Networks provide a powerful mathematical representation for many real-world complex systems. We investigate fundamental statistical and dynamical properties of complex networks, including topological scaling laws, percolation phenomena, network controllability and observability, temporal networks, and collective swarm dynamics. Our work covers network reconstruction, coarse‑graining, and spreading dynamics on heterogeneous and spatially embedded networks. We aim to uncover universal principles governing how structure shapes collective behaviours across diverse natural and artificial systems.
Representative works appear in Physical Review Letters, Nature Physics, Communications Physics and other journals.
We develop interpretable machine‑learning frameworks for complex‑system science. One central direction is dynamical inference: reconstructing hidden stochastic differential equations from noisy, incomplete observational time‑series data (e.g., the LaGNA / Langevin Graph‑network framework). Another key topic is the early quantitative prediction of critical transitions (tipping points) in nonlinear networked dynamical systems. These tools are validated and applied to ecological systems, social spreading phenomena, and biological dynamical processes.
Relevant publications can be found in Nature Computational Science, Nature Communications, Physical Review X, NeurIPS, AAAI.
We study structural‑functional relationships in brain and neuronal connectomes, uncovering geometric scaling rules embedded in neural wiring. We also explore brain‑inspired artificial neural‑network architectures, collective biological swarm behaviours, and computational neuroscience problems. Our goal is to understand how network anatomy gives rise to system‑level functions in living systems and to translate these insights toward artificial‑intelligence designs.
Major results have been published in Nature, PNAS Nexus, Communications Physics.
