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IJCAI 2026

Adversarial Masked Graph Modeling for Robust Graph Autoencoders

Qiqi Zhang, Chuanjin Liu, Gen Liu, Chao Li, Zhongying Zhao

Graph Masked AutoEncoders (GMAEs) have achieved notable success in diverse downstream tasks. However, their superior performance usually relies on reliable and unperturbed input graphs. In real-world scenarios, graphs are vulnerable to adversarial attacks (e.g., perturbations on node features or topology). As a result, existing GMAEs tend to overfit these perturbed inputs, leading to poor robustness. Motivated by this, we propose an adversarial masked graph modeling for robust graph autoencoders, named ArmorGAE. Specifically, we first design a dual-consistency graph augmentation method that enriches the graph topology to mitigate structural vulnerabilities. Theoretically, we establish a connection between ArmorGAE and contrastive learning. It demonstrates that our augmentation strategy in GMAEs is equivalent to effectively expanding the set of high-quality positive samples in contrastive learning, providing richer and more reliable supervisory signals. We then present an adversarial masked autoencoder to mitigate model overfitting to the perturbed graph. The generator and discriminator are jointly trained under an adversarial paradigm, where the former aims to recover the underlying true data distribution, while the latter distinguishes reconstructed and real edges. Experimental results on five datasets demonstrate that ArmorGAE outperforms state-of-the-art methods in terms of classification accuracy on clean graphs and adversarial robustness under 16 distinct adversarial scenarios. The codes are publicly available at https://github.com/ZZY-GraphMiningLab/ArmorGAE.

IJCAI 2026

Neural Projection Fusion for Sliced Wasserstein on the Hypersphere

Hongliang Zhang, Jianjun Qian, Lei Luo, Jian Yang

The Spherical Sliced Wasserstein (SSW) distance has emerged as a fundamental tool for comparing probability measures on the hypersphere, with broad applications spanning geology, medical imaging, computer vision, and representation learning. However, its reliance on uniformly sampling a large number of projection directions from the unit hypersphere can lead to suboptimal performance, as many directions are weakly informative and fail to capture salient differences between distributions. We address this limitation through Fusion Stereographic Spherical Sliced Wasserstein (FS3W), a data-adaptive divergence that incorporates distribution-dependent information into projection selection. FS3W uses a neural slicing fusion mechanism to refine prior directions, steering them toward projections aligned with discriminative geometric structures in the data. This adaptive approach produces more expressive and accurate characterizations of distributional discrepancies on the hypersphere. We evaluate FS3W across five diverse tasks, including gradient flows, Earth density estimation, and self-supervised representation learning. Empirical results consistently demonstrate that FS3W outperforms existing spherical optimal transport–based methods.

IJCAI 2026

Dual-Topology Learning with Adaptive Anchors for Multi-View Clustering

Chenglong Zhang, Chao Zhang, Junhao Zhang, Junyi Guan, Xianzhong Zhou, Bo Wang, Huaxiong Li

As a prominent paradigm for large-scale unsupervised learning, anchor-based multi-view clustering aims to reveal the latent structures across heterogeneous data representations with high efficiency. Despite achieving some progress, existing methods typically suffer from the following two limitations. Firstly, they rely on pre-constructed anchors or rigid constraints (e.g., orthogonality), whereas the intrinsic topological correlations among anchors are completely discarded. Secondly, most existing methods either perform clustering directly on the bipartite graph or treat different views equally, thereby failing to capture the global structural information. To this end, the Dual-tOpology learning with adapTive Anchors (DOTA) is proposed, which not only learns the sample-anchor relationship (i.e., bipartite graph) but also preserves the topology structure among anchors, significantly enhancing the discriminability of learned representation while preserving the underlying data manifold. By integrating an adaptive view-weighting strategy to balance view contributions, DOTA derives discriminative global sample embeddings by propagating spectral information through the bipartite graph. Extensive experiments on benchmark datasets demonstrate the superiority of DOTA.

