Fact-checking, also referred to as fact verification, is essential for evaluating the accuracy of claims and curbing the dissemination and influence of misinformation. Recent advancements in Large Language Models (LLMs) have enabled their use in automated fact-checking systems. These approaches frequently adopt prompting techniques within a ''divide-and-conquer'' framework, where complex claims are broken down into simpler sub-claims that are individually verified to reduce the overall complexity of the task. These existing works often neglect the dependency between sub-claims and verify them in isolation. For complex claims, particularly those requiring multi-hop reasoning, the interconnections between sub-claims are crucial, as verifying each one independently often fails to capture the full context and reasoning needed for accurate verification. To address this, we propose DagFC, a novel LLM-based framework that performs Dependency-Aware Task Generation, Scheduling and Processing for Fact-Checking. DagFC constructs Knowledge Graphs (KGs) from claims to guide the decomposition of fact-checking problems and build dependent verification sub-tasks that capture the interrelations between sub-claims. This dependency-aware approach ensures more coherent and accurate verification by integrating intermediate results. Additionally, DagFC leverages LLMs throughout the verification process, from KG construction to final veracity prediction, enhancing reasoning and generation capabilities. Extensive experiments on two benchmark datasets, FEVEROUS and HoVer, demonstrate that DagFC outperforms state-of-the-art methods in both accuracy and Macro-F1 score. Furthermore, we present a user-friendly fact-checking prototype based on our framework, offering practical value for both research and public use.

Generative images have proliferated on Web platforms in social media and online copyright distribution scenarios, and semantic watermarking has increasingly been integrated into diffusion models to support reliable provenance tracking and forgery prevention for web content. Traditional noise-layer-based watermarking, however, remains vulnerable to inversion attacks that can recover embedded signals. To mitigate this, recent content-aware semantic watermarking schemes bind watermark signals to high-level image semantics, constraining local edits that would otherwise disrupt global coherence. Yet, large language models (LLMs) possess structured reasoning capabilities that enable targeted exploration of semantic spaces, allowing locally fine-grained but globally coherent semantic alterations that invalidate such bindings. To expose this overlooked vulnerability, we introduce a Coherence-Preserving Semantic Injection (CSI) attack that leverages LLM-guided semantic manipulation under embedding-space similarity constraints. This alignment enforces visual-semantic consistency while selectively perturbing watermark-relevant semantics, ultimately inducing detector misclassification. Extensive empirical results show that CSI consistently outperforms prevailing attack baselines against content-aware semantic watermarking, revealing a fundamental security weakness of current semantic watermark designs when confronted with LLM-driven semantic perturbations.

Graph Neural Networks (GNNs) have attracted significant attention for their effectiveness in fake news detection, particularly due to their capability to leverage the social context embedded within news dissemination. Most existing studies, however, operate on static heterogeneous information networks (HINs), assuming these graphs adequately capture the complex interactions among social entities. In practice, the dynamic nature of real-world social networks presents a substantial challenge, as training on suboptimal or outdated graph structures can severely limit the expressiveness of GNNs. Although various approaches have been proposed to model dynamic HINs, they often rely on computationally expensive message-passing mechanisms to update node embeddings, which hinders scalability to large social graphs. In this paper, we introduce DHGNN (Dynamic Heterogeneous Graph Neural Network), a novel model designed to address these challenges. DHGNN simplifies traditional GNN message-passing by employing a dynamic propagation scheme inspired by the personalized PageRank tracking process in HINs. Extensive experiments on three real-world benchmark datasets demonstrate the effectiveness and efficiency of DHGNN in detecting fake news.

The rapid spread of fake news on social media has caused great harm to society in recent years, which raises the detection of fake news as an urgent task. Recent methods utilize the interactions among different entities such as authors, subjects, and news articles to model news propagation as a static heterogeneous information network (HIN). However, this is suboptimal since fake news emerges dynamically, and the latent chronological interactions between news in HIN are essential signals for fake news detection. To this end, we model the dynamics of news and associated entities as a News-Driven Dynamic Heterogeneous Information Network (News-DyHIN), where the temporal relationships among news articles are well captured with meta-path based temporal neighbors. With the support of News-DyHIN, we propose a novel fake news detection framework, named D ynam i c H ierarchical A ttention N etwork (DiHAN), which learns news representations via a hierarchical attention mechanism to fuse temporal interactions among news articles. In particular, DiHAN first employs a temporal node level attention to learn the temporal information from meta-path based news neighbors through the modeled News-DyHIN. Then, a semantic attention layer is adopted to fuse different types of meta-path based temporal information for news representation learning. Extensive evaluations conducted on two public real-world datasets demonstrate that our proposed DiHAN achieves significant improvements over established baseline models.

As the calculation of centrality in complex networks becomes increasingly vital across technological, biological, and social systems, precise and scalable ranking methods are essential for understanding these networks. This paper introduces LayerPlexRank, an algorithm that simultaneously assesses node centrality and layer influence in multiplex networks using algebraic connectivity metrics. This method enhances the robustness of the ranking algorithm by effectively assessing structural changes across layers using random walk, considering the overall connectivity of the graph. We substantiate the utility of LayerPlexRank with theoretical analyses and empirical validations on varied real-world datasets, contrasting it with established centrality measures.