mlsanityData to 7 Oct 2026

Directed Acyclic Graph Structure Learning from Dynamic Graphs

arXiv 2211.17029 ↗First posted 30 Nov 2022cs.LG cs.AIcited by 5

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Abstract (arXiv)

Estimating the structure of directed acyclic graphs (DAGs) of features (variables) plays a vital role in revealing the latent data generation process and providing causal insights in various applications. Although there have been many studies on structure learning with various types of data, the structure learning on the dynamic graph has not been explored yet, and thus we study the learning problem of node feature generation mechanism on such ubiquitous dynamic graph data. In a dynamic graph, we propose to simultaneously estimate contemporaneous relationships and time-lagged interaction relationships between the node features. These two kinds of relationships form a DAG, which could effectively characterize the feature generation process in a concise way. To learn such a DAG, we cast the learning problem as a continuous score-based optimization problem, which consists of a differentiable score function to measure the validity of the learned DAGs and a smooth acyclicity constraint to ensure the acyclicity of the learned DAGs. These two components are translated into an unconstraint augmented Lagrangian objective which could be minimized by mature continuous optimization techniques. The resulting algorithm, named GraphNOTEARS, outperforms baselines on simulated data across a wide range of settings that may encounter in real-world applications. We also apply the proposed approach on two dynamic graphs constructed from the real-world Yelp dataset, demonstrating our method could learn the connections between node features, which conforms with the domain knowledge.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 16 of its 45 references that are in the map. First posted on arXiv 30 Nov 2022.

Builds on

16 of 45 references.

References · 16

  1. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  2. 2018DAGs with NO TEARS: Continuous Optimization for Structure Learningcited by 745
  3. 2020Temporal Graph Networks for Deep Learning on Dynamic Graphscited by 521
  4. 2019DAG-GNN: DAG Structure Learning with Graph Neural Networkscited by 329
  5. 2019Gradient-Based Neural DAG Learningcited by 227
  6. 2019Learning Sparse Nonparametric DAGscited by 207
  7. 2020DYNOTEARS: Structure Learning from Time-Series Datacited by 198
  8. 2013Learning Bayesian Networks: The Combination of Knowledge and Statistical Datacited by 185
  9. 2013Causal Inference in the Presence of Latent Variables and Selection Biascited by 165
  10. 2013High-dimensional learning of linear causal networks via inverse covariance estimationcited by 86
  11. 2022Debiasing Graph Neural Networks via Learning Disentangled Causal Substructurecited by 83
  12. 2021Generalizing Graph Neural Networks on Out-Of-Distribution Graphscited by 69
4 more
  1. 2013Learning Gaussian Networkscited by 62
  2. 2022Debiased Graph Neural Networks with Agnostic Label Selection Biascited by 25
  3. 2013A Sound and Complete Algorithm for Learning Causal Models from Relational Datacited by 12
  4. 2015Learning Directed Acyclic Graphs with Penalized Neighbourhood Regressioncited by 12

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Cited by year

Citing papers per year: 2024: 4; 2025: 1.

5 papers.

  1. 2024A Comprehensive Survey of Dynamic Graph Neural Networks: Models, Frameworks, Benchmarks, Experiments and Challengescited by 29
  2. 2024Graph Contrastive Invariant Learning from the Causal Perspectivecited by 6
  3. 2025Dynamic Causal Structure Discovery and Causal Effect Estimationcited by 3
  4. 2024LOCAL: Learning with Orientation Matrix to Infer Causal Structure from Time Series Datacited by 2
  5. 2024Score-matching-based Structure Learning for Temporal Data on Networkscited by 1