IJCAI 2026

Revisiting Hypernetwork in Model Heterogeneous Personalized Federated Learning

Chen Zhang, Husheng Li, Xiang Liu, Linshan Jiang, Danxin Wang

Recent personalized federated learning research focuses on heterogeneous models across clients. However, existing methods often rely on external data, model decoupling, and partial learning, which makes them sensitive to settings. In contrast, we revisit hypernetworks and leverage their strong generalization ability to propose the first practical method for personalized federated learning. We first propose a model heterogeneous personalized federated learning framework based on hypernetworks, MH-pFedHN, which quantifies clients with different architectures using customized embedding vectors and then generates client-specific model parameters through a server-side hypernetwork. Besides the shared feature extractor, our hypernetwork consists of multiple heads, where clients with similar numbers of parameters are assigned the same number of customized embedding vectors and consequently share the same head. This design enables knowledge sharing across different architectures and reduces the computation of parameter generation. To further enhance the hypernetwork’s learning and generalization, we propose MH-pFedHNGD, which introduces a lightweight yet effective plug-in global model. Our framework requires no external data and does not disclose client model architectures, thereby effectively ensuring security and demonstrating great potential. Experiments across various models and tasks demonstrate that our approach outperforms standard baselines and exhibits strong generalization performance. Our code is available at https://github.com/DangDang1895/MH-pFL

IJCAI 2026

Continuous Test-Time Adaptation via Dual Alignment

Boyuan Zhang, Jie Pen, Shuai Yang, Lichuan Gu

Continual test-time adaptation (CTTA) seeks to adapt a source-pretrained model to continuously shifting target distributions. Recent advances in this field either minimize prediction entropy on streaming target samples, which is efficient but prone to error accumulation, or rely on teacher–student pseudo-labeling, which provides more stable predictions at the cost of high computational overhead. To alleviate these issues, in this paper, we propose Dual Alignment (D-Align), a method that jointly optimizes correlation alignment and masked consistency alignment for stable and efficient online adaptation. Specifically, correlation alignment constructs a reliable pseudo-source domain by selecting resilient target samples, maintaining their diversity via a dynamic memory bank, and aligning nonlinear feature relations through image-level visual domain prompt–driven alignment, enabling effective feature alignment without costly backpropagation. Masked consistency alignment enforces prediction consistency across multiple masked views of each target sample, promoting robust and discriminative target representations. Using multiple standard benchmarks, the experiments have verified the effectiveness of D-Align under dynamic domain shifts, in comparison with state-of-the-art methods.

IJCAI 2026

GBD-DRP: Generalizable Bias-Disentangled Learning for Drug Response Prediction with Missing Modalities

Biyang Zeng, Shikui Tu

Predicting the response of anticancer drugs for specific cancer cell lines remains an important challenge in cancer treatment, largely due to the diversity among cell lines. Existing machine learning methods usually perform well on cell lines seen during training, but their performance often drops when applied to new cell lines. Many approaches rely heavily on cell-line specific features, which limits their ability to generalize. At the same time, methods that focus mainly on generalization often fail to capture differences in drug response across distinct cellular environments. Some studies improve prediction accuracy by introducing additional and more detailed cell line features. However, these features are difficult to collect and are frequently missing, especially for newly studied cell lines. To address these issues, we propose a generalizable bias-disentangled framework for drug response prediction. Our method separates drug-related patterns that are shared across cell lines from representations that reflect cell-line specific environments. We further isolate real but non-generalizable biases that arise from noise or unobserved factors. This design helps preserve accurate predictions for specific cell lines while improving performance on unseen ones. In addition, the bias-related branch supports counterfactual consistency learning when some modalities are missing, allowing the model to handle incomplete data. Experimental results show that GBD-DRP achieves state-of-the-art performance, and additional tests on unseen cell lines and missing-modality settings indicate good generalization in practice.

IJCAI 2026

RODIS: Robust Diffusion Solver to Dataset Quality in Combinatorial Optimization

Hui Yuan, Zhigang Hua, Zihao Li, Qi Xu, Weilin Cong, Yan Xie, Taihui Li, Rong Jin, Shuang Yang, Bo Long

Combinatorial optimization (CO) problems have widespread applications in science and engineering, but they present significant computational challenges. Recent advancements in generative models, particularly diffusion models, have shown promise in bypassing traditional optimization solvers by directly generating near-optimal solutions. However, existing diffusion solvers are highly sensitive to the quality of the training dataset, particularly the number of problem instances for training and the optimality of their labels. Notably, we observe an exponential scaling law between the performance of diffusion-based solvers and the number of near-optimally labeled instances needed. When training instances are scarce or sub-optimally labeled, diffusion-based solvers suffer significant performance degradation. To enhance the robustness of diffusion solvers to dataset quality, we propose a robust diffusion solver for combinatorial optimization capable of learning from sub-optimally labeled instances follows a two-stage generate-then-decode framework, integrating an objective-guided diffusion model, further reinforced by classifier-free guidance, to produce solutions that surpass the optimality of the training dataset. Experiments demonstrate the improved robustness in \myalg compared to the diffusion-based solver baseline, in a range of combinatorial optimization benchmark tasks such as TSP (Traveling Salesman Problem) and MIS (Maximum Independent Set).

