>\n
lada adamic<\/a> talked about how info changes as it propagates through the blogsphere, and she effectively modelled this change \u00c2\u00a0as a simple urn model. more on her upcoming ICSW paper. \u00c2\u00a0her future research will go into how sentiment of memes changes\/evolves (this topic has been recently covered by \u00c2\u00a0jure leskovec).<\/p>\n former navy officer duncan watts<\/a> presented few macro sociological lab experiments and field experiments that he and his colleagues run on social media sites. he showed how media sites such as Twitter, Facebook, and Mechanical Turk allow researchers to measure individual level behaviour and interactions on a massive scale in real time. the good news is that \u00c2\u00a0there are already guides for running experiments on those platforms\u00c2\u00a0 (see, for example, the tutorial at icwsm by paolacci\u00c2\u00a0 and mason).\u00c2\u00a0 the experiments he mentioned are fully reported in his latest book \"Everything is Obvious<\/a>\". The main idea behind the book could be summarised as follows:<\/p>\n our intuition for human behaviour is so rich: we can \"explain\" essentially anything we observe. in turn, we feel like we ought to be able to predict, manage, direct the behaviours of others. yet often when we try to do these things (in government, policy, business, marketing), we fail. that is because, paradoxically, our intuition for human behaviour may actually impede our understanding of it. perhaps a more scientific behaviour would help us. the book is about experiments whose goal was to understand human behaviour at a large scale.<\/p><\/blockquote>\n olivia woolley meza of\u00c2\u00a0 max plank presented the results of a project<\/a> that measured the impact of two events (ie, island vulcan ashes and 9\/11) on flight fluxes. these fluxes were modelled as\u00c2\u00a0 a network and metrics of interest were computed on the network - for example, they computed network fragmentation (nodes remaining in the largest connected component), and network inflation (how distances in the network decay). this study provided few intuitive take-aways, including:<\/p>\n with a presentation titled \"A universal model for mobility and migration patterns\", filippo simini (supervised by \u00c2\u00a0marta gonzalez<\/a>) turned to the question of whether it is possible to predict the number of flying\/public transportation commuters between two locations. law of gravitation for masses (also called gravity model) doesn't work so well with people, and that's why they proposed a\u00c2\u00a0 new migration model.\u00c2\u00a0 the main idea of this model is that\u00c2\u00a0 an individual looks for a better job outside his home country and, as such, accepts the job in the closest country that has benefits higher than his home country. each country has a benefit value that\u00c2\u00a0 is a composite measure based on income, working hours, and general employment conditions. one\u00c2\u00a0 take away was that population (& not distance) is the key predictor of mobility fluxes.<\/p>\n giovanni petti of imperial skilfully delivered a very interesting presentation about a project called freeflow<\/a> whose partners include\u00c2\u00a0 UK unis in london, york, kent. in this project, they have collected\u00c2\u00a0 data from sensors placed under speed bumps that measure the number of cars that pass and the amount of time each car has spent on a bump. traffic data has been arranged in a graph and, to identify congested areas, they run a\u00c2\u00a0 community detection algorithm\u00c2\u00a0 on the graph. it turns out that london behaves like one large giant in terms of traffic flow because of\u00c2\u00a0 long-range spatial correlations.<\/p>\n tam\u00c3\u00a1s vicsek studied\u00c2\u00a0 pigeon flocks and, more generally, studied the roles according to which birds tend to fly with each other. the main finding is that each member of a bird flock takes a specific role in a hierarchy, and roles arranged also\u00c2\u00a0 change over time<\/p>\n\n