  This paper proposes a new meta-learning method -- named HARMLESS (HAwkes
Relational Meta LEarning method for Short Sequences) for learning heterogeneous
point process models from short event sequence data along with a relational
network. Specifically, we propose a hierarchical Bayesian mixture Hawkes
process model, which naturally incorporates the relational information among
sequences into point process modeling. Compared with existing methods, our
model can capture the underlying mixed-community patterns of the relational
network, which simultaneously encourages knowledge sharing among sequences and
facilitates adaptive learning for each individual sequence. We further propose
an efficient stochastic variational meta expectation maximization algorithm
that can scale to large problems. Numerical experiments on both synthetic and
real data show that HARMLESS outperforms existing methods in terms of
predicting the future events.
