Graph anomaly detection faces significant challenges due to the scarcity of reliable anomaly-labeled datasets, driving the development of unsupervised methods. Graph autoencoders (GAEs) have emerged as a dominant approach by reconstructing graph structures and node features while deriving anomaly scores from reconstruction errors. However, relying solely on reconstruction error for anomaly detection has limitations, as it increases the sensitivity to noise and overfitting. To address these issues, we propose Graph Evidential Learning (GEL), a probabilistic framework that redefines the reconstruction process through evidential learning. By modeling node features and graph topology using evidential distributions, GEL quantifies two types of uncertainty: graph uncertainty and reconstruction uncertainty, incorporating them into the anomaly scoring mechanism. Extensive experiments demonstrate that GEL achieves state-of-the-art performance while maintaining high robustness against noise and structural perturbations.

Highly skewed long-tail item distribution commonly hurts model performance on tail items in recommendation systems, especially for graph-based recommendation models. We propose a novel idea to learn relations among items as an auxiliary graph to enhance the graph-based representation learning and make recommendations collectively in a coupled framework. This raises two challenges, 1) the long-tail downstream information may also bias the auxiliary graph learning, and 2) the learned auxiliary graph may cause negative transfer to the original user-item bipartite graph. We innovatively propose a novel Meta Graph Learning framework for long-tail recommendation (MGL) for solving both challenges. The meta-learning strategy is introduced to the learning of an edge generator, which is first tuned to reconstruct a debiased item co-occurrence matrix, and then virtually evaluated on generating item relations for recommendation. Moreover, we propose a popularity-aware contrastive learning strategy to prevent negative transfer by aligning the confident head item representations with those of the learned auxiliary graph. Experiments on public datasets demonstrate that our proposed model significantly outperforms strong baselines for tail items without compromising the overall performance.

Hypergraph-based collaborative filtering for recommendations has emerged as an important research topic due to its ability to model complex relations among users and items. However, most existing methods typically construct the hypergraph structures using heuristics (e.g., motifs and jump connections) based on existing graphs (e.g., user-item bipartite graphs and social networks). From a learning perspective, we argue that the fixed heuristic topology of hypergraph may become a limitation and thus potentially compromise the recommendation performance. To tackle this issue, we propose a novel dynamic hypergraph learning framework for collaborative filtering (DHLCF), which learns hypergraph structures and makes recommendations collectively in a unified framework. In the hypergraph learning process, we solve two main challenges, i.e., 1) optimization issue and 2) regularization issue. Firstly, we propose a differentiable hypergraph learner to adaptively learn the optimized hypergraph structures dynamically for the hypergraph convolutions during the training process. Secondly, to better regularize dynamic hypergraph learning, we introduce a novel hypergraph learning objective, which forces the learned hypergraphs to retain the original graph topology. Extensive experiments on public datasets from different domains are provided to show that our proposed model significantly outperforms strong baselines.

Users’ social connections have recently shown significant benefits to session-based recommendations, and graph neural networks have demonstrated great success in learning the pattern of information flow among users. However, the current paradigm presumes a given social network, which is not necessarily consistent with the fast-evolving shared interests and is expensive to collect. We propose a novel idea to learn the graph structure among users and make recommendations collectively in a coupled framework. This idea raises two challenges, i.e., scalability and effectiveness. We introduce a novel graph-structure learning framework for session-based recommendations (GSL4Rec) for solving both challenges simultaneously. Our framework has a two-stage strategy, i.e., the coarse neighbor screening and the self-adaptive graph structure learning, to enable the exploration of potential links among all users while maintaining a tractable amount of computation for scalability. We also propose a phased heuristic learning strategy to sequentially and synergistically train the graph learning part and recommendation part of GSL4Rec, thus improving the effectiveness by making the model easier to achieve good local optima. Experiments on five public datasets demonstrate that our proposed model significantly outperforms strong baselines, including state-of-the-art social network-based methods.

Infrared and visible image fusion is an active area in digital image processing. Many methods in spatial or transform domains have been proposed, but there are still several complex challenges. In this paper, we introduce a three-scale image transformation, which possesses multi-scale, translation-invariance and spatial-localization characteristics that are very important for image fusion. The decomposition can be implemented sequentially by shear transform, undecimated framelet transform and average filtering. Furthermore, we propose a multi-direction image fusion method based on the transformation, where the guided filtering based fusion rule and regional energy fusion rule are used. Experimental results show that the proposed method outperforms some representative methods, such as the methods based on Laplacian pyramid, wavelets, guided filtering, framelet, and the combination of nonsub-sampled contourlet transform and nonsubsampled shearlet transform.