The dynamics of complex networks have recently received much attention, with many applications in social, biological, and technical systems. Empirical studies suggest that coevolving dynamics, the interplay between network evolution and nodal dynamics, is responsible for many important phenomena in complex networks. Here, we present two concrete examples of the coevolving dynamics in complex networks. 

For complex social systems, polarization is a ubiquitous phenomenon. We proposed a coevolving framework that counts for opinion dynamics and network evolution simultaneously from the perspective of statistical physics. Under a few generic assumptions on social interactions, we found a bi-polarized community structure emerges naturally from the coevolving dynamics. Our analytical result predicts a depolarization/polarization phase transition, in line with empirical observations. For human-AI interaction systems, recent years have witnessed that AI-driven recommendation algorithms lure the system into information homogeneity, in which individuals are isolated from diverse information and eventually trapped in a single topic or viewpoint. We derive a mechanistic model for the coevolving information dynamics in complex human-AI interaction systems. This allows us to unearth basic mechanisms underlying the information homogeneity theoretically. These two examples demonstrate how coevolving dynamics drive the emergence of complex network structures and non-trivial phenomena from simple mechanisms.

报告人简介

刘家臻，清华大学水木博士后、清华大学电子工程系张克潜冠名博士后。本科毕业于山东大学空间科学与物理专业，导师为姜云国教授。博士毕业于美国迈阿密大学（University of Miami）物理专业，导师为Chaoming Song教授。研究方向为统计物理，侧重于复杂系统和复杂网络理论以及相关交叉学科中的应用研究。科研成果发表在Physical Review Letters、Nature Machine Intelligence、Chinese Physics C等期刊上。其研究成果被Nature Machine Intelligence、Physics Today公开报道。

邀请人

周海军 研究员