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.

Fake news detection is essential for safeguarding social media users, as the rapid spread of unverified information on these platforms facilitates the easy dissemination of fake news, creating significant risks to society. This underscores the urgent need for automated fake news detection systems that leverage advanced technologies. Current multimodal fake news detection approaches encounter challenges including inconsistent decision-making, prioritizing shared subspace information fusion over view-specific data extraction, and insufficient attention given to analyzing the sentimental elements of the news. To this end, we introduce sentiment-aware fake news detection with view-specific feature extraction (SentiView), which integrates multimodal and view-specific features and authors’ sentiments into a unified framework. The proposed framework comprises four modules: a) modal-specific encoder, which generates encodings from text and image modalities; b) multimodal consistency learning that captures the interrelationships to recognize the inherent ambiguity between the different modalities; c) view-aware feature extractor that captures information from different views within the image modality, integrating an orthogonal constraint within the shared subspace, thus facilitating the utilization of distinctive discriminative details; and d) sentiment extractor that captures the sentiments reflected to extract the underlying intention and potential bias of the news author. Extensive experiments on three publicly available datasets X, Weibo, and GossipCop show that SentiView outperforms state-of-the-art fake news detection approaches with an accuracy of 93.8%, 94.5%, and 87.3%, on the datasets respectively.

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.

Social media’s rise has enabled rapid content dissemination and fostered ideological echo chambers. These platforms, by design, often promote insular communities and targeted content exposure, making them effective tools for extremist recruitment and propaganda. Existing research has primarily focused on detecting extremist-related content particularly on Twitter (now X), which ignores the increasing adoption of other platforms such as Bluesky and Mastodon. To capture this evolving landscape, we propose SOCINT (Social Observations with Chronic Interactions and Network Tracking), a novel framework for user-level sentiment classification at specific timestamps. Unlike existing approaches that analyze individual posts in isolation, SOCINT leverages both temporal activity patterns and social network interactions to assess user sentiment and track its evolution over time. We also introduce a new multi-platform dataset enriched with social and temporal metadata. Experimental results demonstrate that SOCINT outperforms existing baselines, offering a dynamic and context-aware solution for detecting and monitoring extremist behaviour across social media ecosystems.

Fact verification involves assessing the factual-ity of claims to detect false information. This work focuses on a specific fact verification subtask: verifying claims using retrieved textual and tabular evidence. Existing approaches often overlook the distinct features and interactions of table and text evidence, which are essential for accurate claim verification by providing a comprehensive understanding. Moreover, current evidence fusion strategies used by existing work fail to model complex distinctions, leading to ineffective integration. This work introduces a novel veracity prediction model that leverages dual-view evidence learning and graph-based evidence fusion to address these limitations. Our model incorporates a local view, capturing the unique information within each sentence and table, and a global view, modeling the interactions between these evidence pieces. We further employ graph networks to fuse information within each view and across views, generating richer evidence representations for improved claim verification. Extensive experiments demonstrate the effectiveness of our method.

Given the increasing number and severity of cyber attacks, there has been a surge in cybersecurity information across various mediums such as posts, news articles, reports, and other resources. Cyber Threat Intelligence (CTI) involves processing data from these cybersecurity sources, enabling professionals and organizations to gain valuable insights. However, with the rapid dissemination of cybersecurity information, the inclusion of fake CTI can lead to severe consequences, including data poisoning attacks. To address this challenge, we have implemented a three-step strategy: generating synthetic CTI, evaluating the quality of the generated CTI, and detecting fake CTI. Unlike other subdomains, such as fake COVID news detection, there is currently no publicly available dataset specifically tailored for fake CTI detection research. To address this gap, we first establish a reliable groundtruth dataset by utilizing domain-specific cybersecurity data to fine-tune a Large Language Model (LLM) for synthetic CTI generation. We then employ crowdsourcing techniques and advanced synthetic data verification methods to evaluate the quality of the generated dataset, introducing a novel evaluation methodology that combines quantitative and qualitative approaches. Our comprehensive evaluation reveals that the generated CTI cannot be distinguished from genuine CTI by human annotators, regardless of their computer science background, demonstrating the effectiveness of our generation approach. We benchmark various misinformation detection techniques against our groundtruth dataset to establish baseline performance metrics for identifying fake CTI. By leveraging existing techniques and adapting them to the context of fake CTI detection, we provide a foundation for future research in this critical field. To facilitate further research, we make our code, dataset, and experimental results publicly available on GitHub .

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.

