While short-video platforms have democratized news consumption, they simultaneously exacerbate the proliferation of misinformation. Despite the progress of recent cross-modal frameworks, two critical challenges remain unaddressed: task-irrelevant noise (e.g., background music) that creates spurious correlations and the limitations of existing pairwise alignment methods that fail to capture the holistic structural conflicts within the video-audio-text triad. To address these challenges, we propose a novel framework for short-video fake news detection based on Semantic Purification and Geometric Routing (SPGR). First, to mitigate noise, we introduce Semantic Disentanglement Masking (SDM) as an adaptive filter to suppress environmental noise. Second, to transcend reductionist pairwise views, we introduce an Audio-Adaptive Projector that models the multimodal triad as a parallelotope, using Gramian geometric volume to quantify holistic consistency. Crucially, by utilizing the purified masks and geometric volume as hierarchical cues, SPGR orchestrates a progressive “Intuition-then-Reasoning” workflow: samples first undergo structural conflict checks by “intuition” experts before being passed to “reasoning” experts for fine-grained semantic scrutiny. Experiments on FakeSV and FakeTT demonstrate that SPGR outperforms recent state of-the-art methods in accuracy. The code is available at: https://github.com/sjsgmm/SPGR.

The utilization of automated detection of fake news videos significantly improves early intervention for misinformation on short video platforms. A shortcoming of existing approaches is their inability to fuse multimodal features effectively. They only perform simple fusion of multimodal features to predict fake news, without deeply considering inter-modality relations. Inspired by the way people read news videos, we propose a novel multimodal deep fusion network (MFSVFND) that can effectively capture the important details of unimodality in detecting fake news videos by learning inter-dependencies among multimodal features. We conducted extensive experiments on two large-scale dataset of fake news videos in different languages, and our model outperforms state-of-the-art methods, demonstrating the superiority of MFSVFND in detecting fake news on short video platforms.

The rise of social media has posed a challenging problem of effectively identifying rumors. With the great success of contrastive learning in many fields, many contrastive learning models for rumor detection have been proposed. However, existing models usually use the propagation structure of other events as negative samples and regard more similar samples to anchor events as hard ones across all the training processes, resulting in undesirably pushing away the samples of the same class. Thus, we propose a novel contrastive learning model (CRFB) to solve the above problem. Specifically, we employ contrastive learning between two augmented propagation structure and fit a two-component (true-false) beta mixture model (BMM) to measure the probability of negative samples being true. In addition, we propose a CNN-based model to capture the consistent and complementary information between two augmented propagation structure. The experimental results on public datasets demonstrate that our CRFB outperforms the existing state-of-the-art models for rumor detection.

The complexity of the graph structure poses a challenge for graph representation learning. Contrastive learning offers a straightforward and efficient unsupervised framework for graph representation learning. It achieves unsupervised learning by augmenting the original views and comparing them with the augmented views. Several methods based on this framework have achieved significant progress in the field of graph representation learning. Despite its success, the factors contributing to good augmented views in graph contrast learning have received less attention. In order to address this issue, we introduce the graph info-min principle. We investigate the relationship between mutual information (MI) and good augmented views through experimental and theoretical analysis. Additionally, we present a new contrastive learning method called Info-min Contrastive Learning (IMCL). Specifically, The method comprises an adaptive graph augmentation generator and a pseudo-label generator. The graph augmentation generator ensures sufficient differentiation between the augmented and original views. The pseudo-label generator generates pseudo-labels as supervision signals, ensuring consistency between the classification results of augmented views and original views. Our method demonstrates excellent performance through extensive experimental results on various datasets.

\beginabstract Graph neural networks (GNNs) have shown strong performance in graph-based analysis tasks. Despite their remarkable success, the inherent homophilic message-passing mechanism (MP) makes GNNs challenging to generalize to heterophilic graphs. In addition, the MP explicitly exploits the connection relationships between local neighbor nodes making GNNs unable to maintain stable performance in the face of adversarial perturbation attacks. In this paper, we propose a new method to explore graph structure by removing explicit message-passing mechanisms and present a network embedding framework AMCNE with Adaptive Multi-hop Contrast loss (AMCLoss) to address these challenges. AMCNE only relies on a simple autoencoder to obtain node representations for classification and uses elaborate contrastive loss to drive nodes capturing complex structural information on heterophilic graphs. The comprehensive experiments show that AMCNE outperforms state-of-the-art baseline models on homophilic and heterophilic graphs and is more robust in the node classification task. \endabstract

Graph embedding is becoming increasingly popular due to its ability of representing large-scale graph data by mapping nodes to low-dimensional space. Current research usually focuses on transductive learning, which aims to generates fixed node embeddings by training the whole graph. However, dynamic graph changes constantly with new node additions and interactions. Unlike transductive learning, inductive learning attempts to dynamically generate node embeddings over time even for unseen nodes, which is more suitable for real-world applications. Therefore, we propose an inductive dynamic graph embedding method called AGLI by aggregating global and local influences. We propose an aggregator function that integrates global influence with local influence to generate node embeddings at any time. We conduct extensive experiments on several real-world datasets and compare AGLI with several state-of-the-art baseline methods on various tasks. The experimental results show that AGLI achieves better performance than the state-of-the-art baseline methods.

