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Weihua Ou

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AAAI Conference 2026 Conference Paper

Towards Multiple Missing Values-resistant Unsupervised Graph Anomaly Detection

Jiazhen Chen
Xiuqin Liang
Sichao Fu
Zheng Ma
Weihua Ou

Unsupervised graph anomaly detection (GAD) has received increasing attention in recent years. It aims to identify anomalous data patterns using only unlabeled node information from graph-structured data. However, prevailing unsupervised GAD methods typically assume complete node attributes and structural information-a condition that is seldom satisfied in real-world scenarios due to privacy constraints, collection errors, or dynamic node arrivals. Standard imputation strategies risk "repairing" rare anomalous nodes so that they appear normal, thereby introducing imputation bias into the detection process. Moreover, when both node attributes and edges are missing simultaneously, estimation errors in one view can contaminate the other, causing cross-view interference that further degrades detection performance. To address these challenges, we propose M²V-UGAD, a multiple-missing-values-resistant unsupervised GAD framework for incomplete graphs. Specifically, we introduce a dual-pathway encoder that independently reconstructs missing node attributes and graph structure, preventing errors in one view from propagating to the other. The two pathways are then fused and regularized within a joint latent space such that normal nodes occupy a compact inner manifold while anomalies lie on an outer shell. Finally, to mitigate imputation bias, we sample latent codes just outside the normal region and decode them into realistic node features and subgraphs, yielding hard negative examples that sharpen the decision boundary. Experiments on seven public benchmarks show that M²V-UGAD consistently outperforms existing unsupervised GAD methods across a range of missing rates.

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EAAI Journal 2026 Journal Article

Uncertainty-aware adaptive feature completion networks for incomplete multi-view learning

Wenzheng Wang
Sichao Fu
Jun Wang
Baodi Liu
Chaofeng Tang
Weihua Ou

Incomplete multi-view learning (IMVL) has emerged as a prominent research focus, aiming to address the challenge of missing views by effectively utilizing available information while exploiting the inherent consistency and complementarity across different views. Among the major approaches in this field, feature reconstruction-based IMVL methods restore the structural integrity of the original feature through complex generation strategies. However, such methods tend to overlook the accuracy of reconstructed features for missing views, as they lack mechanisms to assess their reliability. This limitation often results in inaccurately reconstructed features being displaced within the multi-view fusion space, where they fail to align with their true semantic regions and ultimately lead to misclassification. To address these issues, we propose an uncertainty-aware adaptive feature completion network (UAFCN) for incomplete multi-view learning. UAFCN incorporates a multi-view evidence fusion module that explicitly quantifies the confidence of features for missing views, thereby reducing the influence of inaccurate reconstructions during the fusion process. Furthermore, an uncertainty constraint loss is introduced to limit the misleading effects of conflicting supervisory signals, which enhances the reliability of classifier decision boundaries. The framework also includes an adaptive pseudo-label generation module, which dynamically selects high-confidence pseudo-labels across all views via adaptive thresholding to further mitigate category misclassification. Extensive experiments conducted on four benchmark datasets across two multi-view learning tasks and seven different missing rates consistently demonstrate that our proposed UAFCN outperforms existing IMVL methods.

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EAAI Journal 2024 Journal Article

Discriminative transfer regression for low-rank and sparse subspace learning

Zhonghua Liu
Weihua Ou
Jinbo Liu
Kaibing Zhang
Zhihui Lai
Hao Xiong

In the paper, we present a new transfer subspace learning algorithm termed discriminative transfer regression (DTR) for cross-domain image recognition, in which low-rank representation (LRR), discriminative regression, local geometry preserving, and different norm minimization are integrated in to a united framework for transfer learning. Firstly, both the global structure and the local geometry information of the original data are preserved by imposing low-rank and sparse constraints on the reconstruction coefficient matrix. Secondly, DTR algorithm can overcome the disturbance of outliers and noises. Thirdly, the local manifold structure of the observed samples with the same semantics from the source and target domains is captured by the adaptive weight graph. Fourthly, the discriminative information of the samples from the source domain is encoded to the target domain based on ridge regression (RR). Meanwhile, the small-class problem confronted by RR and its extensions, that the obtained projection matrix is limited by the number of classes, can be effectively solved. In addition, the convergence and computational complexity of DTR algorithm is analyzed. An extensive range of experiments on several cross-domain image databases demonstrate the superiority of the DTR algorithm.

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EAAI Journal 2020 Journal Article

Discriminative sparse embedding based on adaptive graph for dimension reduction

Zhonghua Liu
Kaiming Shi
Kaibing Zhang
Weihua Ou
Lin Wang

The traditional manifold learning methods usually utilize the original observed data to directly define the intrinsic structure among data. Because the original samples often contain a deal of redundant information or it is corrupted by noises, it leads to the unreliability of the obtained intrinsic structure. In addition, the intrinsic structure learning and subspace learning are completely separated. For solving above problems, this paper presents a novel dimension reduction method termed discriminative sparse embedding (DSE) based on adaptive graph. By projecting the original samples into a low-dimensional subspace, DSE learns a sparse weight matrix, which can reduce the effects of redundant information and noises of the original data, and uncover essential structural relationship among the data. In DSE, the robust subspace is learned from the original data. Meanwhile, the intrinsic local structure and the optimal subspace can be simultaneously learned, in which they are mutually improved, and the accurate structure can be captured, and the optimal subspace can be obtained. We propose an alternative and iterative method to solve the DSE model. In order to evaluate the performance of DSE, it is compared with some state-of-the-art feature extraction algorithms. Various experiments show that our DSE is effective and feasible.

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