Amidst the rapid technological advancements of today, privacy and anonymity are facing increasing threats. Tor, one of the most widely used anonymity networks, enables users to browse the Internet without their activities being tracked. Extensive research has been conducted on both attacking and defending the anonymity of Tor users. Website Fingerprinting (WF) is one of the popular de-anonymisation techniques employed against Tor users. This paper presents two novel WF techniques based on Graph Neural Networks (GNNs) to explore two relatively understudied avenues of WF: the fingerprintability of Decentralised Applications (DApps) and the impact of reload traffic on WF. Due to the lack of publicly available datasets for DApp traffic and reload traffic suitable for WF, we collected five new datasets for our experiments. Our findings reveal that GNN-based techniques surpass the performance of state-of-the-art WF techniques when reload traffic is used. Meanwhile, certain high-performing state-of-the-art techniques exhibit a significant reduction in accuracy, more than 40%, when reload traffic is used instead of homepage traffic. Additionally, we identify that DApps are less susceptible to fingerprinting than conventional websites, leading to a 25% decrease in accuracy in some state-of-the-art WF techniques. While confirming prior research findings that GNN-based techniques can outperform existing techniques when accessing DApps via Chrome, we further demonstrate that using Tor to access DApps makes them even more difficult to fingerprint. Finally, we expect our datasets, four of which lack publicly available alternatives, will prove invaluable for future research.

The widespread integration of the Internet of Things (IoT) is crucial in advancing sustainable development. IoT service providers actively collect user data for analysis using sophisticated Deep Learning (DL) algorithms. This enables the extraction of valuable insights for business intelligence and improving service quality. However, as these datasets contain sensitive personal information, there is a risk of privacy breaches when DL models are employed. This vulnerability may result in Membership Inference Attacks (MIA), potentially leading to the unauthorized disclosure of highly sensitive data. Therefore, developing an efficient and privacy-preserving data analysis system for IoT is imperative. Recent research has highlighted the effectiveness of utilizing Bloom Filter (BF)-encoding in conjunction with Differential Privacy (DP) for safeguarding privacy during data analysis. Given its attributes of low complexity and high utility, this approach proves effective, particularly in resource-constrained IoT domains. With this in mind, we propose a novel framework for privacy-preserving IoT data analysis based on BF-encoded data. Our research introduces an innovative BF-encoding technique combined with Local Differential Privacy (LDP), capable of efficiently encoding various types of IoT data (such as facial images and smart-meter data) while maintaining privacy when integrated into DL algorithms for downstream analysis. Experimental results demonstrate that our BF-encoded data surpasses the utility of standard BF-encoded data when utilized in DL algorithms for downstream tasks, showcasing an approximate 30% improvement in classification accuracy. Furthermore, we assess the privacy of these DL models against MIA, revealing that attackers can only make random guesses with an accuracy of approximately 50%.

The topic of source identification has attracted wide attention from researchers. In practice, the source identification method aims to locate the sources of rumors, computer viruses, and epidemics, such as COVID-19. However, there are two main problems with existing propagation source detection methods. First, most source detection methods are based on infinite networks, not in line with reality. Second, sources are often randomly selected in simulations, but different sources often cause significantly different detection results in real-world applications. To this end, we study how does the source location impact the effectiveness of source detection in finite networks. This paper first proposes a diameter-based node division method to classify the nodes based on their structural location. We further offer different evaluation indicators to measure the effectiveness of source detection methods. Then, we conduct systematic experiments on three synthetic networks and two real-world networks. Our experiments demonstrate that the location of the source directly effects detection effectiveness in finite networks for all source detection methods. Specifically, sources closer to the network boundary will lead to worse detection performance. It means that attackers can choose sources close to the network boundary to reduce the probability of detection to achieve a larger spreading scale.

Having precise specifications of service APIs is essential for many Software Engineering activities. Unfortunately, available documentation of services is often inadequate and/or imprecise and, hence, cannot be fully relied upon. Generating service documentation manually is a tedious and error-prone task, especially in light of changes to services. Therefore, there is a need for automated support in generating service documentation. In this work, we present a novel approach to infer the API of a service by analyzing recorded messages sent to and received from this service. Our approach includes a novel, two-level clustering technique to cluster messages, a step that many existing approaches to infer message formats fail to perform precisely in the presence of significant variation of payload information of the available messages. We have evaluated our approach on message traces from four different real-world services. The experimental result shows that our approach is more effective than existing techniques in extracting correct message formats from recorded messages.

