Description:

Adaptive networks are a class of dynamical network models in which both the states of nodes/edges and the structure of the network itself interact with each other and co-evolve at similar time scales. They have been actively studied since the mid-2000s and are now considered one of the major modeling frameworks in network science and statistical physics. Meanwhile, this line of research actually had quite diverse origins, and its application areas are also expanding to address questions related to people's behaviors, social media, temporal network analysis, and even biological/neural network data. This talk aims to present a brief historical overview of adaptive network research, from the presenter's own personal perspective, and discuss its potential future directions.

 Hiroki Sayama , Professor Systems Science and Industrial Engineering, Binghamton University

Host: Adilson Motter