IJCAI 2026

From Compression to Construction: Pseudo Neighbor Augmentation Sampling for Dynamic Link Prediction

Zhigang Yu, Hao Yan, Changjun Fan, Senzhang Wang

Dynamic link prediction aims to predict whether two nodes will interact at a future time point in a dynamic graph based on their historical interactions. Existing sampling based methods, which can be considered as a compressor, generally select a subset of one-hop neighbors from the entire interaction sequence for efficiency. It inevitably makes them lose rich context such as high order structural information and temporal patterns, and often underperform on nodes with sparse historical interaction with other nodes. To this aim, we propose a novel context construction based approach named PeNS that adopts Pseudo Neighbor Augmentation Sampling Strategy for more accurate dynamic link prediction. Specifically, PeNS tries to augment neighbor sequences with three complementary types of pseudo neighbors, injecting structural and temporal cues to provide rich context information especially in sparse neighbor scenarios. We further introduce a Streaming Neighbor Memory Module for efficient pseudo neighbor construction and a dual-stream fusion mechanism for robust representation learning. Extensive experiments demonstrate the effectiveness and generalizability of PeNS.

IJCAI 2026

BehaviorGuard: Online Backdoor Defense for Deep Reinforcement Learning

Yinbo Yu, Xueyu Yin, Jiadai Wang, Chunwei Tian, Sai Xu, Qi Zhu, Daoqiang Zhang

Backdoor attacks pose a serious threat to deep reinforcement learning (DRL). Current defenses typically rely on reward anomalies to reverse-engineer triggers and model finetuning to remove backdoors. However, complex trigger patterns undermine their robustness, and fine-tuning entails high costs, limiting practical utility. To this end, we shift defense concerns to trigger-agnostic backdoor output behaviors and propose BehaviorGuard, an online behavior-based backdoor detection and mitigation framework for DRL. Specifically, we find that regardless of attacks, backdoored policies induce consistent shifts in action distributions to ensure reliable activation, leaving detectable traces in high-quantile regions and distribution tails, even in the absence of triggers. Based on this, we design a novel metric that captures behavioral drift in action distributions to identify and suppress backdoor actions at runtime. To our knowledge, this is the first online backdoor defense that counters attacks both in single- and multi-agent DRL. Evaluated across diverse benchmarks with different backdoor attacks, BehaviorGuard consistently surpasses prior methods in both efficacy and efficiency.

IJCAI 2026

Active Arbitration: Decoupling Spatio-Temporal Duality for Efficient Traffic Forecasting

Jiajun Yu, Fang YUAN, Guang-Yong Chen, Min Gan

Spatio-temporal forecasting is inherently bottlenecked by a phenomenon we term Spatio-Temporal Duality: the paradoxical coexistence of time-varying physical lags and instantaneous semantic synchrony. Constrained by passive coupling, existing architectures often employ indiscriminate spatial aggregation that fails to dynamically arbitrate interaction intensity based on traffic states, forcing models to rely on redundant deep stacking to approximate complex dynamics. We address this issue by introducing Time Arbitrated Spatial GNN (TAS-GNN), an efficient framework that arbitrates spatial interactions through time. Our model leverages deep temporal semantics to dynamically and proactively manage the aggregation of spatial domains, effectively decoupling physical connectivity from semantic relevance. In addition, to ensure model simplicity, we also introduce spectral decoupling via discrete wavelet Transform (DWT) to capture multiscale dependencies with minimal overhead. Experiments on three real-world datasets (PEMS04, 07, 08) show that TAS-GNN not only outperforms the current baseline model in accuracy, but also significantly improves the inference speed while reducing the number of parameters.

