Large Language Models (LLMs) extended to multi-modal inputs have led to Multi-modal LLMs (MLLMs) that perform strongly on vision-language tasks. Recent MLLMs adopt multi-resolution inputs to capture both global context and local details, but this substantially increases visual tokens and computational cost. Existing pruning methods reduce redundancy but are designed for single-resolution settings, overlooking the characteristics of multi-resolution tokens. We observe two key properties: tokens from different resolutions follow distinct distributions of information content, and tokens across resolutions exhibit mutual complementarity, such that pruning one type can often be compensated by the other. Based on this observation, we propose Multi-Resolution Token Pruning method (MR-Pruner), a training-free, graph-based pruning framework for multi-resolution MLLMs. MR-Pruner incorporates three components—Intra-resolution, Cross-resolution Token Scoring, and Informativeness-aware Token Pruning—that adaptively allocate pruning ratios and facilitate information propagation across resolutions. Experiments on eight benchmarks show that MR-Pruner achieves superior efficiency–performance trade-offs. For example, when only 10% of the visual tokens are retained, it leads to an average performance degradation of 3.6%. For reproducibility, the source code is available at https://github.com/gooriiie/MR-Pruner.

To leverage the complex structures within heterogeneous graphs, recent studies on heterogeneous graph embedding use a hyperbolic space, characterized by a constant negative curvature and exponentially increasing space, which aligns with the structural properties of heterogeneous graphs. However, despite heterogeneous graphs inherently possessing diverse power-law structures, most hyperbolic heterogeneous graph embedding models use a single hyperbolic space for the entire heterogeneous graph, which may not effectively capture the diverse power-law structures within the heterogeneous graph. To address this limitation, we propose Multi-hyperbolic Space-based heterogeneous Graph Attention Network (MSGAT), which uses multiple hyperbolic spaces to effectively capture diverse power-law structures within heterogeneous graphs. We conduct comprehensive experiments to evaluate the effectiveness of MSGAT. The experimental results demonstrate that MSGAT outperforms state-of-the-art baselines in various graph machine learning tasks, effectively capturing the complex structures of heterogeneous graphs.

Traffic forecasting is a crucial application of the Intelligent Transportation System (ITS), with research focusing on various methods, from classical statistical approaches to graph-based methods integrated with RNN-based approaches to capture spatial and temporal correlations simultaneously. During the traffic data collection phase, the absence of vehicles on each road or sensor malfunctions can result in the collection of traffic time series data as zeros. However, storing such zero values makes accurate traffic prediction more challenging. To address this challenge, we present a novel model for improving traffic forecasting using graph convolutional recurrent neural networks. The proposed method is evaluated on two real-world public benchmark datasets and compared with six baseline models, showcasing its superior performance.