Statistical inference links data and theory in network science
The number of network science applications across many different fields has been rapidly
increasing. Surprisingly, the development of theory and domain-specific applications often …
increasing. Surprisingly, the development of theory and domain-specific applications often …
Learning dynamics from large biological data sets: machine learning meets systems biology
In the past few decades, mathematical models based on dynamical systems theory have
provided new insight into diverse biological systems. In this review, we ask whether the …
provided new insight into diverse biological systems. In this review, we ask whether the …
Learning interpretable dynamics of stochastic complex systems from experimental data
Complex systems with many interacting nodes are inherently stochastic and best described
by stochastic differential equations. Despite increasing observation data, inferring these …
by stochastic differential equations. Despite increasing observation data, inferring these …
Autonomous inference of complex network dynamics from incomplete and noisy data
The availability of empirical data that capture the structure and behaviour of complex
networked systems has been greatly increased in recent years; however, a versatile …
networked systems has been greatly increased in recent years; however, a versatile …
Network properties determine neural network performance
Abstract Machine learning influences numerous aspects of modern society, empowers new
technologies, from Alphago to ChatGPT, and increasingly materializes in consumer products …
technologies, from Alphago to ChatGPT, and increasingly materializes in consumer products …
Full reconstruction of simplicial complexes from binary contagion and Ising data
Previous efforts on data-based reconstruction focused on complex networks with pairwise or
two-body interactions. There is a growing interest in networks with higher-order or many …
two-body interactions. There is a growing interest in networks with higher-order or many …
Predicting network dynamics without requiring the knowledge of the interaction graph
A network consists of two interdependent parts: the network topology or graph, consisting of
the links between nodes and the network dynamics, specified by some governing equations …
the links between nodes and the network dynamics, specified by some governing equations …
Bayesian parameter estimation for dynamical models in systems biology
Dynamical systems modeling, particularly via systems of ordinary differential equations, has
been used to effectively capture the temporal behavior of different biochemical components …
been used to effectively capture the temporal behavior of different biochemical components …
Systematic errors in connectivity inferred from activity in strongly recurrent networks
Understanding the mechanisms of neural computation and learning will require knowledge
of the underlying circuitry. Because it is difficult to directly measure the wiring diagrams of …
of the underlying circuitry. Because it is difficult to directly measure the wiring diagrams of …
Spatio-temporal breather dynamics in microcomb soliton crystals
Solitons, the distinct balance between nonlinearity and dispersion, provide a route toward
ultrafast electromagnetic pulse sha**, high-harmonic generation, real-time image …
ultrafast electromagnetic pulse sha**, high-harmonic generation, real-time image …