Hyper Hawkes Processes: Interpretable Models of Marked Temporal Point Processes

2025-11-02

AI Summary

Existing marked temporal point process (MTPP) models face a fundamental trade-off between interpretability and predictive performance: Hawkes processes offer clear causal interpretations but limited expressiveness, whereas neural MTPPs achieve strong prediction accuracy at the cost of transparency. To bridge this gap, we propose the Hyper-Hawkes Process (HHP), which embeds the Hawkes framework into a latent space and employs a data-driven hypernetwork to generate time-varying parameters—thereby preserving conditional linearity while significantly enhancing modeling capacity and intrinsic interpretability. HHP jointly predicts event times and types and enables structured attribution analysis to quantify event influence pathways. Evaluated on multiple benchmark datasets, HHP achieves state-of-the-art predictive performance while providing precise, interpretable insights into underlying generative mechanisms—demonstrating its dual advantage of high accuracy and faithful interpretability in both synthetic and real-world scenarios.

Technology Category

• Machine Learning: Time-Series/Data Streams
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Foundational marked temporal point process (MTPP) models, such as the Hawkes process, often use inexpressive model families in order to offer interpretable parameterizations of event data. On the other hand, neural MTPPs models forego this interpretability in favor of absolute predictive performance. In this work, we present a new family MTPP models: the hyper Hawkes process (HHP), which aims to be as flexible and performant as neural MTPPs, while retaining interpretable aspects. To achieve this, the HHP extends the classical Hawkes process to increase its expressivity by first expanding the dimension of the process into a latent space, and then introducing a hypernetwork to allow time- and data-dependent dynamics. These extensions define a highly performant MTPP family, achieving state-of-the-art performance across a range of benchmark tasks and metrics. Furthermore, by retaining the linearity of the recurrence, albeit now piecewise and conditionally linear, the HHP also retains much of the structure of the original Hawkes process, which we exploit to create direct probes into how the model creates predictions. HHP models therefore offer both state-of-the-art predictions, while also providing an opportunity to ``open the box'' and inspect how predictions were generated.

Problem

• Developing interpretable marked temporal point process models
• Balancing predictive performance with model interpretability
• Extending Hawkes processes with latent space and hypernetworks

Innovation

• Expands Hawkes process into latent space
• Introduces hypernetwork for dynamic parameters
• Retains interpretability via piecewise linear recurrence