Towards OOD Generalization in Dynamic Graphs via Causal Invariant Learning

• URL: http://arxiv.org/abs/2603.01626v1
• Date: Mon, 02 Mar 2026 09:00:11 GMT
• Title: Towards OOD Generalization in Dynamic Graphs via Causal Invariant Learning
• Authors: Xinxun Zhang, Pengfei Jiao, Mengzhou Gao, Tianpeng Li, Xuan Guo,
• Abstract summary: Dynamic graph neural networks (DyGNNs) have demonstrated promising capabilities.<n> Dynamic graph OOD generalization is non-trivial due to the following challenges.<n>We propose a Dynamic graph Causal Invariant Learning model for OOD generalization via exploiting intrinsic invariant patterns from a causal view.
• Score: 13.955024397991169
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Although dynamic graph neural networks (DyGNNs) have demonstrated promising capabilities, most existing methods ignore out-of-distribution (OOD) shifts that commonly exist in dynamic graphs. Dynamic graph OOD generalization is non-trivial due to the following challenges: 1) Identifying invariant and variant patterns amid complex graph evolution, 2) Capturing the intrinsic evolution rationale from these patterns, and 3) Ensuring model generalization across diverse OOD shifts despite limited data distribution observations. Although several attempts have been made to tackle these challenges, none has successfully addressed all three simultaneously, and they face various limitations in complex OOD scenarios. To solve these issues, we propose a Dynamic graph Causal Invariant Learning (DyCIL) model for OOD generalization via exploiting invariant spatio-temporal patterns from a causal view. Specifically, we first develop a dynamic causal subgraph generator to identify causal dynamic subgraphs explicitly. Next, we design a causal-aware spatio-temporal attention module to extract the intrinsic evolution rationale behind invariant patterns. Finally, we further introduce an adaptive environment generator to capture the underlying dynamics of distributional shifts. Extensive experiments on both real-world and synthetic dynamic graph datasets demonstrate the superiority of our model over state-of-the-art baselines in handling OOD shifts.

Related papers

• Balanced Anomaly-guided Ego-graph Diffusion Model for Inductive Graph Anomaly Detection [20.567053994822867]
  Graph anomaly detection is crucial in applications like fraud detection and cybersecurity.<n>We propose a novel data-centric framework that integrates dynamic graph modeling with balanced anomaly synthesis.<n>Our framework features: (1) a discrete ego-graph diffusion model, which captures the local topology of anomalies to generate ego-graphs aligned with anomalous structural distribution, and (2) a curriculum anomaly augmentation mechanism, which dynamically adjusts synthetic data generation during training, focusing on underrepresented anomaly patterns to improve detection and generalization.
  arXiv  Detail & Related papers  (2026-02-05T02:46:54Z)
• Evolving Graph Learning for Out-of-Distribution Generalization in Non-stationary Environments [61.62036321848316]
  Graph neural networks (GNNs) have shown remarkable success in exploiting the spatial and temporal patterns on dynamic graphs.<n>Existing GNNs exhibit poor ability under distribution shifts, which is inevitable in dynamic scenarios.<n>This paper proposes Evolving Graph Learning framework for evolving graph generalization (Evoal) by environment-aware invariant pattern recognition.
  arXiv  Detail & Related papers  (2025-11-04T08:22:29Z)
• Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization [41.58330883016538]
  We study the out-of-distribution (OOD) generalization on dynamic graphs from the environment learning perspective. We propose a Environment-Aware dynamic Graph LEarning (EAGLE) framework for OOD generalization by modeling complex environments and exploiting novel graph-temporalvariant patterns. To the best of our knowledge, we are the first to study OOD generalization on dynamic graphs from the environment learning perspective.
  arXiv  Detail & Related papers  (2023-11-18T16:31:10Z)
• PGODE: Towards High-quality System Dynamics Modeling [40.76121531452706]
  This paper studies the problem of modeling multi-agent dynamical systems, where agents could interact mutually to influence their behaviors. Recent research predominantly uses geometric graphs to depict these mutual interactions, which are then captured by graph neural networks (GNNs) We propose a new approach named Prototypical Graph ODE to address the problem.
  arXiv  Detail & Related papers  (2023-11-11T12:04:47Z)
• GADY: Unsupervised Anomaly Detection on Dynamic Graphs [18.1896489628884]
  We propose a continuous dynamic graph model to capture the fine-grained information, which breaks the limit of existing discrete methods. For the second challenge, we pioneer the use of Generative Adversarial Networks to generate negative interactions. Our proposed GADY significantly outperforms the previous state-of-the-art method on three real-world datasets.
  arXiv  Detail & Related papers  (2023-10-25T05:27:45Z)
• SEGNO: Generalizing Equivariant Graph Neural Networks with Physical Inductive Biases [66.61789780666727]
  We show how the second-order continuity can be incorporated into GNNs while maintaining the equivariant property. We also offer theoretical insights into SEGNO, highlighting that it can learn a unique trajectory between adjacent states. Our model yields a significant improvement over the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-08-25T07:15:58Z)
• Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs [65.18780403244178]
  We propose a continuous model to forecast Multivariate Time series with dynamic Graph neural Ordinary Differential Equations (MTGODE) Specifically, we first abstract multivariate time series into dynamic graphs with time-evolving node features and unknown graph structures. Then, we design and solve a neural ODE to complement missing graph topologies and unify both spatial and temporal message passing.
  arXiv  Detail & Related papers  (2022-02-17T02:17:31Z)
• Invariance Principle Meets Out-of-Distribution Generalization on Graphs [66.04137805277632]
  Complex nature of graphs thwarts the adoption of the invariance principle for OOD generalization. domain or environment partitions, which are often required by OOD methods, can be expensive to obtain for graphs. We propose a novel framework to explicitly model this process using a contrastive strategy.
  arXiv  Detail & Related papers  (2022-02-11T04:38:39Z)
• Handling Distribution Shifts on Graphs: An Invariance Perspective [78.31180235269035]
  We formulate the OOD problem on graphs and develop a new invariant learning approach, Explore-to-Extrapolate Risk Minimization (EERM) EERM resorts to multiple context explorers that are adversarially trained to maximize the variance of risks from multiple virtual environments. We prove the validity of our method by theoretically showing its guarantee of a valid OOD solution.
  arXiv  Detail & Related papers  (2022-02-05T02:31:01Z)
• Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective [72.55093886515824]
  We introduce a causal formalism of motion forecasting, which casts the problem as a dynamic process with three groups of latent variables. We devise a modular architecture that factorizes the representations of invariant mechanisms and style confounders to approximate a causal graph. Experiment results on synthetic and real datasets show that our three proposed components significantly improve the robustness and reusability of the learned motion representations.
  arXiv  Detail & Related papers  (2021-11-29T18:59:09Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.