HEP is a hierarchical point process model that superposes time-evolving excitation kernels to capture stimulus-driven event times and clusters latent response dynamics via likelihood inference.
B Proof of Theorem 1 Proof
3 Pith papers cite this work. Polarity classification is still indexing.
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UNVERDICTED 3representative citing papers
ULSE extends spectral embedding using normalized Laplacians with proven cross-sectional and longitudinal stability plus a dynamic Cheeger inequality under dynamic stochastic block models.
Introduces DMPRDPG model and DUASE embedding for dynamic multiplex graphs with consistency and asymptotic normality results, plus applications to real networks.
citing papers explorer
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Hierarchical excitatory processes for modelling event-time data in the presence of exogenous stimuli
HEP is a hierarchical point process model that superposes time-evolving excitation kernels to capture stimulus-driven event times and clusters latent response dynamics via likelihood inference.
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Unfolded Laplacian Spectral Embedding: A Theoretically Grounded Approach to Dynamic Network Representation
ULSE extends spectral embedding using normalized Laplacians with proven cross-sectional and longitudinal stability plus a dynamic Cheeger inequality under dynamic stochastic block models.
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Doubly unfolded adjacency spectral embedding of dynamic multiplex graphs
Introduces DMPRDPG model and DUASE embedding for dynamic multiplex graphs with consistency and asymptotic normality results, plus applications to real networks.