Semi-supervised graph anomaly detection confronts the fundamental challenge of identifying anomalous nodes that exhibit deviations from normal graph patterns in node attributes or structure connectivity, using a small set of labeled normal nodes. Existing methods fall short in modeling the spectrum of anomaly severity, as they generate anomalies in a single, undifferentiated step. This oversight restricts the detection of complex anomalies. In this paper, we propose a progressive anomaly generation network (PAGE) to overcome this limitation. PAGE enhances complex anomaly detection through progressive attribute-structure perturbation: It first injects attribute noise into selected normal nodes to generate pseudo-anomalous nodes. Then it applies progressive structure perturbation to create hybrid anomalies that simulate the evolution from mild to complex perturbations. Importantly, the proposed anomaly progressive constraint loss enforces that hybrid anomalies exhibit a higher degree of abnormality than primary anomalies, enhancing the model's anomaly quantification capability. PAGE further integrates deviation constraint loss and classification loss to optimize node representations and improve prediction. Extensive experiments on benchmark datasets demonstrate that PAGE significantly outperforms state-of-the-art methods, demonstrating its effectiveness for graph anomaly detection.

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 \textit{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.

In the vanilla zero-shot learning (ZSL) paradigm, category attributes is the key for knowledge generalizable transfer from seen to unseen classes. By contrast, the current contrastive language-image pretraining (CLIP) model relies on the category names to achieve a more general ZSL-like prediction. When vanilla ZSL meets general CLIP, however, most existing methods on both sides struggle to benefit from each other. In this brief, we resort to attribute prompt tuning (APT) for improving the knowledge transferability from the pretrained CLIP model to the downstream ZSL framework for pursuing desirable feature representations. Our approach, termed as attribute prompt alignment network (APAN), leverages APT for cross-network feature alignment (CFA). In this way, we can investigate the effects of CLIP to vanilla ZSL task in the era of large model by the two branch APAN architecture. Specifically, APT takes as an input the templates of class attribute descriptions to produce attribute prompts, which are further used to both guide the localizations of visual regions across two frozen feature extraction networks, through a visual-semantic interaction attention. This enables APAN to progressively refine and align these cross-network features, thus resulting in generalizable feature representations that can capture fine-grained attribute information. For CFA, we simply introduce prediction alignment loss that constrains the predictions from these two cross-network visual features. Experimental results on three benchmark datasets well demonstrate that APAN outperforms the state-of-the-art methods by absorbing generalizable knowledge from CLIP models.

Zero-shot node classification is a vital task in the field of graph data processing, aiming to identify nodes of classes unseen during the training process. Prediction bias is one of the primary challenges in zero-shot node classification, referring to the model's propensity to misclassify nodes of unseen classes as seen classes. However, most methods introduce external knowledge to mitigate the bias, inadequately leveraging the inherent cluster information within the unlabeled nodes. To address this issue, we employ spectral analysis coupled with learnable class prototypes to discover the implicit cluster structures within the graph, providing a more comprehensive understanding of classes. In this paper, we propose a spectral approach for zero-shot node classification (SpeAr). Specifically, we establish an approximate relationship between minimizing the spectral contrastive loss and performing spectral decomposition on the graph, thereby enabling effective node characterization through loss minimization. Subsequently, the class prototypes are iteratively refined based on the learned node representations, initialized with the semantic vectors. Finally, extensive experiments verify the effectiveness of the SpeAr, which can further alleviate the bias problem.

Compositional zero-shot learning (CZSL) aims to distinguish images from unseen compositional classes, which consist of state and object concepts that individually appear in some seen compositional images. The key challenge of CZSL is how to effectively mitigate the contextuality issue for achieving a desirable compositional transfer from seen classes to unseen ones. In CZSL, the visual appearances of the same state are inconsistent when combined with different objects. To address the above dilemma, we propose a swap-reconstruction autoencoder (SRA) to capture the intrinsic context of the ambiguous states. Specifically, SRA learns a consistent embedding space for multi-modal data. A swap-reconstruction mechanism is designed to disentangle the visual embedding of states and objects. The loss including a superclass-oriented state swap-reconstruction loss and object swap-reconstruction loss model the contextual relationship between states and objects. Extensive experiments demonstrate that SRA outperforms current state-of-the-art methods on the three benchmark datasets.