Complex system simulation has been playing an irreplaceable role in understanding,
predicting, and controlling diverse complex systems. In the past few decades, the multi-scale …

Emergence and causality are two fundamental concepts for understanding complex
systems. They are interconnected. On one hand, emergence refers to the phenomenon …

Is reduction always a good scientific strategy? The existence of the special sciences above
physics suggests not. Previous research has shown that dimensionality reduction …

The connectivity of a network contains information about the relationships between nodes,
which can denote interactions, associations, or dependencies. We show that this information …

Accurate demand forecasting is critical to hotel revenue management and related decision-
making. Considering the heterogeneity and dynamics of spatial effects across different time …

Quantifying emergence and modeling emergent dynamics in a data-driven manner for
complex dynamical systems is challenging due to the fact that emergent behaviors cannot …

The internal workings of biological systems are notoriously difficult to understand. Due to the
prevalence of noise and degeneracy in evolved systems, in many cases the workings of …

Community detection and graph clustering are essential for unsupervised data exploration
and understanding the high-level organisation of networked systems. Recently, graph …

Understanding noise in networks and finding the right scale to represent a system are
important problems in network biology. Most research focuses on the raw, micro‐scale …

The biological sciences span many spatial and temporal scales in attempts to understand
the function and evolution of complex systems-level processes, such as embryogenesis. It is …