The inherently social nature of humans and the technologies they develop has throughout history shaped the sociality of individuals and their societies. Information and Communication Technology (ICT) makes this shaping faster affecting the ways we communicate and socially interact, transmit information, handle resources, do innovations, and create new products and services, yet making our socio-economic systems increasingly based on information spreading and cascading. As these systems are complex they show emergent collective behavior that can appear as cultural fads, abrupt stock market fluctuations, failures in infrastructure networks, and political turmoil. In order to the consequences of ICT to be useful and to avoid its adverse effect a deeper understanding to these processes and underlying mechanisms is needed.
This understanding can be sought with computational social science or network science that has emerged as a truly multidisciplinary endeavor of statistical physics, computational science, and sociology. The importance of networks results from their role as scaffolds of complexity. The data deluge or “Big Data” due to ICT and the advancement of the computational capacities have created ideal conditions to study complexity. First the focus has been on the structural aspects of networks leading to number of groundbreaking discoveries of their scale-free character, motifs or sub-graphs, and to development of efficient tools to study them. After this the focus has been turned to the processes on networks and other time-dependent phenomena.
A key assumption behind the research in computational social science is that the quantitative analysis of datasets from large techno- social systems (web, mobile networks and online services) may be used to gather accurate understanding of the structure of social system and the underlying mechanisms of social interaction between individuals in that system. The feasibility of this hypothesis is ever increasing as the rapid development of ICT provides multiple channels for us to be active on and interact socially thus making it our ‘real world’ and its dataset analysis `reality-mining’ of human social behavior.
The general aim of this research proposal is to take the next step in the state-of-the-art research moving from structural–topological aspects of Network Science to investigate Social Dynamics in Networks, focusing the research on Dynamics of networks, Processes on networks and Temporal networks.
This project is funded by Academy of Finland (Project id 2228357-4)
P.I. Prof. Kimmo Kaski
Asim Ghosh, PhD
Kunal Bhattacharya, PhD
Daniel Monsivais Velázquez, MSc
Prof. Robin M. Dunbar (University of Oxford & Aalto Visiting Professor)
Prof. Janos Kertész (Central European University, Budapest & Aalto Visiting Professor)
Prof. Anna Rotkirch (Väestöliitto, Helsinki)
Dr. Tamas-David Barret (Kiel Institute for the World Economy, Kiel)
List of publications: