The inherent fluctuations in the stock market present significant challenges in understanding stock dynamics, especially for investment decisions based on stock ranking. Recent advancements in learning-based methods have led to promising results in exploring temporal dependencies to understand stock movements. However, they often assume stable, certain, and reliable environments, narrowing their insight into the complex and fluctuating nature of markets. This complexity is driven by two influential factors: the explicit consistency of dynamic yet stable trends across diverse temporal patterns, coupled with the implicit interplay of logic and possibility under uncertainty. Hence, we introduce aSensitivity-awareDependencyLearning solution (SDL) for stock ranking. With bridging the ideal and reality in mind, SDL captures short-term fluctuations under the guidance of long-term dependencies, associated with the augmentation of counterfactual knowledge. Specifically, SDL devises aShort-termCo-integrationDetector (SCD) that concentrates on capturing time-varying correlations and immediate market reactions, in addition to multi-period attention. Furthermore, aLong-termCo-movementsTracker (LCT) takes advantage of enduring industry relationships and incorporates counterfactual knowledge, allowing the model to generalize beyond observed patterns and identify diverse long-term trends. Comprehensive experiments on five real-world stock markets demonstrate that our proposed SDL outperforms several representative baselines.

High-quality embeddings for urban regions have enabled influential insights into urban structures and characteristics, facilitating the creation of more sustainable cities. However, the existing practices still face certain challenges, notably: (1) When multiple views contain distinct semantic information, ignoring the reliability and possibly inadequate collection differences (e.g., data missingness) among those views may degrade the representation robustness. (2) Consensus semantics extracted from different views are often fused in a simplistic manner, without considering the uniformity of embeddings (quality variations) and the complementarity between views. To address such challenges, we propose a novel Adaptive Risk-aware Consensus learning (ARC) solution for urban region embeddings. Specifically, we design both local- and region-level masking within the inter-view representation, following the paradigm of masked autoencoders, to better handle uncertainty risks. More importantly, we introduce a self-weighted contrastive mechanism in consensus learning to achieve maximum alignment and mitigate degradation. To enhance the uniformity of embeddings, we employ entropy, ensuring the diversity and complementarity of information. Ultimately, we apply the learned embeddings to down-stream tasks, demonstrating remarkable improvements compared to several representative baselines.

The automatic and accurate detection of online fake news is crucial to society, drawing significant attention from both industry and academia. With news content becoming increasingly multimodal, assessing its truthfulness has become more challenging. Existing efforts to combat multimodal fake news primarily follow a target-egocentric paradigm, which makes predictions based solely on features extracted from the target news and its associated social context. However, their performance is constrained by the inherent knowledge paucity within the target news. To address this challenge, we propose ReTIP, a novel retrieval-enhanced framework for multimodal fake news detection. ReTIP enriches the knowledge of target news by retrieving relevant news content, along with potential diffusion participants. Specifically, ReTIP retrieves relevant content from a local content pool using a key vector generated through the joint modeling of text and images, and employs a communitybased strategy to retrieve potential participants from a historical user interaction pool. Additionally, ReTIP employs a hypergraphbased information enhancement module to align knowledge across modalities and instances at a fine-grained level by capturing higher-order correlations. Finally, an attention-based fusion layer is employed to aggregate the multi-element knowledge from retrieved instances, which is then concatenated with the target news knowledge for the final prediction. Extensive experiments on three real-world multimodal fake news datasets not only demonstrate the superior performance of ReTIP compared to state-of-the-art baselines but also confirm the effectiveness of its individual components. Our code is made publicly available at https://github.com/xytitor/ReTIP.

Optimizing stock selection through stock ranking is one of the critical but intricate tasks in quantitative trading areas because of the non-stationary dynamics and complicated interdependencies behind stock markets. Recent studies have made efforts to model historical market movements to enhance stock selection. However, they primarily borrowed the spirit of time series modeling and sought to build a deterministic paradigm without considering the uncertain fluctuations. In addition, some of these studies tailor to explore stock correlations from a predefined (e.g., binary) graph structure and use explicitly simple relations (such as first-order relations) to guide evolving interactions. Nevertheless, aggregating predefined but shallow relationships to collaborate with stock movements may affect selection generalizability and increase the risk of portfolio failure. This study introduces a novelRelational stock selection framework via probabilisticStateSpaceLearning (orRSSL) for stock selection. Specifically, RSSL first attempts to build a tree-based structure to explicitly expose higher-order relations in the stock market, primarily by discovering a hierarchical delineation of ties between stocks. Whereafter, it couples with time-varying movements via an attention mechanism to smoothly explore the interactive correlations among different stocks. Inspired by recent state space models (SSM) in probabilistic Bayesian learning, we devise a Probabilistic Kalman Network (PKNet) with uncertainty estimates to recursively simulate ever-changing stock volatility, enabling more promising return-risk trade-offs. The experimental results on several real-world stock market datasets demonstrate that RSSL outperforms several representative baseline methods by a significant margin.

Graph neural networks, as well as attention mechanisms, have gained widespread popularity for traffic flow forecasting due to their capacity to incorporate the complicated interactions behind flow dynamics. However, existing solutions either formulate a graph-based skeleton with narrow (e.g., static) interaction capture or build the spatiotemporal (e.g., dynamic) attention without proper comprehension of diverse risks, which inevitably burdens the generalization of high-accuracy traffic trends. In this study, we introduce Gboot (Graph bootstrap) enhancement framework for traffic flow forecasting. Gboot takes the traffic flow forecasting problem from a dependency dynamic learning perspective by treating each traffic sensor as the graph node while regarding the observed flows at each sensor as the node feature. In addition to exposing the explicit spatial connectivity behind traffic flows, we hierarchically devise temporal-aware and factual-aware graph learning blocks to consider temporal interactive dynamics and factual interactive dynamics. The former shows the trend dependencies behind flow signals and the latter uncovers different views of traffic situations (e.g., current observation vs. historical observation). More importantly, we present a Dual-view Bootstrap (DvBoot) mechanism in Gboot, which includes both risk-free and risk-aware stands. DvBoot attempts to flexibly align these two views in the latent space to enhance the generalization capability of capturing dynamic dependencies. Experiments on several real-world traffic datasets demonstrate the superiority of our Gboot over representative approaches.