| Title | : | Social Physics: Data-Driven Discovery of Social Connectome |
| Speaker | : | Kimmo Kaski (Aalto University, Finland) |
| Details | : | Tue, 21 Feb, 2017 5:15 PM @ BSB 361 |
| Abstract: | : | Abstract: As Information Communication Technology (ICT) has become to the hands of practically every one of us it keeps molding our social interactions in many unprecedented ways, the events of which leave behind digital traces of our individual behavior as ever-growing datasets allowing us to empirically understand the structures and processes of social life better. The study of such data using computational analysis and modeling with Network Theory approach can give us unprecedented insight into human sociality. 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 has allowed us to look at the nature of social interaction in more detail and from a different Dunbarian egocentric perspective, due to it including demographic data in the form of gender and age information of individual service subscribers. 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 the lifespan, in particular women's shifting patterns of investment in reproduction and parental care, i.e. showing grand-mothering behavior. 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. This modeling approach 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 totally new perspective to gain understanding of human sociality. |
