Recent generative unlearning models synthesize high quality samples while protecting private information by unlearning the identity. However, existing generative identity unlearning methods face two challenges in multi-identity unlearning: 1) identity conflicts, which cause conflicts of model parameters in the continuous erasure of multiple identities; 2) fragile unlearning, where the model's unlearning ability deteriorates or fails under malicious attacks. In this paper, we introduce a critical yet under-explored task called robust multi-identity unlearning, with the goals of resolving identity conflicts to achieve interference-free unlearning and protecting against malicious attacks to achieve robust unlearning. To satisfy these goals, we propose a novel framework, RObust generatiVE continual identity unlearning against Relearning attacks (ROVER). By filtering unlearning requests with latent similarity, our method effectively isolates benign unlearning from malicious attacks to preserve identity removal integrity. Meanwhile, residual orthogonal resonator resolves identity conflicts in the continuous erasure of multiple identities, preserving stability in benign continual unlearning. Moreover, we introduce the phantom guard network to block malicious attacks by absorbing adversarial gradients, ensuring irreversible identity unlearning. The extensive experiments demonstrate that our proposed method achieves state-of-the-art performance on the task of robust multi-identity unlearning against relearning attacks.

Graph neural networks (GNNs) for fraud detection has received extensive attention, where malicious behaviors often exhibit complex relational patterns. Despite their success, the GNN architecture design of existing graph-based fraud detection methods requires significant manual work and expert knowledge. The application of manually designed architectures to diverse real-world scenarios remains a huge time cost, as it requires numerous parameter tuning for varying conditions. Moreover, the GNN-based methods suffer from the over-smoothing problem during multi-layer message passing, which limits the performance in the fraud detection task. To address these problems, we propose the Automatic lightning decoupled Graph neural architecture search for Fraud Detection (AutoGFD). Specifically, AutoGFD designs the decoupled search algorithm to automatically construct the optimal architecture from the specialized architecture search space for the fraud detection task, which can effectively solve the over-smoothing problem. In addition, AutoGFD designs the lightning search tuning mechanism to improve the efficiency of architecture estimation. As far as we know, AutoGFD is the first attempt to design decoupled architecture search for fraud detection, which can automatically search for optimal architectures in different fraud detection scenarios without manual design and expert knowledge. The experimental results based on multiple benchmark datasets show that AutoGFD can achieve significant performance advantages over state-of-the-art baseline methods.

Graph fraud detection has garnered significant attention as Graph Neural Networks (GNNs) have proven effective in modeling complex relationships within multimodal data. However, existing graph fraud detection methods typically use preprocessed node embeddings and predefined graph structures to reveal fraudsters, which ignore the rich semantic cues contained in raw textual information. Although Large Language Models (LLMs) exhibit powerful capabilities in processing textual information, it remains a significant challenge to perform multimodal fusion of processed textual embeddings with graph structures. In this paper, we propose a Multi-level LLM Enhanced Graph Fraud Detection framework called MLED. In MLED, we utilize LLMs to extract external knowledge from textual information to enhance graph fraud detection methods. To integrate LLMs with graph structure information and enhance the ability to distinguish fraudsters, we design a multi-level LLM enhanced framework including type-level enhancer and relation-level enhancer. One is to enhance the difference between the fraudsters and the benign entities, the other is to enhance the importance of the fraudsters in different relations. The experiments on four real-world datasets show that MLED achieves state-of-the-art performance in graph fraud detection as a generalized framework that can be applied to existing methods.

Heterogeneous temporal graphs (HTGs) are ubiquitous data structures in the real world. Recently, to enhance representation learning on HTGs, numerous attention-based neural networks have been proposed. Despite these successes, existing methods rely on a decoupled temporal and spatial learning paradigm, which weakens interactions of spatio-temporal information and leads to a high model complexity. To bridge this gap, we propose a novel learning paradigm for HTGs called Simple and Efficient Heterogeneous Temporal Graph Neural Network (SE-HTGNN). Specifically, we innovatively integrate temporal modeling into spatial learning via a novel dynamic attention mechanism, which substantially reduces model complexity while enhancing discriminative representation learning on HTGs. Additionally, to comprehensively and adaptively understand HTGs, we leverage large language models to prompt SE-HTGNN, enabling the model to capture the implicit properties of node types as prior knowledge. Extensive experiments demonstrate that SE-HTGNN achieves up to 10× speed-up over the state-of-the-art and latest baseline while maintaining the best forecasting accuracy.

In lossless data center networks (DCN), many existing congestion control schemes are used to address the impact caused by priority-based flow control (PFC), such as congestion spreading, and victim flow problems. However, in some special cases, this problem is not solved. Through observation, we examine the interaction between flow control and congestion control, and realize that the mismatch between hop-by-hop flow control and end-to-end congestion feedback, as well as inaccurate rate regulation, are the root causes of frequent PFC triggering. Therefore, we propose Egress Queue Congestion Information Notification (EQCIN). EQCIN implements threshold-based flow identification to avoid packet buildup due to congestion spreading being considered as the root cause of congestion, while using direct feedback from the congestion side to reduce unnecessary link loss. For different flow identifiers, EQCIN adopts different algorithms to achieve targeted rate control. Experimental results show that EQCIN can reduce the number of PFC PAUSEs tends to zero, compared to TIMELY, DCQCN, DCQCN+TCD and improve the link utilization by 7%-77%, respectively.