mlsanityData to 14 Oct 2026

Cardinality-Regularized Hawkes-Granger Model

arXiv 2208.10671 ↗First posted 23 Aug 2022cs.LG cs.AI stat.MEcited by 14

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Abstract (arXiv)

We propose a new sparse Granger-causal learning framework for temporal event data. We focus on a specific class of point processes called the Hawkes process. We begin by pointing out that most of the existing sparse causal learning algorithms for the Hawkes process suffer from a singularity in maximum likelihood estimation. As a result, their sparse solutions can appear only as numerical artifacts. In this paper, we propose a mathematically well-defined sparse causal learning framework based on a cardinality-regularized Hawkes process, which remedies the pathological issues of existing approaches. We leverage the proposed algorithm for the task of instance-wise causal event analysis, where sparsity plays a critical role. We validate the proposed framework with two real use-cases, one from the power grid and the other from the cloud data center management domain.

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 12 of its 52 references that are in the map. First posted on arXiv 23 Aug 2022.

Builds on

12 of 52 references.

References · 12

  1. 2020Transformer Hawkes Processcited by 235
  2. 2019Fully Neural Network based Model for General Temporal Point Processescited by 122
  3. 2017Modeling The Intensity Function Of Point Process Via Recurrent Neural Networkscited by 93
  4. 2016Learning Granger Causality for Hawkes Processescited by 69
  5. 2016Uncovering Causality from Multivariate Hawkes Integrated Cumulantscited by 39
  6. 2020Neural Temporal Point Processes For Modelling Electronic Health Recordscited by 39
  7. 2016Expecting to be HIP: Hawkes Intensity Processes for Social Media Popularitycited by 26
  8. 2015Sparse and low-rank multivariate Hawkes processescited by 19
  9. 2020Learning Multivariate Hawkes Processes at Scalecited by 5
  10. 2019Hawkes Process for Understanding the Influence of Pathogenic Social Media Accountscited by 4
  11. 2020Network Hawkes Process Models for Exploring Latent Hierarchy in Social Animal Interactionscited by 4
  12. 2016HNP3: A Hierarchical Nonparametric Point Process for Modeling Content Diffusion over Social Mediacited by 3

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 1; 2023: 6; 2024: 2; 2025: 4; 2026: 1.

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  1. 2023Causal Discovery from Temporal Data: An Overview and New Perspectivescited by 43
  2. 2024MECD: Unlocking Multi-Event Causal Discovery in Video Reasoningcited by 23
  3. 2023Structural Hawkes Processes for Learning Causal Structure from Discrete-Time Event Sequencescited by 14
  4. 2024Learning Granger Causality from Instance-wise Self-attentive Hawkes Processescited by 7
  5. 2022Granger Causal Chain Discovery for Sepsis-Associated Derangements via Continuous-Time Hawkes Processescited by 6
  6. 2023Granger Causal Inference in Multivariate Hawkes Processes by Minimum Message Lengthcited by 3
  7. 2025Advances in Temporal Point Processes: Bayesian, Neural, and LLM Approachescited by 3
  8. 2023Causal Graph Discovery from Self and Mutually Exciting Time Seriescited by 1
  9. 2023Accelerating Cutting-Plane Algorithms via Reinforcement Learning Surrogatescited by 0
  10. 2023Explainable History Distillation by Marked Temporal Point Processcited by 0
  11. 2025Causal Structure Learning in Hawkes Processes with Complex Latent Confounder Networkscited by 0
  12. 2025Learning Marked Temporal Point Process Explanations based on Counterfactual and Factual Reasoningcited by 0
2 more
  1. 2025Addressing Mark Imbalance in Integration-free Neural Marked Temporal Point Processescited by 0
  2. 2026Resilient Concurrent Causal Discovery for Topological Event Sequencescited by 0