IJCAI 2026

ILR-SMO: Iterative Latent Refinement for Robust Spatial Multi-Omics Integration

Anqi Yu, Xudong Xu, Jianzhi Lu, Yuqi Sun, Bingguo Chen, Chenxi Ma, Weimin Tan, Bo Yan

Spatial multi-omics technologies jointly profile diverse molecular modalities with spatial context, providing a comprehensive view of cellular heterogeneity and tissue organization. To integrate spatial multi-omics data and identify spatial domains, a wide range of unsupervised methods has been proposed. However, recent approaches rely on single-step fusion, directly aggregating heterogeneous modalities into a shared representation, making embeddings sensitive to modality imbalance and measurement noise that are inherent to spatial multi-omics data, as well as unstable optimization under weak supervision. Here, we propose ILR-SMO (Iterative Latent Refinement for Spatial Multi-Omics), a unified self-supervised framework that reformulates multimodal integration as a stability-aware refinement process over a shared latent representation. Instead of single-step plain fusion, ILR-SMO progressively integrates modality-specific information through sequential updates, allowing information to be injected in a controlled manner. This incremental refinement enables later updates to correct or compensate for earlier deviations, resulting in more stable and robust representation learning under heterogeneous and noisy modalities. ILR-SMO further incorporates reliability-aware modality gating for adaptive modulation of modality contributions, and employs a joint objective to enforce spatial coherence while preventing representation collapse. Extensive experiments on five spatial multi-omics benchmarks demonstrate that ILR-SMO consistently outperforms seven state-of-the-art methods and exhibits strong robustness across diverse settings.

IJCAI 2026

Info-Driven Zero-Cost Proxy: Rethinking Vision Transformer Architecture Evaluation via Information Quantification

Yue Yang, Jiacheng Wang, Zhenkai Yang, Menglan Hu, Gaoyang Liu, bo xu, Tianyue Zheng, Kai Peng

Neural Architecture Search (NAS) automates the design of Vision Transformer (ViT) architectures. However, the high computational cost of training-based methods has made training-free, zero-cost proxies a key research direction. While existing proxies can estimate model potential, they fail to capture critical information transmission characteristics due to the black-box nature of neural networks. Consequently, they suffer from high computational latency and weak correlation with ground-truth performance. Although information transmission determines performance, it has not been effectively quantified or utilized. This limitation remains the core bottleneck for current zero-cost proxies. In this paper, we propose Info-NAS, a zero-cost proxy based on architectural information. This method achieves the first structured quantification of information transmission in ViT. Specifically, it evaluates architectures using three core components: global information volume, local information gradient, and global consistency. Info-NAS derives proxy scores solely from architectural parameters, requiring no forward or backward propagation. Consequently, the calculation and search processes are highly efficient. Moreover, we construct the ViT-Info-Bench dataset to facilitate correlation analysis and algorithm evaluation. Experimental results demonstrate that Info-NAS significantly reduces search overhead while achieving superior ranking accuracy. With a single evaluation requiring only 1.8 ms, Info-NAS outperforms existing methods in both efficiency and performance.

IJCAI 2026

TransAlpha: Lightweight Design Empowers Stock Return Forecasting

Xiao Yang

In intraday stock return forecasting, existing Transformer methods face high computational complexity and do not explicitly handle noisy predictors. We propose TransAlpha, a light-weight Transformer variant tailored for cross section data to advance end-to-end forecasting methods with three novel components (Cross-Sectional Denoising for signal purification, Temporal Attention Gating for trend weighting, and Smart Pooling to avoid information loss) along with a multi-component hybrid loss function. We validate on full A-share market stock data and four key segments (CSI 300/500/1000/2000) with 400 proprietary alpha factors of 15-minute frequencies. TransAlpha outperforms state-of-the-art baselines in predictive power and portfolio profitability, indicating tailored Transformer models have significant practical value in quantitative trade.

IJCAI 2026

Robust Federated Hyperspectral Image Clustering

Xiang Yang, Zhengzhong Zhu, Dayu Hu, Xiaowen Ma, Zihao Li, Wenxuan Tu, Taichun Zhou, Wenxin Zhang, Renxiang Guan

