With the increasing prevalence of graph data in various practical applications, Graph Neural Networks (GNNs) have established themselves as essential tools for effective graph data processing. However, existing GNNs always perform well on in-distribution data, but exhibit significant performance degradation under distribution shifts. To improve GNN generalization, graph invariant learning aims to identify accurate invariant subgraphs but is constrained by limited diversity in environment subgraphs. Conversely, graph data augmentation focuses on enriching this diversity through environment subgraph augmentation, but its efficacy heavily depends on having accurate invariant subgraphs first. This creates a core paradox: acquiring accurate invariant subgraphs requires diverse data, whereas effective augmentation presupposes accurate invariant subgraphs. To address this issue, we propose IGESA, a method for learning accurate Invariant subGraph via effective Environment Subgraph Augmentation. IGESA introduces two key strategies: (1) For accurate invariant subgraph identification, we propose a precise invariant subgraph extraction strategy to refine the subgraph learning process. (2) For sufficiently diverse augmentations, we propose a cross-graph environment fusion strategy that combines sampled components from pair-wise distinct environment subgraphs to construct new subgraphs. These two strategies are collaboratively optimized to boost GNNs’ generalizability, and extensive experiments on benchmark datasets demonstrate their superiority over state-of-the-art methods.
Yu-Jie Wang, Kui Yu, Xiang Wang et al.· IEEE Transactions on Big Dat...· 0 citations
Offline reinforcement learning (RL) aims to learn optimal policies from static datasets while enhancing generalization to out-of-distribution (OOD) data. To mitigate overfitting to suboptimal behaviors in offline datasets, existing methods often relax constraints on policy and data or extract informative patterns through data-driven techniques. However, there has been limited exploration into structurally guiding the optimization process toward flatter regions of the solution space that offer better generalization. Motivated by this observation, we present FANS , a generalization-oriented structured network framework that promotes flatter and robust policy learning by guiding the optimization trajectory through modular architectural design. FANS comprises four key components: (1) Residual Blocks, which facilitate compact and expressive representations; (2) Gaussian Activation, which promotes smoother gradients; (3) Layer Normalization, which mitigates overfitting; and (4) Ensemble Modeling, which reduces estimation variance. By integrating FANS into a standard actor-critic framework, we highlight that this remarkably simple architecture achieves superior performance across various tasks compared to many existing advanced methods. Moreover, we validate the effectiveness of FANS in mitigating overestimation and promoting generalization, demonstrating the promising potential of architectural design in advancing offline RL.
Da Wang, Yi Ma, Ting Guo et al.· Neural Information Processin...· 0 citations
Hypergraphs offer a natural paradigm for modeling complex systems with multi-way interactions. Hypergraph neural networks (HGNNs) have demonstrated remarkable success in learning from such higher-order relational data. While such higher-order modeling enhances relational reasoning, the effectiveness of hyper-graph learning remains bottlenecked by two persistent challenges: the scarcity of labeled data inherent to complex systems, and the vulnerability to structural noise in real-world interaction patterns. Traditional data augmentation methods, though successful in Euclidean and graph-structured domains, struggle to preserve the intricate balance between node features and hyperedge semantics, often disrupting the very group-wise interactions that define hypergraph value. To bridge this gap, we present HyperMixup, a hypergraph-aware augmentation framework that preserves higher-order interaction patterns through structure-guided feature mixing. Specifically, HyperMixup contains three critical components: 1) Structure-aware node pairing guided by joint feature-hyperedge similarity metrics, 2) Context-enhanced hierarchical mixing that preserves hyperedge semantics through dual-level feature fusion, and 3) Adaptive topology reconstruction mechanisms that maintain hypergraph consistency while enabling controlled diversity expansion. Theoret-ically, we establish that our method induces hypergraph-specific regularization effects through gradient alignment with hyperedge covariance structures, while providing robustness guarantees against combined node-hyperedge perturbations. Comprehensive experiments across diverse hypergraph learning tasks demonstrate consistent performance improvements over state-of-the-art baselines, with particular effectiveness in low-label regimes. The proposed framework advances hypergraph representation learning by unifying data augmentation with higher-order topological constraints, offering both practical utility and theoretical insights for relational machine learning
Kaixuan Yao, Zhuo Li, Jianqing Liang et al.· Neural Information Processin...· 0 citations
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