Meta Learning with Relational Information for Short Sequences
This addresses the challenge of modeling short event sequences with relational data, which is incremental as it builds on existing Hawkes process methods.
The paper tackles the problem of learning heterogeneous point process models from short event sequence data with relational networks by proposing HARMLESS, a hierarchical Bayesian mixture Hawkes process model that incorporates relational information. Experiments on synthetic and real data show it outperforms existing methods in predicting future events.
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.