  Efforts to model viral cascades provide a vital view into how they form and
spread. A range of methods, such as Multivariate Hawkes Processes or network
inference algorithms, attempt to decompose cascades into constituent components
via inference -- by constructing an underlying network of influence, or by
generating direct pairwise influence measures between cascade participants.
While these approaches provide detailed models of the generative mechanics
underlying event sequences, their sophistication often comes at a steep
computational cost that prevents them from being applied to large-scale
datasets. This is particularly the case for Multivariate Hawkes Processes. In
this work, we propose a novel, scalable method for generating individual-level
influence measures across a set of cascades. Across real-world datasets, we
demonstrate the alignment of this approach's calculations with the influence
inferred by established methods, as well as the computational scalability of
this method.
