  Attributed event sequences are commonly encountered in practice. A recent
research line focuses on incorporating neural networks with the statistical
model -- marked point processes, which is the conventional tool for dealing
with attributed event sequences. Neural marked point processes possess good
interpretability of probabilistic models as well as the representational power
of neural networks. However, we find that performance of neural marked point
processes is not always increasing as the network architecture becomes more
complicated and larger, which is what we call the performance saturation
phenomenon. This is due to the fact that the generalization error of neural
marked point processes is determined by both the network representational
ability and the model specification at the same time. Therefore we can draw two
major conclusions: first, simple network structures can perform no worse than
complicated ones for some cases; second, using a proper probabilistic
assumption is as equally, if not more, important as improving the complexity of
the network. Based on this observation, we propose a simple graph-based network
structure called GCHP, which utilizes only graph convolutional layers, thus it
can be easily accelerated by the parallel mechanism. We directly consider the
distribution of interarrival times instead of imposing a specific assumption on
the conditional intensity function, and propose to use a likelihood ratio loss
with a moment matching mechanism for optimization and model selection.
Experimental results show that GCHP can significantly reduce training time and
the likelihood ratio loss with interarrival time probability assumptions can
greatly improve the model performance.
