Graph structure learning (GSL) has emerged as a promising approach for optimizing graph structures to enhance downstream task performance. However, the quadratic complexity of GSL renders it impractical for large graphs, such as social networks. While several attempts have been made to mitigate the scalability issue of GSL, they struggle to ensure efficiency and expressiveness simultaneously. In this paper, we propose a novel scalable and expressive graph structure learning framework (SAVE-GSL), that models all-pair interactions with linear complexity. Specifically, we design cluster and expander sparse patterns for efficient expressiveness enhancement, and adaptively fuse them to generate the optimized graph. Moreover, leveraging these sparse patterns, we theoretically prove that SAVE-GSL is an efficient universal approximator of permutation-equivariant functions, providing a formal justification for its superior expressiveness. Extensive experiments demonstrate that SAVE-GSL outperforms state-of-the-art schemes in both efficiency and accuracy on social network datasets and other graph datasets of various scales.

Medical Visual Question Answering (MVQA) requires AI models to answer questions related to medical images, offering significant potential to assist medical professionals in evaluating and diagnosing diseases, thereby improving early interventions. However, existing MVQA datasets primarily focus on basic questions regarding visual perception and pattern recognition, without addressing the more complex questions that are critical in clinical diagnosis and decision-making. This paper introduces a new benchmark designed for professional-level medical reasoning, simulating the decision-making process. We achieve this by collecting MRI and clinical data related to Hypoxic-Ischemic Encephalopathy, enriched with expert annotations and insights. Building on this data, we generate clinical question-answer pairs and MRI interpretations to enable comprehensive diagnosis, interpretation, and prediction of neurocognitive outcomes. Our evaluation of current large vision-language models (LVLMs) shows limited performance on this benchmark, highlighting both the challenges of the task and the importance of this benchmark for advancing medical AI. Furthermore, we propose a novel ``Clinical Graph of Thoughts" model, which integrates domain-specific medical knowledge and clinical reasoning processes with the interpretive abilities of LVLMs. The model demonstrates promising results, achieving around 15\% absolute gain on the most important neurocognitive outcome task, while the benchmark still reveals substantial opportunities for further research innovation.

In urban planning, traffic flow prediction, a core component of Intelligent Transportation Systems, has made significant progress with the development of deep learning. The key problem of traffic flow prediction lies in capturing the complex spatio-temporal correlations in traffic flow. In recent years, more and more research has tended to apply Transformer-based models to solve this problem. However, Transformer-based models have two major limitations for traffic flow prediction: i) Most methods only focus on extracting data features within the attention head, while ignoring the correlation between these heads, making it difficult to integrate the multi-features of traffic data; ii) Most methods do not recognize the unique impact of nodes that serve as pivotal traffic hubs in the traffic networks, which cause the Transformer to excessively focus on the influence of non-pivotal nodes. In this study, we propose a novel Transformer-based model, the Multi-Feature Fusion Transformer (MF2former), aimed at addressing the above limitations of traffic flow prediction. MF2former incorporates the Augmented Synergistic Transformer Module to achieve a comprehensive multi-feature fusion of traffic data by enhancing the information capacity within self-attention heads and performing information fusion between these heads. Additionally, our model incorporates the Pivotal Node Module, which extracts pivotal nodes from all nodes and masks the global receptive field of the Transformer to enhance its focus on pivotal nodes in the traffic network. Our model is evaluated using two real-world traffic datasets, demonstrating superior performance compared to existing methods. This study provides a stable framework for accurate traffic flow prediction, offering valuable insights for urban planners and commuters.

Understanding and reasoning about objects' physical properties in the natural world is a fundamental challenge in artificial intelligence. While some properties like colors and shapes can be directly observed, others, such as mass and electric charge, are hidden from the objects' visual appearance. This paper addresses the unique challenge of inferring these hidden physical properties from objects' motion and interactions and predicting corresponding dynamics based on the inferred physical properties. We first introduce the Compositional Physical Reasoning (ComPhy) dataset. For a given set of objects, ComPhy includes limited videos of them moving and interacting under different initial conditions. The model is evaluated based on its capability to unravel the compositional hidden properties, such as mass and charge, and use this knowledge to answer a set of questions. Besides the synthetic videos from simulators, we also collect a real-world dataset to show further test physical reasoning abilities of different models. We evaluate state-of-the-art video reasoning models on ComPhy and reveal their limited ability to capture these hidden properties, which leads to inferior performance. We also propose a novel neuro-symbolic framework, Physical Concept Reasoner (PCR), that learns and reasons about both visible and hidden physical properties from question answering. Leveraging an object-centric representation, PCR utilizes videos and the associated natural language to infer objects' physical properties without dense object annotations. Furthermore, It incorporates property-aware graph networks to approximate the dynamic interactions among objects. PCR also employs a semantic parser to convert questions into semantic programs, and a program executor to execute the programs based on the learned physical properties and dynamics. After training, PCR demonstrates remarkable capabilities. It can detect and associate objects across frames, ground visible and hidden physical properties, make future and counterfactual predictions, and utilize these extracted representations to answer challenging questions. We hope the proposed ComPhy dataset and the PCR model present a promising step towards more comprehensive physical reasoning in AI systems.

Accurate and efficient traffic flow forecasting plays an essential role in urban management which is conductive to traffic safety and travel experience. Despite the tremendous progress of attention-based traffic flow forecasting methods, complicated spatial and temporal correlations of traffic data pose difficulty for the network in simultaneously capturing the long-range contexts and the local feature details, leading to limited performance. To this end, we propose Convolution Augmented Graph Sampling Transformer Network (CASTNet) for accurate traffic flow prediction. Specifically, we design a hybrid graph transformer module to utilize the graph convolutions to perceive local structural details, which are incorporated into the transformer to augment the attention computation for more accurate global dependency modeling. Moreover, we develop a sampling-based attention layer based on the approximated sampling strategy to further optimize the graph structure, which is implemented with linear complexity. Finally, the spatial features from the graph transformer module are fused with the temporal features from the sample convolution and interaction network. Experimental results on three real-world traffic flow datasets demonstrate the superiority of the proposed CASTNet over existing schemes.

Sequential recommendation aims to anticipate the next preference of users by examining their recent interactions. Recently, graph neural networks (GNNs) have been widely utilized in sequential recommendation, but existing schemes focus on interactions within individual sequences and tend to connect irrelevant items in case of insufficient historical data. In this work, we propose Sequence Extension Augmented GNN (SEA-GNN) which augments the node representation learning with inter-sequence global context aggregation while maintaining intra-sequence local preference. Specifically, we augment the graph construction with sequence extension that diversifies the item connections to exploit global context and robustify the node representation against data insufficiency. Meanwhile, we extract local preference based on the intra-sequence user-item graph to enhance the node representation with user-specific interest. Experimental results demonstrate the superiority of the proposed algorithm compared with existing schemes in recommendation performance.