There is a growing interest in the use of renewable energy sources to power fog networks in order to mitigate the detrimental effects of conventional energy production. However, renewable energy sources, such as solar and wind, are by nature unstable in their availability and capacity. The dynamics of energy supply hence impose new challenges for network planning and resource management. In this paper, the sustainable performance of a fog node powered by renewable energy sources is studied. We develop a generic analytical model to study the energy sustainability of fog nodes powered by renewable energy sources, by generalizing the leaky bucket model to shape and police traffic source for rate-based congestion control in high-speed fog networks. Based on the closed-form solutions of energy buffer analysis, i.e., the energy depletion probability and mean energy length, we study the energy sustainability in two special but real-happening scenarios. The experimental results show that with proper design the leaky bucket model effectively reflects the energy sustainability of data traffic in fog networks. Numerical results also reveal that the model performance is sensitive to certain traffic source characteristics in fog networks.

APIs play a significant role in the sharing, utilization and integration of information and service assets for enterprises, delivering significant business value. However, the documentation of service APIs can often be incomplete, ambiguous, or even non-existent, hindering API-based application development efforts. In this paper, we introduce an approach to automatically mine the fine-grained message formats required in defining the APIs of services and applications from their interaction traces, without assuming any prior knowledge. Our approach includes three major steps with corresponding techniques: (1) classifying the interaction messages of a service into clusters corresponding to message types, (2) identifying the keywords of messages in each cluster, and (3) extracting the format of each message type. We have applied our approach to network traces collected from four real services which used the following application protocols: REST, SOAP, LDAP and SIP. The results show that our approach achieves much greater accuracy in extracting message formats for service APIs than current state-of-art approaches.

Identifying rumor sources in social networks plays a critical role in limiting the damage caused by them through the timely quarantine of the sources. However, the temporal variation in the topology of social networks and the ongoing dynamic processes challenge our traditional source identification techniques that are considered in static networks. In this paper, we borrow an idea from criminology and propose a novel method to overcome the challenges. First, we reduce the time-varying networks to a series of static networks by introducing a time-integrating window. Second, instead of inspecting every individual in traditional techniques, we adopt a reverse dissemination strategy to specify a set of suspects of the real rumor source. This process addresses the scalability issue of source identification problems, and therefore dramatically promotes the efficiency of rumor source identification. Third, to determine the real source from the suspects, we employ a novel microscopic rumor spreading model to calculate the maximum likelihood (ML) for each suspect. The one who can provide the largest ML estimate is considered as the real source. The evaluations are carried out on real social networks with time-varying topology. The experiment results show that our method can reduce 60 - 90 percent of the source seeking area in various time-varying social networks. The results further indicate that our method can accurately identify the real source, or an individual who is very close to the real source. To the best of our knowledge, the proposed method is the first that can be used to identify rumor sources in time-varying social networks.

This paper gives models and analytic techniques for studying shaping and policing data traffic in fog networks. The traffic in these networks is expected to be highly diverse and bursty, and regulation will be required as an integral part of congestion control. We generalize the Leaky Bucket model to shape and police traffic source for rate-based congestion control in high-speed fog networks. In particular, the Markov modulated fluid sources reflect the bursty characteristics of data traffic. To measure the performance of the model in shaping and policing traffic, we derive four performance metrics. The experimental results show that with proper design the Leaky Bucket model effectively controls a 4-way trade-off between throughput, loss probability, delay and burstiness of data traffic. Numerical results also reveal that the model performance is sensitive to certain traffic source characteristics.

Summary Scale‐free networks are often used to model a wide range of real‐world networks, such as social, technological, and biological networks. Understanding the structure of scale‐free networks evolves into a big data problem for business, management, and protein function prediction. In the past decade, there has been a surge of interest in exploring the properties of scale‐free networks. Two interesting properties have attracted much attention: the assortative mixing and community structure. However, these two properties have been studied separately in either theoretical models or real‐world networks. In this paper, we show that the structural features of communities are highly related with the assortative mixing in scale‐free networks. According to the value of assortativity coefficient, scale‐free networks can be categorized into assortative, disassortative, and neutral networks, respectively. We systematically analyze the community structure in these three types of scale‐free networks through six metrics: node embeddedness, link density, hub dominance, community compactness, the distribution of community sizes, and the presence of hierarchical communities. We find that the three types of scale‐free networks exhibit significant differences in these six metrics of community structures. First, assortative networks present high embeddedness, meaning that many links lying within communities but few links lying between communities. This leads to the high link density of communities. Second, disassortative networks exhibit great hubs in communities, which results in the high compactness of communities that nodes can reach each other via short paths. Third, in neutral networks, a big portion of links act as community bridges, so they display sparse and less compact communities. In addition, we find that (dis)assortative networks show hierarchical community structure with power‐law‐distributed community sizes, while neutral networks present no hierarchy. Understanding the structure of communities from the angle of assortative mixing patterns of nodes can provide insights into the network structure and guide us in modeling information propagation in different categories of scale‐free networks. Copyright © 2016 John Wiley & Sons, Ltd.

