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.

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.

Information diffusion prediction, which aims to infer the infected behavior of individual users during information spread, is critical for understanding the dynamics of information propagation and users' influence on online social media. To date, existing methods either focus on capturing limited contextual information from a single cascade, overlooking the potentially complex dependencies across different cascades, or they are committed to improving model performance by using intricate technologies to extract additional features as supplements to user representations, neglecting the drift of model performance across different platforms. To address these limitations, we propose a novel framework called CARE (CAscade-REtrieved In-Context Learning) inspired by the concept of in-context learning in LLMs. Specifically, CARE first constructs a prompts pool derived from historical cascades, then utilizes ranking-based search engine techniques to retrieve prompts with similar patterns based on the query. Moreover, CARE also introduces two augmentation strategies alongside social relationship enhancement to enrich the input context. Finally, the transformed query-cascade representation from a GPT-type architecture is projected to obtain the prediction. Experiments on real-world datasets from various platforms show that CARE outperforms state-of-the-art baselines in terms of effectiveness and robustness in information diffusion prediction.

Exploring and modeling the spreading process of rumors have shown great potential in improving rumor detection performance. However, existing propagation‐based rumor detection models often overlook the uncertainty of the underlying propagation structure and typically require a large amount of labeled data for training. To address these challenges, we propose a novel rumor detection framework, namely, the Uncertainty‐Inference Contrastive Learning (UICL) model. Specifically, UICL innovatively incorporates an edge‐wise augmentation strategy into the general contrastive learning framework, including an edge‐inference augmentation component and an EdgeDrop augmentation component, which primarily aim to capture the edge uncertainty of the propagation structure and alleviate the sparsity problem of the original dataset. A new negative sampling strategy is also introduced to enhance contrastive learning on rumor propagation graphs. Furthermore, we use labeled data to fine‐tune the detection module. Our experiments, conducted on three real‐world datasets, demonstrate that UICL can not only significantly improve detection accuracy but also reduce the dependency on labeled data compared to state‐of‐the‐art baselines.

Information cascade size prediction is one of the primary challenges for understanding the diffusion of information. Traditional feature-based methods heavily rely on the quality of handcrafted features, requiring extensive domain knowledge and hard to generalize to new domains. Recently, inspired by the success of deep learning in computer vision and natural language processing, researchers have developed neural network-based approaches for tackling this problem. However, existing deep learning-based methods either focused on modeling the temporal characteristics of cascades but ignored the structural information or failed to take the order-scale and position-scale into consideration in modeling structures of information propagation. This paper proposed a novel graph neural network-based model, called MUCas, to learn the latent representations of cascade graphs from a multi-scale perspective, which can make full use of the direction-scale, high-order-scale, position-scale, and dynamic-scale of cascades via a newly designed MUlti-scale Graph Capsule Network (MUG-Caps) and the influence-attention mechanism. Extensive experiments conducted on two real-world data sets demonstrate that our MUCas significantly outperforms the state-of-the-art approaches.

Researchers have exerted tremendous effort in designing ways to detect and identify rumors automatically. Traditional approaches focus on feature engineering, which requires extensive manual efforts and are difficult to generalize to different domains. Recently, deep learning solutions have emerged as the de facto methods which detect online rumors in an end-to-end manner. However, they still fail to fully capture the dissemination patterns of rumors. In this study, we propose a novel diffusion-based rumor detection model, called Macroscopic and Microscopic-aware Rumor Detection, to explore the full-scale diffusion patterns of information. It leverages graph neural networks to learn the macroscopic diffusion of rumor propagation and capture microscopic diffusion patterns using bidirectional recurrent neural networks while taking into account the user-time series. Moreover, it leverages knowledge distillation technique to create a more informative student model and further improve the model performance. Experiments conducted on two real-world data sets demonstrate that our method achieves significant accuracy improvements over the state-of-the-art baseline models on rumor detection.

Effectively predicting the size of an information cascade is critical for many applications spanning from identifying viral marketing and fake news to precise recommendation and online advertising. Traditional approaches either heavily depend on underlying diffusion models and are not optimized for popularity prediction, or use complicated hand-crafted features that cannot be easily generalized to different types of cascades. Recent generative approaches allow for understanding the spreading mechanisms, but with unsatisfactory prediction accuracy. To capture both the underlying structures governing the spread of information and inherent dependencies between re-tweeting behaviors of users, we propose a semi-supervised method, called Recurrent Cascades Convolutional Networks (CasCN), which explicitly models and predicts cascades through learning the latent representation of both structural and temporal information, without involving any other features. In contrast to the existing single, undirected and stationary Graph Convolutional Networks (GCNs), CasCN is a novel multi-directional/dynamic GCN. Our experiments conducted on real-world datasets show that CasCN significantly improves the prediction accuracy and reduces the computational cost compared to state-of-the-art approaches.

Effectively modeling and predicting the information cascades is at the core of understanding the information diffusion, which is essential for many related downstream applications, such as fake news detection and viral marketing identification. Conventional methods for cascade prediction heavily depend on the hypothesis of diffusion models and hand-crafted features. Owing to the significant recent successes of deep learning in multiple domains, attempts have been made to predict cascades by developing neural networks based approaches. However, the existing models are not capable of capturing both the underlying structure of a cascade graph and the node sequence in the diffusion process which, in turn, results in unsatisfactory prediction performance. In this paper, we propose a deep multi-task learning framework with a novel design of shared-representation layer to aid in explicitly understanding and predicting the cascades. As it turns out, the learned latent representation from the shared-representation layer can encode the structure and the node sequence of the cascade very well. Our experiments conducted on real-world datasets demonstrate that our method can significantly improve the prediction accuracy and reduce the computational cost compared to state-of-the-art baselines.