Graph unlearning aims to provably remove some training data from graph neural networks (GNNs) while eliminating their impact on model predictions. Although retraining the GNNs from scratch is a direct and legitimate solution, it entails substantial computational resources. To address this issue, graph unlearning methods have been proposed in the domain of graph data. However, applying existing graph unlearning methods directly to hypergraph data affects model utility. Specifically, the graph unlearning methods severely damage the higher-order structural information of hypergraphs and fail to remove all the information that needs to be unlearned. In this paper, we propose Hyperedge Size-Based Core-Sharing Decomposition, a novel hypergraph unlearning framework tailored to the structural characteristics of hypergraph data. Its contributions include a subgraph partitioning method specific to hypergraphs and an aggregation method based on node coverage. We conduct extensive experiments on seven real-world hypergraph datasets to demonstrate the unlearning efficiency and model utility. Compared to the baseline methods, our approach achieves up to 5% higher accuracy and reduces the average unlearning time by 28%. Furthermore, our node-coverage-based aggregation approach achieves up to 6% higher accuracy.

Saliency region detection methods have become one of the hotspots in the field of image processing as an important method to improve the real-time and accurate analysis of massive data. Integrating more effective prior knowledge is a viable direction for improving the performance of saliency region detection methods. Most of the methods based on background prior and boundary connectivity prior assume the boundary area of the image as the background, by restraining the background to highlight the salient area. When the boundary area of the image does not describe the background well (such as a large difference in border area features), if the entire frame of the image is put together to compute the background feature, the calculation of the background feature will be inaccurate. In view of the above shortcomings, this paper proposed a saliency region detection method based on background and spatial position. This method carried on the image boundary super pixel clustering, determined the background feature according to the clustering center, and used the difference between the super pixel on the image and the background super pixel, and its spatial position to calculate the salient of the super pixels. This approach used MATLAB to program and experiment. The method was compared with a series of the state-of-the-art methods. The AUC of proposed algorithm reaches 0.839, and the MAE is 0.220, showing the effectiveness of the proposed algorithm.