Social event prediction has garnered increasing attention in web-centered society. Most existing studies represent web-based event stream as chronological graph sequences, then leverage RNNs and GNNs to model temporal and relational patterns. However, this paradigm is inherently flawed: (1) RNNs struggle to capture long-term temporal dependencies, ignoring those temporally distant but influential events. (2) Spatio-temporal GNNs exhibit high computational complexity on large-scale real-time event streams, which hinders their web applications. To this end, we explore a novel paradigm called Fourier Graph Learning from the perspective of frequency domain. Specifically, we first define a novel data structure called Fourier Graph (FG). In FG, both nodes and edges are complex vectors, with real part encoding semantics and imaginary part representing semantic-specific temporal patterns. These temporal patterns are obtained by semantic-aware frequency filter, which utilizes semantics as guidance to adaptively incorporates both long-term dependency and short-term dynamic. Based on FG, we further propose Fourier Graph Neural Network (FGNN). It replaces time-domain convolution with frequency-domain multiplication for efficient aggregation. FGNN also includes a complex-valued event decoder, which fully leverages semantics and temporal patterns from complex space to predict future event probabilities. Extensive experiments show our superior performance with higher accuracy, less complexity and better interpretability compared with baselines.

Rare social events are infrequent yet influential incidents. Predicting such events is practically significant yet inherently challenging due to their extreme scarcity in web-based event stream. Existing studies view this task as an imbalanced classification problem and adopt static rebalancing methods to mitigate scarcity. However, they (1) ignore inter-event dependency that represents the interactions between different event streams, failing to capture precursors that lead to rare events and fundamentally limits performance. They (2) overlook intra-event dependency between different time points within single event stream, which prevents the model from adapting to shifting event patterns and degrades its generalization ability. To this end, we propose a novel Mediator Learning (ML) framework, which introduces mediators to explicitly model complex dependencies within web-based event streams. Specifically, we propose (1) Precursor Event Router (PER) that utilizes an information-theoretic routing approach to extract precursor events as mediators from massive event streams. Based on extracted mediators, (2) Conditional Hierarchical Graph Network (CHG) is introduced to model observed events, mediators and rare events into bottom-up graph levels, where its upper-level propagation is conditioned on bottom-level probability distribution. Jointly, these two modules decompose the imbalanced task into two more balanced stages, which not only mitigates the scarcity of rare events but also explicitly model inter-event dependencies, so as to capture precursor events leading to target rare events. Finally, we design (3) Adaptive Information Regularizer (AIR) to optimize the two stages. It dynamically adjusts the information flow between two stages, which models intra-event dependencies and facilitate adaption to drifting event patterns. We theoretically reveal the effectiveness of ML by framing it within Information Bottleneck (IB) principle. Extensive experiments show our superior accuracy and interpretability compared with SOTA.

Fake news video detection has become a pressing concern with the growth of short video platforms. However, previous studies have primarily focused on videos with modality-complete data, failing to handle the uncertain missing modality issue in real-world applications. They fall short in two key aspects: (1) Highly coupled feature fusion hinders the model to learn intra- and inter-modality dependencies, making it difficult to form robust multimodal representations when facing uncertain modality missing. (2) Excessive reliance on discriminative modality combinations toward fake news, which leads to inferior performance on other modality combinations. To this end, we propose a novel model for modality-incomplete fake news video detection called MyGO. It contains three modules: (1) Caption-guided Keyframe Attention (CKA) leverages embedded captions to guide feature extraction, which adaptively excludes irrelevant frames to enhance the learning of intra-modality dependencies, resulting in refined modality features. (2) Based on refined modality features from CKA, Modality Disentangling Network (MDN) is designed to decompose them into shared and specific parts, which captures fine-grained inter-modality dependencies effectively. These two kinds of dependencies help avoid coupled multimodal fusion and bridge information gaps caused by missing modalities. (3) Furthermore, missing prompts are newly introduced to explicitly mark modality combinations within each news video. By integrating missing prompts with aforementioned inter-modality dependencies within Prompt-assisted Modality Aligning (PMA) Module, we alleviate over-reliance on discriminative modality combinations and enhancing the representation of less discriminative ones. Extensive experiments showcase that MyGO achieves 3.79–4.85% improvements in accuracy, demonstrating its performance over state-of-the-art approaches under different missing conditions.

Event causality identification (ECI) aims to identify possible causal relationships between event-mention pairs in a text. In the past, ECI models mainly used classification frameworks and rarely used generative models to solve this task. Although some progress has been made, the existing approaches suffer from the following two problems: (1) In the generative approach of inter-event mention dependency paths, noise and unnecessary sentence components cannot be effectively reduced, thus limiting the ability of the model to capture the critical correlation knowledge between event mentions; and (2) Existing multi-task generative model training which uses the REINFORCE algorithm suffers from a high-variance problem that imposes limitations on capturing critical causal knowledge. Therefore, we propose a novel Structural Optimization strategy Reinforcement Learning algorithm Generation model, GenSORL. The model aims to generate causal relationships from input sentences and includes dependency path generation as a complementary task to improve the causal label prediction performance. Specifically, this approach utilizes a new dependency syntax strategy to optimize dependency-path generation and extract important ECI contextual words between event mentions. Regarding the high-variance problem, a policy gradient with baseline is proposed for training the generative model, further adopting an innovative reward function to measure the accuracy of causal prediction and generation quality. In experiments using two frequently used benchmark datasets, the proposed method outperformed state-of-the-art models.

This paper presents a feature guided multi-window area-based matching method for urban remote sensing stereo pairs. The method achieves the goal that producing dense disparity maps for urban remote sensing stereo pairs. The proposed method can be divided into four stages: feature-based matching, edge support region extraction, area-based matching and post-processing. The point feature matching is applied firstly as it provides precise matching results of few points, which can be used to restrict the searching range of area-based matching process. Then, building edges are extracted by an efficient line segment detector. In order to enhance the accuracy of the area-based matching, edge support regions are derived from these edges and multi-windows are applied during the area-based matching to preserve details of edges. Finally the post-processing including interpolation and filtering is applied to heighten the completeness and accuracy of the disparity map.