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Zeyang Zhang

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10 papers

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NeurIPS Conference 2025 Conference Paper

Out-of-Distribution Generalized Graph Anomaly Detection with Homophily-aware Environment Mixup

Sibo Tian
Xin Wang
Zeyang Zhang
Haibo Chen
Wenwu Zhu

Graph anomaly detection (GAD) is widely prevalent in scenarios such as financial fraud detection, anti-money laundering, and social bot detection. However, structural distribution shifts are commonly observed in real-world GAD data due to selection bias, resulting in reduced homophily. Existing GAD methods tend to rely on homophilic shortcuts when trained on high-homophily structures, limiting their ability to generalize well to data with low homophily under structural distribution shifts. In this study, we propose to handle structural distribution shifts by generating novel environments characterized by diverse homophilic structures and utilizing invariant patterns, i. e. , features and structures with the capability of stable prediction across structural distribution shifts, which face two challenges: (1) How to discover invariant patterns from entangled features and structures, as structures are sensitive to varying homophilic distributions. (2) How to systematically construct new environments with diverse homophilic structures. To address these challenges, we propose the Ego-Neighborhood Disentangled Encoder with Homophily-aware Environment Mixup (HEM), which effectively handles structural distribution shifts in GAD by discovering invariant patterns. Specifically, we first propose an ego-neighborhood disentangled encoder to decouple the learning of feature embeddings and structural embeddings, which facilitates subsequent improvements in the invariance of structural embeddings for prediction. Next, we introduce a homophily-aware environment mixup that dynamically adjusts edge weights through adversarial learning, effectively generating environments with diverse structural distributions. Finally, we iteratively train the classifier and environment mixup via adversarial training, simultaneously improving the diversity of constructed environments and discovering invariant patterns under structural distribution shifts. Extensive experiments on real-world datasets demonstrate that our method outperforms existing baselines and achieves state-of-the-art performance under structural distribution shift conditions.

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JBHI Journal 2025 Journal Article

PVCsNet: A Specialized Artificial Intelligence-Based Model to Classify Premature Ventricular Contractions From ECG Images

Biren Guo
Fei Gu
Ziheng Zhang
Zeyang Zhang
Shikun Sun

Premature ventricular complexes (PVCs) are irregularities in heart rhythm where the ventricles contract earlier than expected, disrupting the normal cardiac cycle. Identifying the origin of PVCs before surgery is crucial as it can reduce operation duration, lower radiation exposure, and potentially enhance ablation success rates. Current detection methods face limitations in accuracy and data processing, often requiring large datasets and complex interpretations. This study presents PVCsNet, a deep-learning network specifically designed for classifying premature ventricular complexes (PVCs) in ECG images. It incorporates residual structures and attention mechanisms to enhance classification performance. PVCsNet consists of four 3 × 3 convolutional layers as feature extractors, followed by residual connections and attention blocks. This design enables the network to map image features to class probability distributions, enhancing performance even with limited data. Our experimental results demonstrate that using the SE Block with MaxPool and a ratio of 4, PVCsNet achieves an overall accuracy of 94. 49%, with high precision in critical categories and a moderate parameter size. We successfully categorize the data into six distinct classes based on their origin locations in the heart: right ventricular outflow tract (RVOT), left ventricular outflow tract (LVOT), papillary muscle (PM), valvular annulus (VA), summit, and His-Purkinje system (HPS). Among these, RVOT is the most common and crucial origin of PVCs. PM and HPS are also significant origins due to their clinical implications. This study demonstrates the potential of PVCsNet in clinical diagnostics, providing promising results in classifying ECG images and contributing to future medical research and diagnosis.

