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.

With the rapid global growth of short video platforms, the spread of fake news has become increasingly prevalent, creating an urgent demand for effective automated detection methods. Current approaches typically rely on feature extractors to gather information from multiple modalities and then generate predictions through classifiers. However, these methods often fail to fully utilize the complex information across all modalities and overlook the potential for video manipulation, limiting their overall performance. To tackle these issues, MFAE is proposed, a novel framework for Multimodal Feature Adaptive Enhancement for Fake News Video Detection. The framework starts by extracting semantic and emotional features from the news, which are the basis for generating coarse multimodal representations. These representations are further refined through Adaptive Enhancement, a module specifically designed to strengthen the visual and audio modalities. Subsequently, spatial and temporal features are extracted separately, with temporal features undergoing additional refinement via a Temporal Enhancement module. The final result is obtained by feeding the individually enhanced features into the multimodal feature integration module for interaction Comprehensive experiments on two benchmark datasets highlight the exceptional performance of MFAE in detecting fake news on short video platforms. Specifically, the method achieves accuracy improvements of 2.21% and 4.35% on FakeSV and FakeTT, respectively.

Early rumor detection is crucial for mitigating the widespread dissemination of misinformation. Existing methods predominantly rely on complete rumor diffusion graphs, which are challenging to obtain in real-world scenarios, complicating early detection efforts. To address this challenge, we propose D2, a two-stage framework for early rumor Detection, integrating cascade Diffusion prediction. This framework aims to enhance early rumor detection by incorporating diffusion prediction capabilities. Specifically, a dynamic heterogeneous graph neural network (GNN) is developed to jointly model users' social and propagation graphs, enabling accurate prediction of potential diffusion paths using limited observed data within short time windows. The inferred diffusion paths are then integrated with early-stage data, and GNNs are employed for graph classification. However, the varying data distributions across different social media platforms necessitate extensive tuning to optimize GNN architectures. To facilitate the detection of rumor diffusion graphs at the initial stages, a search space is designed across four dimensions- aggregation, merge, readout, and sequence functions-encompassing various GNN architectures. Subsequently, D2 employs an efficient differentiable search algorithm to identify high-performance GNNs within this search space. Experimental results on real social media datasets demonstrate that this approach significantly improves both the accuracy and robustness of early rumor detection.

Source localization in social platforms is critical for managing and controlling the misinformation spreading. Despite all the recent advancements, existing methods do not consider the dynamic and heterogeneous propagation behaviors of users and are developed based on simulated data with strong model assumptions, limiting the application in real-world scenarios. This research addresses this limitation by presenting a novel framework for source localization, grounded in real-world propagation cascades from platforms like Weibo and Twitter. What's more, recognizing the user-driven nature of users in information spread, we systematically crawl and integrate user-specific profiles, offering a realistic understanding of user-driven propagation dynamics. In summary, by developing datasets derived from real-world propagation cascades, we set a precedent in enhancing the authenticity and practice of source identification for social media. Our comprehensive experiments not only validate the feasibility and rationale of our novel user-centric localization approaches but also emphasize the significance of considering user profiles in real-world propagation scenarios. The code is available at https://github.com/cgao-comp/NFSL.

The topology of diffusion networks plays an essential role in understanding information propagation dynamics and conducting social network analysis. However, diffusion networks are often unobservable in practical applications, leading to wide research on network inference from information cascades over the past decade. At present, novel cascades-based methods have been further developed to recover temporal dynamics and network topology by exploring the utilization of node temporal information, resulting in notable advancements. However, it requires high costs to acquire extensive temporal information, and the performance of network inference may decrease due to potential observational errors. Therefore, this paper specifically focuses on the time-independent scenario to address these limitations. Firstly, this paper models the node statuses of each diffusion process by leveraging the assumption of propagation trees based on the well-known independent cascade model. Subsequently, a gradient-based approach is developed to estimate the influences between nodes, facilitating the inference of network structure. Furthermore, this paper proposes a Monte Carlo EM-based approach to enhance the efficiency of network inference while maintaining comparable accuracy. Extensive experiments are conducted to verify the efficiency and effectiveness of our approaches on both synthetic and real-world networks.

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.

An explicit network structure plays an important role when analyzing and understanding diffusion processes. In many scenarios, however, the interactions between nodes in an underlying network are unavailable. Although many methods for inferring a network structure from observed cascades have been proposed, they did not perceive the relationship between pairwise interactions in a cascade. Therefore, this paper proposes a Pairwise-interactions-based Bayesian Inference method (named PBI) to infer the underlying diffusion network structure. More specifically, to get more accurate inference results, we measure the weights of each candidate pairwise interaction in different cascades and add them to the likelihood of a contagion process. In addition, a pre-pruning work is introduced for candidate edges to further improve the inference efficiency. Experiments on synthetic and real-world networks show that PBI achieves significantly better results.

Community structure division is a crucial problem in the field of network data analysis. Algorithms based on Markov chains are easy to use and provide promising solutions for community detection. In a Markov chain-based algorithm (i.e., MCL), a flow distribution matrix and a transition matrix are used to describe stochastic flows and transition probabilities, respectively, on a network. The dynamic interaction process between stochastic flows and transition probabilities in MCLs is manifested through an iterative process of updating the abovementioned two matrices. As one of the key mechanisms of MCLs, such a dynamic process for increasing the inhomogeneity directly affects the accuracy and computational cost of MCL-based methods. Inspired by a kind of positive feedback interaction of a dendritic network of tube-like amoeba cell pseudopodia (named thePhysarumforaging network), aPhysarum-inspired relationship among vertices is proposed to enhance the transition probability in the dynamic process of MCL-based community detection algorithms. Specifically, the proposed hybrid community detection algorithm can adaptively search for a better combination of parameters based on a genetic algorithm. Some experiments are carried out on both static and dynamic networks. The results show that the uniquePhysaruminspired algorithm achieved better computational efficiency and detection performance than other algorithms.

Evolutionary algorithm belongs to the behaviorism which is one of major approaches to artificial intelligence. Community detection is one of the important applications of the evolutionary algorithm. Detecting the community structure, an essential property for complex networks, can help us understand the inherent functions of real systems. It has been proved that genetic algorithm (GA) is feasible for community detection, and yet existing GA-based community detection algorithms still need improving in terms of their robustness and accuracy. A Physarum-based network model (PNM) with an intelligence of recognizing inter-community edges based on a kind of multi-headed slime mold, has been proposed in the phase of GA's initialization for optimization. In this paper, integrated with PNM after three operators of GA during the process of community detection, a novel genetic algorithm, called P-GACD, is proposed to improve the efficiency of GA for community detection. In addition, some experiments are implemented in five real-world networks to evaluate the performance of P-GACD. The results reveal that P-GACD shows an advantage in terms of the robustness and accuracy, contrasted with the existing algorithms.