Information-Centric Networking (ICN) has emerged as a promising way for the efficient content delivery over the Internet, and it can be seen as a super large-scale caching distributed system. However, as one of the most important problems, the cache consistency issue, which refers to whether cached contents in routers are outdated, is still not investigated thoroughly in ICN. Thus, in this paper, we propose a cost-effective Popularity-based Cache Consistency (PCC) mechanism to guarantee the freshness of cached contents in ICN routers. PCC is able to balance the trade between the consistency strength and related costs since it only maintains the strong consistency for popular contents while the weak for unpopular ones. Besides, we improve another two cache consistency mechanisms used in the web caching, namely Polling-Every-Time (PET) and Time-To-Live (TTL), to be suitable for ICN, and use them as the benchmarks for comparisons with PCC. To evaluate their performance, we firstly analyse the costs of these mechanisms including the user latency in terms of hop counts and corresponding signaling overheads, and then conduct extensive simulations using a real topology. The simulation results show the high efficiency of PCC compared with the improved PET and TTL.

Identifying influential spreaders in online social networks (OSNs) has long been an important but difficult problem to be addressed. Distinguished from previous works that mainly focused on the stationary features of users' influence, we systematically study the variations of users' spreading capability given the fact that influential spreaders are more likely to be the targets of various cyber attacks in real OSNs. In order to rank users' spreading capability, we adopt the k-shell structure which assigns a coreness index, ks, to each user. We find that users' spreading capability can considerably change when attacks occur in specific structures of OSNs. Generally, if the OSN structure is assortative (i.e., large-degree nodes preferably connect to nodes with large degree), users' spreading capability is resilient to attacks. However, if the OSN structure is disassortative (i.e., large-degree nodes preferably connect to nodes with small degree), users' spreading capability decreases significantly under attacks. We further carried out a series of empirical studies in real OSN datasets to disclose the causation of the variations caused by attacks. The research presented in this paper benefit decision makers to protect the propagation in the case of product promotion or prevent the diffusion like rumor.

The global diffusion of epidemics, computer viruses, and rumors causes great damage to our society. It is critical to identify the diffusion sources and timely quarantine them. However, most methods proposed so far are unsuitable for diffusion with multiple sources because of the high computational cost and the complex spatiotemporal diffusion processes. In this paper, based on the knowledge of infected nodes and their connections, we propose a novel method to identify multiple diffusion sources, which can address three main issues in this area: 1) how many sources are there? 2) where did the diffusion emerge? and 3) when did the diffusion break out? We first derive an optimization formulation for multi-source identification problem. This is based on altering the original network into a new network concerning two key elements: 1) propagation probability and 2) the number of hops between nodes. Experiments demonstrate that the altered network can accurately reflect the complex diffusion processes with multiple sources. Second, we derive a fast method to optimize the formulation. It has been proved that the proposed method is convergent and the computational complexity is O(mn logα), where α = α(m, n) is the slowly growing inverse-Ackermann function, n is the number of infected nodes, and m is the number of edges connecting them. Finally, we introduce an efficient algorithm to estimate the spreading time and the number of diffusion sources. To evaluate the proposed method, we compare the proposed method with many competing methods in various real-world network topologies. Our method shows significant advantages in the estimation of multiple sources and the prediction of spreading time.

Restraining the spread of rumors in online social networks (OSNs) has long been an important but difficult problem to be addressed. Currently, there are mainly two types of methods 1) blocking rumors at the most influential users or community bridges, or 2) spreading truths to clarify the rumors. Each method claims the better performance among all the others according to their own considerations and environments. However, there must be one standing out of the rest. In this paper, we focus on this part of work. The difficulty is that there does not exist a universal standard to evaluate them. In order to address this problem, we carry out a series of empirical and theoretical analysis on the basis of the introduced mathematical model. Based on this mathematical platform, each method will be evaluated by using real OSN data. We have done three types of analysis in this work. First, we compare all the measures of locating important users. The results suggest that the degree and betweenness measures outperform all the others in the Facebook network. Second, we analyze the method of the truth clarification method, and find that this method has a long-term performance while the degree measure performs well only in the early stage. Third, in order to leverage these two methods, we further explore the strategy of different methods working together and their equivalence. Given a fixed budget in the real world, our analysis provides a potential solution to find out a better strategy by integrating both types of methods together. From both the academic and technical perspective, the work in this paper is an important step towards the most practical and optimal strategies of restraining rumors in OSNs.