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NeurIPS Conference 2024 Conference Paper

VERIFIED: A Video Corpus Moment Retrieval Benchmark for Fine-Grained Video Understanding

Houlun Chen
Xin Wang
Hong Chen
Zeyang Zhang
Wei Feng
Bin Huang
Jia Jia
Wenwu Zhu

Existing Video Corpus Moment Retrieval (VCMR) is limited to coarse-grained understanding that hinders precise video moment localization when given fine-grained queries. In this paper, we propose a more challenging fine-grained VCMR benchmark requiring methods to localize the best-matched moment from the corpus with other partially matched candidates. To improve the dataset construction efficiency and guarantee high-quality data annotations, we propose VERIFIED, an automatic \underline{V}id\underline{E}o-text annotation pipeline to generate captions with \underline{R}el\underline{I}able \underline{FI}n\underline{E}-grained statics and \underline{D}ynamics. Specifically, we resort to large language models (LLM) and large multimodal models (LMM) with our proposed Statics and Dynamics Enhanced Captioning modules to generate diverse fine-grained captions for each video. To filter out the inaccurate annotations caused by the LLM hallucination, we propose a Fine-Granularity Aware Noise Evaluator where we fine-tune a video foundation model with disturbed hard-negatives augmented contrastive and matching losses. With VERIFIED, we construct a more challenging fine-grained VCMR benchmark containing Charades-FIG, DiDeMo-FIG, and ActivityNet-FIG which demonstrate a high level of annotation quality. We evaluate several state-of-the-art VCMR models on the proposed dataset, revealing that there is still significant scope for fine-grained video understanding in VCMR.

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AAAI Conference 2023 Conference Paper

Dynamic Heterogeneous Graph Attention Neural Architecture Search

Zeyang Zhang
Ziwei Zhang
Xin Wang
Yijian Qin
Zhou Qin
Wenwu Zhu

Dynamic heterogeneous graph neural networks (DHGNNs) have been shown to be effective in handling the ubiquitous dynamic heterogeneous graphs. However, the existing DHGNNs are hand-designed, requiring extensive human efforts and failing to adapt to diverse dynamic heterogeneous graph scenarios. In this paper, we propose to automate the design of DHGNN, which faces two major challenges: 1) how to design the search space to jointly consider the spatial-temporal dependencies and heterogeneous interactions in graphs; 2) how to design an efficient search algorithm in the potentially large and complex search space. To tackle these challenges, we propose a novel Dynamic Heterogeneous Graph Attention Search (DHGAS) method. Our proposed method can automatically discover the optimal DHGNN architecture and adapt to various dynamic heterogeneous graph scenarios without human guidance. In particular, we first propose a unified dynamic heterogeneous graph attention (DHGA) framework, which enables each node to jointly attend its heterogeneous and dynamic neighbors. Based on the framework, we design a localization space to determine where the attention should be applied and a parameterization space to determine how the attention should be parameterized. Lastly, we design a multi-stage differentiable search algorithm to efficiently explore the search space. Extensive experiments on real-world dynamic heterogeneous graph datasets demonstrate that our proposed method significantly outperforms state-of-the-art baselines for tasks including link prediction, node classification and node regression. To the best of our knowledge, DHGAS is the first dynamic heterogeneous graph neural architecture search method.

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NeurIPS Conference 2023 Conference Paper

Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts

Zeyang Zhang
Xin Wang
Ziwei Zhang
Zhou Qin
Weigao Wen
Hui Xue'
Haoyang Li
Wenwu Zhu

