Hypergraph Neural Networks (HNNs) have recently gained attention as a powerful approach for capturing high-order correlations through hypergraph-structured encoding and learning techniques. However, despite their potential, existing HNN methods often encounter over-smoothing issues, which limit their ability to effectively integrate global information while maintaining high-order structural details. This limitation compromises the overall effectiveness of these models. To tackle this challenge, we introduce a novel HNN framework called Hypergraph Structural Fusion (HyperSF). HyperSF combines the structural characteristics of both hypergraphs and graphs to effectively integrate global and local information while preserving the complex high-order structures inherent in hypergraphs. This structural fusion mechanism significantly improves model performance by ensuring that both types of information are utilized in a balanced manner. Comprehensive evaluations show that our method outperforms state-of-the-art approaches, demonstrating its effectiveness in hypergraph representation learning.

Hypergraph representation learning has garnered increasing attention across various domains due to its capability to model high-order relationships. Traditional methods often rely on hypergraph neural networks (HNNs) employing messagepassing mechanisms to aggregate vertex and hyperedge features. However, these methods are constrained by their dependence on hypergraph topology, leading to the challenge of imbalanced information aggregation, where high-degree vertices tend to aggregate redundant features, while low-degree vertices often struggle to capture sufficient structural features. To overcome the above challenges, we introduce HyperKAN, a novel framework for hypergraph representation learning that transcends the limitations of message-passing techniques. Hyper- KAN begins by encoding features for each vertex and then leverages Kolmogorov-Arnold Networks (KANs) to capture complex nonlinear relationships. By adjusting structural features based on similarity, our approach generates refined vertex representations that effectively addresses the challenge of imbalanced information aggregation. Experiments conducted on the real-world datasets demonstrate that HyperKAN significantly outperforms stateof-the-art HNN methods, achieving nearly a 9% performance improvement on the Senate dataset.

Existing X-ray prohibited item detection methods primarily focus on boosting the detection performance of uniformly distributed items. However, in the real-world scenarios, various prohibited items exhibit the long-tailed distribution, thus posing the huge challenge to the detection task. To support this study, we introduce LTXRay, a dedicated X-ray benchmark that better assesses long-tailed prohibited item detection. LTXRay consists of 18,718 images from 12 common classes with an imbalance factor of 280.35. Meanwhile, we propose a novel Memory-Guided Learning Network(MGLNet) to develop baseline methods on LTXRay, which enhance the within-class diversity for the tail classes and consequentially improves long-tailed object detection. Specifically, we first introduce a frequency-based feature refinement module to extract discriminative contextual representations, then store the various instance features in the memory bank and dynamically generate the sample according to the historical features. Extensive experiments have been performed on the LTXRay to demonstrate the effectiveness of the proposed method. The experimental results indicate that the proposed method can consistently improve the performance of baseline methods.

The k -core model has garnered widespread adoption for preserving essential cohesive subgraphs owing to its linear-time computability, making it particularly suitable for hypergraph analysis. However, considering the continuously evolving characteristics of real-world hypergraphs, recent research efforts have focused on developing efficient algorithms that can maintain the core value of each vertex amid structural alterations. Despite these efforts, frequent insertions and deletions in dynamic hypergraphs continue to pose significant inefficiencies, primarily due to the increased traversal overhead incurred by hyperedge insertion algorithms. This exacerbates performance disparities between handling hyperedge insertions and deletions, underscoring the persistent challenge of effective k -core analysis in hypergraphs. To effectively address these challenges, we have gained key insights that enable us to define a specific order, termed the hypergraph k -order, which significantly reduces redundant vertex traversal and narrows down the search space during hyperedge insertions. Based on the proposed hypergraph k -order, we define two indices, the order index and the pivotal index, aimed at minimizing traversal costs and expediting the hyperedge insertion algorithm. Moreover, it is essential to recognize that the recomputation of the support degree ( sd ) for all vertices following each hyperedge deletion can significantly diminish the performance efficiency of deletion algorithms. To address this, we introduce an optimized approach that leverages the incremental maintenance of the support degree ( sd ) value to expedite the hyperedge deletion process. By leveraging these optimizations, we introduce a novel Order-based Traversal core Maintenance methodology, designated as OTM , which markedly enhances the efficiency of core maintenance in dynamic hypergraphs. Our comprehensive evaluation, which covers 12 real-world hypergraph datasets and a synthetic dataset, reveals that OTM achieves staggering speedup, outperforming the state-of-the-art approach with a 41,420 \(\times\) speedup in the insertion algorithm and 8,284 \(\times\) speedup in the deletion algorithm, underscoring its remarkable efficiency and effectiveness.

Many real-world networks are characterized by being temporal and dynamic, wherein the temporal information signifies the changes in connections, such as the addition or removal of links between nodes. Employing random walks on these temporal networks is a crucial technique for understanding the structural evolution of such graphs over time. However, existing state-of-the-art sampling methods are designed for traditional static graphs, and as such, they struggle to efficiently handle the dynamic aspects of temporal networks. This deficiency can be attributed to several challenges, including increased sampling complexity, extensive index space, limited programmability, and a lack of scalability. In this article, we introduce TEA+ , a robust, fast, and scalable engine for conducting random walks on temporal graphs. Central to TEA+ is an innovative hybrid sampling method that amalgamates two Monte Carlo sampling techniques. This fusion significantly diminishes space complexity while maintaining a fast sampling speed. Additionally, TEA+ integrates a range of optimizations that significantly enhance sampling efficiency. This is further supported by an effective graph updating strategy, skilled in managing dynamic graph modifications and adeptly handling the insertion and deletion of both edges and vertices. For ease of implementation, we propose a temporal-centric programming model, designed to simplify the development of various random walk algorithms on temporal graphs. To ensure optimal performance across storage constraints, TEA+ features a degree-aware hybrid storage architecture, capable of adeptly scaling in different memory environments. Experimental results showcase the prowess of TEA+ , as it attains up to three orders of magnitude speedups compared to current random walk engines on extensive temporal graphs.