Graph-informed Neural Point Process With Monotonic Nets

Multi-class event data is ubiquitous in real-world applications. The recent neural temporal point processes have used monotonic nets to model the cumulative conditional intensity to avoid an intractable integration in the likelihood. While successful, they are restricted to single-type events and easily sink into poor learning results. To address these limitations and exploit valuable structural information within event participants, we develop a Graph-Informed Neural Point Process (GINPP) that can freely handle multiple event types, greatly improve learning efficiency with the monotonic net, and effectively integrate the graph information to facilitate training. First, we find the bottleneck of the previous model arises from the standard soft-plus transformation over the output of the monotonic net, which greatly enlarges the prediction variations of the monotonic net and increases the training challenge. We propose a shift-scale version that can significantly reduce the variation and promote learning efficiency. Second, we use a conditional mark distribution to model multiple event types, without the need for explicitly estimating the intensity for each type. The latter can be much more challenging. Third, we use random walks to collect the neighborhood of each event participant and use an attention mechanism to update the hidden state of each participant according to the observed events of both the participant itself and its neighborhood. In this way, we can effectively leverage the graph knowledge, and scale up to large graphs. We have shown the advantage of our approach in both ablation studies and real-world applications.