May 16, 2017
Kimmo Kaski’s talk will take place on May 16 at 11am in seminar room 101.
While Information Communication Technology (ICT) has offered us news ways to communicate and socially interact, it leaves behind digital traces of our individual behavior as records of ever-growing datasets. The study of such data using computational analysis and modeling with Network Theory approach can give us unprecedented insight into human sociality and to the structures and processes of social life and the society. This is well-demonstrated by our analysis of the dataset of mobile phone communication-logs, confirming the Granovetterian picture for the social network structure, i.e. being modular showing communities with strong internal ties and weaker external ties linking them. More recently the same dataset, but with additional data of the gender and age of the service subscribers, has allowed us to look at the nature of social interaction in more detail and from a different Dunbarian egocentric perspective. With this we have got a deeper insight into the gender and age-related social behavior patterns and dynamics of close human relationships.
Our analysis results demonstrate sex differences in the gender-bias of preferred relationships that reflect the way the reproductive investment strategies of both sexes change across their lifespan. We have also investigated the influence of seasonally and geographically related daily dynamics of daylight and ambient temperature on human resting patterns and observed two daily inactivity periods in the people’s aggregated mobile phone calling patterns. The nocturnal resting period was found to be influenced by the length of daylight, and that its seasonal variation depends on the latitude. In addition, the duration of the afternoon resting period was found influenced by the temperature, above certain threshold, and that the yearly dynamics of the afternoon and nocturnal resting periods appear to be counterbalancing each other.
These empirical findings inspired us to take the next step in network theory, namely developing models to catch some salient features of social networks and processes of human sociality. One of our first models, based on network sociology mechanisms for making friends, turned out to produce many empirically observed Granovetterian features of social networks, like meso-scale community and macro-scale topology formation. The modeling has subsequently been extended to take into account social networks being layered, multiplexing or context based, geography dependent, and having relationships between people changing in time. To summarize we believe that social physics approach to social systems opens up a new perspective to gain understanding of human sociality.