We introduce the concept of programmable feature engineering for time series modeling
and propose a feature programming framework. This framework generates large amounts of …

Stochastic processes on graphs can describe a great variety of phenomena ranging from
neural activity to epidemic spreading. While many existing methods can accurately describe …

The Ising model on networks plays a fundamental role as a testing ground for understanding
cooperative phenomena in complex systems. Here we solve the synchronous dynamics of …

A novel generalized mean field approximation, called the Small-Coupling Dynamic Cavity
(SCDC) method, for epidemic inference and risk assessment is presented. The method is …

We present an alternate method to close the master equation representing the continuous
time dynamics of interacting Ising spins. The method makes use of the theory of random …

We consider classical spin systems evolving in continuous time with interactions given by a
locally tree-like graph. Several approximate analysis methods have earlier been reported …

We introduce and apply an efficient method for the precise simulation of stochastic
dynamical processes on locally treelike graphs. Networks with cycles are treated in the …

Although real-world complex systems typically interact through sparse and heterogeneous
networks, analytic solutions of their dynamics are limited to models with all-to-all …

The Onsager's reciprocal relation plays a fundamental role in the nonequilibrium
thermodynamics. However, unfortunately, its classical version is valid only within a narrow …

Abstract Continuous-time Bayesian networks (CTBNs) constitute a general and powerful
framework for modeling continuous-time stochastic processes on networks. This makes …