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Sichao Fu

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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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