NetworkHawkesProcesses.jl

NetworkHawkesProcesses.jl implements methods to simulate and estimate a class of probabilistic models that combines mutually-exciting Hawkes processes with network structure. It allows researchers to construct such models from a flexible set of model components, run inference from a list of compatible methods (including maximum-likelihood estimation, Markov chain Monte Carlo sampling, and variational inference), and explore results with visualization and diagnostic utilities.

NetworkHawkesProcesses.jl is a pure Julia framework for defining, simulating, and performing inference on a class of probabilistic models that permit simultaneous inference on the structure of a network and its event generating process—the network Hawkes processes (Linderman, 2016). The event generating process is assumed to follow an auto-regressive, multi-variate Poisson process known as a Hawkes process. Connections between nodes—the network "structure"—are assumed to follow any standard network model (i.e., independent connections). Combining these models provides a disciplined method for discovering latent network structure from event data observed in neuroscience, finance, and beyond.

Package features

• Supports continuous and discrete processes
• Uses modular design to support extensible components
• Implements simulation via Poisson thinning
• Provides multiple estimation/inference methods
• Supports a wide range of network specifications
• Supports non-homogeneous baselines
• Accelerates methods via Julia's built-in Threads module