In this paper, graph neural networks are applied to hyperspectral anomaly detection, treating each pixel as a node, the correlations between pixels as edges, and the spectral features of each pixel as node attributes, thus fully utilizing both spatial and spectral information in hyperspectral data. Due to the phenomenon of ‘right-shifted’ spectral energy caused by the presence of anomalous targets, the beta wavelet graph neural network detector (BWGNND) is proposed in this paper, which takes advantage of this phenomenon using beta graph wavelets to generate an efficient, localized bandpass filter to accurately capture anomalous targets. Experimental results show that the proposed method has high accuracy and robustness in hyperspectral anomaly detection.

Multi-behavior recommendation aims to model the interaction information of multiple behaviors to enhance the target behavior’s recommendation performance. Despite progress in recent research, it is challenging to represent users’ preferences using the multi-feature behavior information of user interactions. In this paper, we propose a Multi-Feature Behavior Relationship for Multi-Behavior Recommendation (MFBR) framework, which models the multi-behavior recommendation problem from both sequence structure and graph structure perspectives for user preference prediction of target behaviors. Specifically, the MFBR model is designed with a sequence encoder and a graph encoder to construct behavioral representations of different aspects of the user; the correlations between behaviors are modeled by a behavioral relationship encoding layer, and the importance of different behaviors is finally learned in order to construct the final representation of user preferences. Experimental validation conducted on two real-world recommendation datasets shows that our MFBR consistently outperforms state-of-the-art methods.