The rapid spread of fake news on social media has significantly increased the importance of computational detection methods. Graph-based approaches, particularly Graph Neural Networks (GNNs), have emerged as powerful tools for modeling news propagation patterns. Despite their potential, current GNN-based methods still face challenges in robustness and interpretability due to two key shortcomings: they inadequately filter out irrelevant user-induced noise within propagation graphs, and their shallow architectures fail to effectively capture the intricate long-range dependencies characteristic of news propagation. To overcome these limitations, we propose NEGT (Noise-filtering Enhanced Graph Transformer), a novel graph Transformer framework explicitly designed for fake news detection. NEGT introduces a noise-augmented information bottleneck strategy embedded within its self-attention mechanism, effectively identifying and removing task-irrelevant interactions. Additionally, we propose a novel relational propagation graph encoding a strategy that explicitly captures multi-scale user relationships and propagation depth, enabling NEGT to model long-sequence propagation dependencies accurately. Experiments on various benchmark datasets show that NEGT surpasses current methods in accuracy, noise robustness, and interpretability.

Federated Learning (FL) has emerged as a key framework to deliver AI services, recognized for its capability to construct global models while ensuring individual data. Nevertheless, FL heavily relies on a central server, which introduces significant challenges for participants to collaborate effectively and substantially limits the scalability of FL. Blockchain-based FL (BFL) offers a promising solution by replacing the central server with a decentralized blockchain system, thereby establishing a secure and trustworthy environment for FL. However, current BFL approaches face challenges in balancing high computational overhead, consistency, and security. In view of this, this paper introduces EBFL, an efficient blockchain framework for FL services. EBFL incorporates both asynchronous and synchronous advantages. A DAG-based (Directed Acyclic Graph) asynchronous computation enhances computational efficiency by mitigating delays caused by slow devices and reducing unnecessary waiting due to frequent synchronized consensus. Simultaneously, a periodic synchronized consensus mechanism is introduced during asynchronous training to ensure consistency, thereby improving security and model accuracy. Additionally, taking into account the unique characteristics of FL, we have designed a series of operations tailored for EBFL to further enhance the performance. Experimental results demonstrate that, compared to traditional synchronous BFL (TBFL) approaches, EBFL achieved a maximum speedup of up to 2.38× while retaining 92% of their accuracy. Subsequently, in-depth analytical experiments show that EBFL excels in both convergence speed and security, thereby confirming its potential to balance computational efficiency, consistency, and security.

The rise of social media has intensified fake news risks, prompting a growing focus on leveraging graph learning methods such as graph neural networks (GNNs) to understand post-spread patterns of news. However, existing methods often produce less robust and interpretable results as they assume that all information within the propagation graph is relevant to the news item, without adequately eliminating noise from engaged users. Furthermore, they inadequately capture intricate patterns inherent in long-sequence dependencies of news propagation due to their use of shallow GNNs aimed at avoiding the over-smoothing issue, consequently diminishing their overall accuracy. In this paper, we address these issues by proposing the Propagation Structure-aware Graph Transformer (PSGT). Specifically, to filter out noise from users within propagation graphs, PSGT first designs a noise-reduction self-attention mechanism based on the information bottleneck principle, aiming to minimize or completely remove the noise attention links among task-irrelevant users. Moreover, to capture multi-scale propagation structures while considering long-sequence features, we present a novel relational propagation graph as a position encoding for the graph Transformer, enabling the model to capture both propagation depth and distance relationships of users. Extensive experiments demonstrate the effectiveness, interpretability, and robustness of our PSGT.