Hyperspectral image (HSI) clustering facilitates the unsupervised discrimination of complex surface materials but traditionally relies on the idealized assumption of centralized data availability. In real-world scenarios, however, this assumption clashes with data privacy regulations and the physical distribution of data across isolated silos. While Federated Learning offers a decentralized solution, existing frameworks in remote sensing are predominantly confined to supervised paradigms and struggle to address the heavy reliance on annotations and Non-IID distributions inherent among clients. To overcome these limitations, we propose a novel framework named Robust Federated HSI Clustering(RFHC) that enables collaborative unsupervised learning without requiring raw data exchange. Specifically, we design a dual-encoder architecture that incorporates a Federated Model Weight Augmentation strategy(FMWA), which generates consistent views through local-global network interactions to mitigate the spectral distortion introduced by traditional data augmentation. Furthermore, we develop a hybrid optimization objective that synergizes prototype relationship optimization with contrastive learning. This mechanism utilizes global prototypes to guide local training, effectively stabilizing feature learning against data heterogeneity while ensuring intra-cluster compactness. Extensive experiments on three benchmark HSI datasets demonstrate the effectiveness and superiority of the proposed method against state-of-the-art model(SOTA) federated approaches.

IJCAI 2026

Toward a More Discriminative Learnware Paradigm via Explicitly Distinctive Specification

Wenlu Yang, Wei Chen, Zhenan He

The learnware paradigm aims to construct a dock system that maintains a collection of learnwares, each consisting of a well-established model coupled with a specification, thereby enabling users to directly reuse existing models to solve their tasks without training models from scratch. As a core component of the paradigm, the specification sketches the model’s properties and establishes reusability between the learnware and user tasks. Existing methods have demonstrated promising performance by designing specifications that characterize the task distributions mastered by well-established models. However, in complex real-world scenarios, task distributions often overlap, thereby weakening the uniqueness of specifications and obscuring the reusability relationships between learnwares and tasks. In this paper, we design an Explicitly Distinctive Specification (EDS) that enforces specification uniqueness, improving the discriminative capability of the learnware paradigm and avoiding ambiguity caused by overlapping task distributions. This specification method explicitly leverages distributional discrepancies between specifications to strengthen the uniqueness of the model properties sketched during the submitting stage and subsequently exploits the enhanced distributional discriminability in the deploying stage to improve learnware reusability. Extensive experiments demonstrate the effectiveness and strong performance of the proposed EDS within the learnware paradigm under complex task scenarios.

IJCAI 2026

PhyRE-Net: A Physics-Anchored Network with Active Spatial Gating for Industrial Infrared Thermal Forecasting

Shuotong Yang, Haitao Zhang

Spatiotemporal forecasting of infrared thermal fields is a critical technology for the predictive maintenance of industrial electrical control equipment. However, many existing methods are designed under index-based temporal representations and global regression objectives, which are not well aligned with schedule-driven, non-stationary industrial regimes and the long-tailed spatial distribution of safety-critical hotspots. To address these issues, this paper proposes PhyRE-Net, a physical-time-anchored network architecture featuring active spatial gating. Specifically, we first design the multi-period resolution rotary positional embeddings mechanism within the backbone, which injects absolute temporal features into attention geometry to align predicted phases with non-stationary production schedules. We then devise a reinforcement learning-driven spatial gating mechanism to dynamically modulate feature responses, actively shifting the computational focus from the dominant background to sparse but critical thermal anomalies. Extensive experiments on a large-scale real-world dataset covering seven types of heterogeneous electrical equipment show that PhyRE-Net achieves consistent improvements over strong baselines across all evaluated metrics. The source code is available at https://github.com/YST-10/PhyRE-Net.

IJCAI 2026

EMMS: Evidential Multi-Label Multi-Dimensional Selection

Li Yang, Yanyong Huang, Jinyuan Chang, Ou Zheng, Minbo Ma, Xiaoyi Jiang

Multi-label data often contain high-dimensional features, outlier instances, and noisy labels, all of which can lead to the curse of dimensionality and decreased performance in downstream tasks. Although numerous data reduction methods have been developed, existing approaches face two major limitations: 1) existing methods typically select features, instances, or labels independently, without considering how noise or redundancy in one dimension may negatively influence the selection of others; 2) there are very few feature and instance co-selection methods that commonly assume label annotations are free of noise, which is seldom true in practice. To address these issues, we propose Evidential Multi-Label Multi-Dimensional Selection (EMMS), which jointly performs feature, instance, and label selection on multi-label data. EMMS introduces a dual projection mechanism with sparsity constraints that transforms high-dimensional data first into a latent space and then into the label space. Simultaneously, projection residuals are explicitly modeled to facilitate the identification of representative instances, enabling unified selection across features, instances, and labels. Moreover, EMMS employs evidence theory to fuse instance-level and label-level evidence, thereby enhancing the reliability of the learned labels and reducing the influence of noisy labels, which in turn promotes multi-dimensional selection. Extensive experiments demonstrate that EMMS consistently outperforms state-of-the-art methods.

