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Huijun Wu

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4 papers

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AAAI Conference 2026 Conference Paper

DeloopSGNN: Revisiting Spectral GNNs Through the Lens of Spatial Aggregation

Duanyu Li
Huijun Wu
Min Xie
Kai Lu
Wenzhe Zhang
Zhenwei Wu
Yong Dong
Ruibo Wang

Graph Neural Networks (GNNs) have been studied from two primary perspectives: spectral, which employs global graph signal filtering and is theoretically more expressive, and spatial, which builds on local neighborhood aggregation and generalizes well across diverse graph structures. While spectral GNNs are expected to perform better in theory, they often underperform in practice compared to spatial models. To better understand this gap, we introduce a novel theoretical framework for converting spectral GNNs into the spatial domain, allowing for more intuitive analysis. This transformation reveals that signal looping and repeated high-order aggregation are major causes of over-smoothing in spectral GNNs. By addressing these issues in the spatial domain and converting the model back to the spectral domain, we propose DeloopSGNN, a spectral GNN with improved expressive capacity. Experiments on benchmark datasets show that DeloopSGNN achieves consistently strong performance in terms of accuracy and adversarial robustness, demonstrating that spectral GNNs can benefit significantly from careful architectural design grounded in our proposed framework.

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NeurIPS Conference 2025 Conference Paper

Incomplete Multi-view Deep Clustering with Data Imputation and Alignment

Jiyuan Liu
Xinwang Liu
Xinhang Wan
Ke Liang
Weixuan Liang
Sihang Zhou
Huijun Wu
Kehua Guo

Incomplete multi-view deep clustering is an emerging research hot-pot to incorporate data information of multiple sources or modalities when parts of them are missing. Most of existing approaches encode the available data observations into multiple view-specific latent representations and subsequently integrate them for the next clustering task. However, they ignore that the latent representations are unique to a fixed set of data samples in all views. Meanwhile, the pair-wise similarities of missing data observations are also failed to utilize in latent representation learning sufficiently, leading to unsatisfactory clustering performance. To address these issues, we propose an incomplete multi-view deep clustering method with data imputation and alignment. Assuming that each data sample corresponds to a same latent representation among all views, it projects the latent representations into feature spaces with neural networks. As a result, not only the available data observations are reconstructed, but also the missing ones can be imputed accordingly. Moreover, a linear alignment measurement of linear complexity is defined to compute the pair-wise similarities of all data observations, especially including those of the missing. By executing the above two procedures iteratively, the discriminative latent representations can be learned and used to group the data into categories with off-the-shelf clustering algorithms. In experiment, the proposed method is validated on a set of benchmark datasets and achieves state-of-the-art performances.

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JBHI Journal 2024 Journal Article

Self-Supervised Medical Image Denoising Based on WISTA-Net for Human Healthcare in Metaverse

Huakun Huang
Chaoran Zhang
Lingjun Zhao
Shuxue Ding
Hanpin Wang
Huijun Wu

Medical image processing plays an important role in the interaction of real world and metaverse for healthcare. Self-supervised denoising based on sparse coding methods, without any prerequisite on large-scale training samples, has been attracting extensive attention for medical image processing. Whereas, existing self-supervised methods suffer from poor performance and low efficiency. In this paper, to achieve state-of-the-art denoising performance on the one hand, we present a self-supervised sparse coding method, named the weighted iterative shrinkage thresholding algorithm (WISTA). It does not rely on noisy-clean ground-truth image pairs to learn from only a single noisy image. On the other hand, to further improve denoising efficiency, we unfold the WISTA to construct a deep neural network (DNN) structured WISTA, named WISTA-Net. Specifically, in WISTA, motivated by the merit of the $l_{p}$ -norm, WISTA-Net has better denoising performance than the classical orthogonal matching pursuit (OMP) algorithm and the ISTA. Moreover, leveraging the high-efficiency of DNN structure in parameter updating, WISTA-Net outperforms the compared methods in denoising efficiency. In detail, for a 256 by 256 noisy image, the running time of WISTA-Net is 4. 72 s on the CPU, which is much faster than WISTA, OMP, and ISTA by 32. 88 s, 13. 06 s, and 6. 17 s, respectively.

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IJCAI Conference 2019 Conference Paper

Adversarial Examples for Graph Data: Deep Insights into Attack and Defense

Huijun Wu
Chen Wang
Yuriy Tyshetskiy
Andrew Docherty
Kai Lu
Liming Zhu

Graph deep learning models, such as graph convolutional networks (GCN) achieve state-of-the-art performance for tasks on graph data. However, similar to other deep learning models, graph deep learning models are susceptible to adversarial attacks. However, compared with non-graph data the discrete nature of the graph connections and features provide unique challenges and opportunities for adversarial attacks and defenses. In this paper, we propose techniques for both an adversarial attack and a defense against adversarial attacks. Firstly, we show that the problem of discrete graph connections and the discrete features of common datasets can be handled by using the integrated gradient technique that accurately determines the effect of changing selected features or edges while still benefiting from parallel computations. In addition, we show that an adversarially manipulated graph using a targeted attack statistically differs from un-manipulated graphs. Based on this observation, we propose a defense approach which can detect and recover a potential adversarial perturbation. Our experiments on a number of datasets show the effectiveness of the proposed techniques.

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