Predicting traffic speed is a crucial task in intelligent transportation systems, as it helps analyze traffic congestion and improve road flow. The complex spatio-temporal interactions present in traffic data make accurate predictions challenging. In recent years, many studies have focused on extracting and learning spatio-temporal features. Deep learning methods, particularly spatio-temporal graph learning models, show promising performance and become the mainstream approach in this area of research. However, existing methods cannot exploit and unify the heterogeneous spatio-temporal interactions hidden in traffic data to achieve multi-correlation modeling. As a result, they cannot effectively model the complex evolving patterns in traffic dynamics. To this end, we propose a heterogeneous spatio-temporal traffic graph learning framework (HSTGL) to capture these diverse spatio-temporal interactions comprehensively. In terms of design, HSTGL consists of three modules: spatio-temporal heterogeneous graph construction, spatio-temporal heterogeneous graph attention learning, and heterogeneous information supplementation. The first two modules utilize similar temporal pattern clustering and heterogeneous spatio-temporal graph attention mechanisms (HSTGAT) to learn heterogeneous spatio-temporal interactions in traffic data. The latter feature fusion module (FFM) is developed to complement potential heterogeneous information in the global spatio-temporal context. Our HSTGL conducts extensive experiments on three real-world public traffic datasets: METR-LA, PEMS-BAY, and PEMSD7M. The results demonstrate that HSTGL achieves superior predictive performance compared to representative benchmark methods with average improvements of 3.3% in MAE, 1.8% in RMSE, and 5.2% in MAPE compared to the optimal baseline.

Highway traffic flow prediction under overload scenarios (HIPO) is a critical problem in intelligent transportation systems, which aims to forecast future traffic patterns on highway segments during periods of exceptionally high demand. Despite its importance, this problem has rarely been explored in recent research due to the unique challenges posed by irregular flow patterns, complex traffic behaviors, and sparse contextual data. In this paper, we propose a Heterogeneous Spatial-Temporal graph network With Adaptive contrastiVE learning (HST-WAVE) to address the HIPO problem. Specifically, we first construct a heterogeneous traffic graph according to the physical highway structure. Then, we develop a multi-scale temporal weaving Transformer and a coupled heterogeneous graph attention network to capture the irregular traffic flow patterns and complex transition behaviors. Furthermore, we introduce an adaptive temporal enhancement contrastive learning strategy to bridge the gap between divergent temporal patterns and mitigate data sparsity. We conduct extensive experiments on two real-world highway network datasets (No. G56 and G60 in Hangzhou, China), showing that our model can effectively handle the HIPO problem and achieve state-of-the-art performance. The source code is available at https://github.com/luck-seu/HST-WAVE.

Urban region representation is crucial for various urban downstream tasks. However, despite the proliferation of methods and their success, acquiring general urban region knowledge and adapting to different tasks remains challenging. Existing work pays limited attention to the fine-grained functional layout semantics in urban regions, limiting their ability to capture transferable knowledge across regions. Further, inadequate handling of the unique features and relationships required for different downstream tasks may also hinder effective task adaptation. In this paper, we propose a Graph-based Urban Region Pre-training and Prompting framework (GURPP) for region representation learning. Specifically, we first construct an urban region graph and develop a subgraph-centric urban region pre-training model to capture the heterogeneous and transferable patterns of entity interactions. This model pre-trains knowledge-rich region embeddings using contrastive learning and multi-view learning methods. To further refine these representations, we design two graph-based prompting methods: a manually-defined prompt to incorporate explicit task knowledge and a task-learnable prompt to discover hidden knowledge, which enhances the adaptability of these embeddings to different tasks. Extensive experiments on various urban region prediction tasks and different cities demonstrate the superior performance of our framework.

Next Point-of-Interest (POI) recommendation aims to predict user's subsequent destinations based on historical check-in sequences, thereby enhancing travel experiences. While traditional methods primarily rely on unique identifiers (IDs) to represent POIs, they face data scarcity challenges. Recent multi-modal approaches offer alternatives but struggle with two key issues: inadequate handling of heterogeneity between ID and multi-modal features, and difficulties in unified framework integration, limiting their potential benefits. To address these limitations, we propose IM-POI, a novel framework that leverages the complementary strengths of both ID embeddings and multi-modal representations for next POI recommendation. In our framework, a global POI weighted transition graph inspired by TF-IDF captures sequential dependencies and enhances memorization capabilities, while a geographical graph incorporates spatial information into multi-modal features to be consistent with real-world visitation patterns. To address representation integration, we introduce an IM-Aligner module to prevent representation collapse during distribution matching. Extensive experiments on three real-world datasets demonstrate that IM-POI significantly outperforms state-of-the-art baselines.

Information diffusion can be regarded as the process of multi-user collaboration to deliver information. How to predict the cascade size is a fundamental task and has many applications such as rumor detection, product marketing, etc. Recent works attempt to mine temporal and structural characteristics hidden in the information cascade based on deep learning models. As we know, the complicated interactions between nodes are critical for cascade size prediction, and these interactions are usually hidden in the multiple types of cascade relationships. However, the cascade relationship diversity has not been comprehensively exploited by current studies. In this paper, we propose a novel model named CTformer, which leverages the global receptive field of Transformer to make accurate prediction. Specifically, CTformer takes advantage of both global position encoding and bias matrices to explore cascade relationship diversity. Extensive evaluation results on multiple real-world datasets show that CTformer achieves significant performance gains over the state-of-the-art methods.