IJCAI 2026

ROAD: Adaptive Data Mixing for Offline-to-Online Reinforcement Learning via Bi-Level Optimization

Letian Yang, Xu Liu, Yiqiang Lu, Jian Liu, Weiqiang Wang, Shuai Li

Offline-to-online reinforcement learning harnesses the stability of offline pretraining and the flexibility of online fine-tuning. A key challenge lies in the non-stationary distribution shift between offline datasets and the evolving online policy. Common approaches often rely on static mixing ratios or heuristic-based replay strategies, which lack adaptability to different environments and varying training dynamics, resulting in suboptimal tradeoff between stability and asymptotic performance. In this work, we propose Reinforcement Learning with Optimized Adaptive Data-mixing (ROAD), a dynamic plug-and-play framework that automates the data replay process. We identify a fundamental objective misalignment in existing approaches. To tackle this, we formulate the data selection problem as a bi-level optimization process, interpreting the data mixing strategy as a meta-decision governing the policy performance (outer-level) during online fine-tuning, while the conventional Q-learning updates operate at the inner level. To make it tractable, we propose a practical algorithm using a multi-armed bandit mechanism. This is guided by a surrogate objective which simultaneously maintains offline priors and prevents value overestimation. Our empirical results demonstrate that this approach consistently outperforms existing data replay methods across various datasets, eliminating the need for manual, context-specific adjustments while achieving superior stability and asymptotic performance.

IJCAI 2026

PA-GMAE: A Position-Assigned Graph Masked Autoencoder for Point Cloud Representation Learning

Haifeng Yang, Lupeng Fang, Jianghui Cai, Jie Wang, Guojiao An, Lihua Hu

Masked autoencoder have demonstrated excellent performance in point cloud representation learning and received widespread attention. However, due to the unstructured nature of point clouds, existing methods struggle to effectively model both local geometric information and global topological features, while generally neglecting positional information, resulting in insufficient model representational capacity. To address these challenges, we propose a Position-Assigned Graph Masked Autoencoder (PA-GMAE) framework for point cloud representation learning. Specifically, we first explicitly transform the unstructured point clouds into a graph structure and assigns it positional information, leveraging the structured properties of graph to mine the intrinsic correlations among points. During the encoding stage, global contextual are dynamically injected into node features, endowing the model with capabilities of global awareness. In the decoding stage, collaborative work across multiple decoding tasks fully captures the global topology and positional dependencies of the point cloud, enhancing the quality of learned representations. Extensive experiments are conducted on three authoritative benchmark datasets, namely ScanObjectNN, ModelNet40, and ShapeNetPart, across typical downstream tasks including point cloud classification and part segmentation, the results demonstrate the effectiveness and superiority of the proposed framework.

IJCAI 2026

Beyond Sequences: A Dynamic Hierarchical Heterogeneous Spatio-Temporal Graph for Irregular Multivariate Time Series Forecasting

Xiaowei Yan, Zhuo Li, Junjie Zhang, Bing Li, Jun Yan, Buzhou Tang

Irregular Multivariate Time Series (IMTS) analysis is a challenging task as asynchronous irregular sampling disrupts intra-variable temporal consistency and cross-variable alignment. Most existing methods model multivariate correlations at either the variable or observation level in static ways. They suffer from correlation loss or cross-variable misalignment inevitably. In this paper, we propose a novel method DyH2-STGraph, Dynamic Hierarchical Heterogeneous Spatio-Temporal Graph, for IMTS forecasting. Within the graph, irregular observations are represented as nodes with spatio-temporal coordinates and observed values, connected with other neighbor observation nodes dynamically by spatio-temporal neighbor selection, while variables are treated as hyper nodes, connected with their constituent observation nodes as well as other variable nodes. The multivariate correlations are classified into the following three categories in specific manners: (1) Intra-variable coarse-to-fine correlations between variable nodes and their constituent observation nodes captured by hierarchical message propagation, (2) Inter-variable fine-grained correlations among neighboring observation nodes captured by spatio-temporal attention, and (3) Inter-variable coarse-grained correlations among variable nodes captured by attention. Extensive experiments on four benchmark datasets demonstrate that DyH2-STGraph significantly outperforms state-of-the-art methods while maintaining competitive efficiency.

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