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.

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the dynamic nature of real-world social networks presents a substantial challenge, as training on suboptimal or outdated graph structures can severely limit the expressiveness of GNNs.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.

User engagement has recently received significant attention in understanding the decay and expansion of communities in many online social networking platforms. Many user engagement studies have been conducted to find a set of critical (anchored) users in the static social network. However, social networks are highly dynamic and their structures are continuously evolving. In this paper, we target a new research problem called Anchored Vertex Tracking (AVT), aiming to track the anchored users at each timestamp of evolving networks. To address the AVT problem, we develop a greedy algorithm inspired by the previous anchored k-core study in the static networks. Furthermore, we design an incremental algorithm to efficiently solve the AVT problem by utilizing the smoothness of the network structure's evolution. The extensive experiments demonstrate the performance of our proposed algorithms.

Influence Maximization(IM), which aims to select a set of users from a social network to maximize the expected number of influenced users, has recently received significant attention for mass communication and commercial marketing. Existing research efforts dedicated to the IM problem depend on a strong assumption: the selected seed users are willing to spread the information after receiving benefits from a company or organization. In reality, however, some seed users may be reluctant to spread the information or need to be paid higher to be motivated. Furthermore, the existing IM works pay little attention to capture users’ influence propagation in the future period. In this paper, we target a new research problem named,ReconnectingTop-$l$lRelationships(RT$l$R) query, which aims to find$l$number of previous existing relationships but being estranged later such that reconnecting these relationships will maximize the expected number of influenced users by the given group in a future period. We prove that the RT$l$R problem is NP-hard. An efficient greedy algorithm is proposed to answer the RT$l$R queries with the influence estimation technique and the well-chosen link prediction method to predict the near future network structure. We also design a pruning method to reduce unnecessary probing from candidate edges. Further, a carefully designed order-based algorithm is proposed to accelerate the RT$l$R queries. Finally, we conduct extensive experiments on real-world datasets to demonstrate the effectiveness and efficiency of our proposed methods.

User engagement has recently received significant attention in understanding the decay and expansion of communities in many online social networking platforms. When a user chooses to leave a social networking platform, it may cause a cascading dropping out among her friends. In many scenarios, it would be a good idea to persuade critical users to stay active in the network and prevent such a cascade because critical users can have significant influence on user engagement of the whole network. Many user engagement studies have been conducted to find a set of critical(anchored)users in the static social network. However, social networks are highly dynamic and their structures are continuously evolving. In order to fully utilize the power of anchored users in evolving networks, existing studies have to mine multiple sets of anchored users at different times, which incurs an expensive computational cost. To better understand user engagement in evolving network, we target a new research problem calledAnchored Vertex Tracking(AVT) in this paper, aiming to track the anchored users at each timestamp of evolving networks. Nonetheless, it is nontrivial to handle the AVT problem which we have proved to be NP-hard. To address the challenge, we develop a greedy algorithm inspired by the previous anchored$k$-core study in the static networks. Furthermore, we design an incremental algorithm to efficiently solve the AVT problem by utilizing the smoothness of the network structure's evolution. The extensive experiments conducted on real and synthetic datasets demonstrate the performance of our proposed algorithms and the effectiveness in solving the AVT problem.