Due to the rapid spread of fake news on social media, society and economy have been negatively affected in many ways. How to effectively identify fake news is a challenging problem that has received great attention from academic and industry. Existing deep learning methods for fake news detection require a large amount of labeled data to train the model, but obtaining labeled data is a time-consuming and labor-intensive process. To extract useful information from a large amount of unlabeled data, some contrastive learning methods for fake news detection are proposed. However, existing contrastive learning methods only randomly sample negative samples at different training stages, resulting in the role of negative samples not being fully played. Intuitively, increasing the contrastive difficulty of negative samples gradually in a way similar to human learning will contribute to improve the performance of the model. Inspired by the idea of curriculum learning, we propose a curriculum contrastive model (CCFD) for fake news detection which automatically select and train negative samples with different difficulty at different training stages. Furthermore, we also propose three new augmentation methods which consider the importance of edges and node attributes in the propagation structure to obtain more effective positive samples. The experimental results on three public datasets show that our model CCFD outperforms the existing state-of-the-art models for fake news detection.

In recent years, graph neural network (GNN) has become the most important method for graph representation learning. However, most GNNs focus on using the message passing mechanism to guide the information aggregation between neighbors, which results in the over-smoothing and weak robustness. To address the above issues, we propose a novel graph representation learning framework via Adaptive Multi-layer Neighborhood Diffusion Contrast, called AM-NDC in this paper. Without using the message passing mechanism, AM-NDC can still capture the complex structural information between nodes through a neighborhood diffusion contrast loss. Experimental results show that AM-NDC outperforms the existing state-of-the-art models in both node classification and robustness against adversarial attacks. Our dataset and code are available at https://github.com/YJ199804/AM-NDC.

Network representation learning aims to generate an embedding for each node in a network, which facilitates downstream machine learning tasks such as node classification and link prediction. Current work mainly focuses on transductive network representation learning, i.e. generating fixed node embeddings, which is not suitable for real-world applications. Therefore, we propose a new inductive network representation learning method called MNCI by mining neighborhood and community influences in temporal networks. We propose an aggregator function that integrates neighborhood influence with community influence to generate node embeddings at any time. We conduct extensive experiments on several real-world datasets and compare MNCI with several state-of-the-art baseline methods on various tasks, including node classification and network visualization. The experimental results show that MNCI achieves better performance than baselines.

We investigate the problem of summarizing frequent subgraphs by a smaller set of representative patterns. We show that some special graph patterns, called δ-jump patterns in this paper, must be representative patterns. Based on the fact, we devise two algorithms, RP-FP and RP-GD, to mine a representative set that summarizes frequent subgraphs. RP-FP derives a representative set from frequent closed subgraphs, whereas RP-GD mines a representative set from graph databases directly. Three novel heuristic strategies, Last-Succeed-First-Check, Reverse-Path-Trace, and Nephew-Representative-Based-Cover, are proposed to further improve the efficiency of RP-GD. RP-FP can provide a tight ratio bound but has heavy computation cost. RP-GD cannot provide a ratio bound guarantee but is more efficient than RP-FP. We also make use of the similarity between sibling branches in the graph pattern space to devise another much more efficient algorithm, RP-Leap, for mining a representative set that can approximately summarize frequent subgraphs. Our extensive experiments on both real and synthetic data sets verify the summarization quality and efficiency of our algorithms. To further demonstrate the interestingness of representative patterns, we study an application of representative patterns to classification. We demonstrate that the classification accuracy achieved by representative pattern-based model is no less than that achieved by closed graph pattern-based model.

Several efficient frequent subgraph mining algorithms have been recently proposed. However, the number of frequent graph patterns generated by these graph mining algorithms may be too large to be effectively explored by users, especially when the support threshold is low. In this paper, we propose to summarize frequent graph patterns by a much smaller number of representative graph patterns. Several novel concepts such as delta-covergraph,jumpvalueanddelta-jumppatternare proposed for efficiently summarizing frequent graph patterns. Based on the fact that all delta-jumppatternsmust be representative graph patterns, we propose two efficient algorithms for summarizing frequent graph patterns, RP-FP and RP-GD. The RP-FP algorithm computes representative graph patterns from a set of closed frequent graph patterns, whereas the RP-GD algorithm directly mines representative graph patterns from graph databases. Experimental results show that RP-FP and RP-GD are able to obtain compact summarization in both real and synthetic graph databases. When the number of closed graph patterns is very large, RP-GD is much more efficient than RP-FP, while achieving comparable summarization quality.