Dynamic graph neural networks (DyGNNs) currently struggle with handling distribution shifts that are inherent in dynamic graphs. Existing work on DyGNNs with out-of-distribution settings only focuses on the time domain, failing to handle cases involving distribution shifts in the spectral domain. In this paper, we discover that there exist cases with distribution shifts unobservable in the time domain while observable in the spectral domain, and propose to study distribution shifts on dynamic graphs in the spectral domain for the first time. However, this investigation poses two key challenges: i) it is non-trivial to capture different graph patterns that are driven by various frequency components entangled in the spectral domain; and ii) it remains unclear how to handle distribution shifts with the discovered spectral patterns. To address these challenges, we propose Spectral Invariant Learning for Dynamic Graphs under Distribution Shifts (SILD), which can handle distribution shifts on dynamic graphs by capturing and utilizing invariant and variant spectral patterns. Specifically, we first design a DyGNN with Fourier transform to obtain the ego-graph trajectory spectrums, allowing the mixed dynamic graph patterns to be transformed into separate frequency components. We then develop a disentangled spectrum mask to filter graph dynamics from various frequency components and discover the invariant and variant spectral patterns. Finally, we propose invariant spectral filtering, which encourages the model to rely on invariant patterns for generalization under distribution shifts. Experimental results on synthetic and real-world dynamic graph datasets demonstrate the superiority of our method for both node classification and link prediction tasks under distribution shifts.

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NeurIPS Conference 2023 Conference Paper

Unsupervised Graph Neural Architecture Search with Disentangled Self-Supervision

Zeyang Zhang
Xin Wang
Ziwei Zhang
Guangyao Shen
Shiqi Shen
Wenwu Zhu

The existing graph neural architecture search (GNAS) methods heavily rely on supervised labels during the search process, failing to handle ubiquitous scenarios where supervisions are not available. In this paper, we study the problem of unsupervised graph neural architecture search, which remains unexplored in the literature. The key problem is to discover the latent graph factors that drive the formation of graph data as well as the underlying relations between the factors and the optimal neural architectures. Handling this problem is challenging given that the latent graph factors together with architectures are highly entangled due to the nature of the graph and the complexity of the neural architecture search process. To address the challenge, we propose a novel Disentangled Self-supervised Graph Neural Architecture Search (DSGAS) model, which is able to discover the optimal architectures capturing various latent graph factors in a self-supervised fashion based on unlabeled graph data. Specifically, we first design a disentangled graph super-network capable of incorporating multiple architectures with factor-wise disentanglement, which are optimized simultaneously. Then, we estimate the performance of architectures under different factors by our proposed self-supervised training with joint architecture-graph disentanglement. Finally, we propose a contrastive search with architecture augmentations to discover architectures with factor-specific expertise. Extensive experiments on 11 real-world datasets demonstrate that the proposed model is able to achieve state-of-the-art performance against several baseline methods in an unsupervised manner.

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NeurIPS Conference 2022 Conference Paper

Dynamic Graph Neural Networks Under Spatio-Temporal Distribution Shift

Zeyang Zhang
Xin Wang
Ziwei Zhang
Haoyang Li
Zhou Qin
Wenwu Zhu

Dynamic graph neural networks (DyGNNs) have demonstrated powerful predictive abilities by exploiting graph structural and temporal dynamics. However, the existing DyGNNs fail to handle distribution shifts, which naturally exist in dynamic graphs, mainly because the patterns exploited by DyGNNs may be variant with respect to labels under distribution shifts. In this paper, we propose to handle spatio-temporal distribution shifts in dynamic graphs by discovering and utilizing {\it invariant patterns}, i. e. , structures and features whose predictive abilities are stable across distribution shifts, which faces two key challenges: 1) How to discover the complex variant and invariant spatio-temporal patterns in dynamic graphs, which involve both time-varying graph structures and node features. 2) How to handle spatio-temporal distribution shifts with the discovered variant and invariant patterns. To tackle these challenges, we propose the Disentangled Intervention-based Dynamic graph Attention networks (DIDA). Our proposed method can effectively handle spatio-temporal distribution shifts in dynamic graphs by discovering and fully utilizing invariant spatio-temporal patterns. Specifically, we first propose a disentangled spatio-temporal attention network to capture the variant and invariant patterns. Then, we design a spatio-temporal intervention mechanism to create multiple interventional distributions by sampling and reassembling variant patterns across neighborhoods and time stamps to eliminate the spurious impacts of variant patterns. Lastly, we propose an invariance regularization term to minimize the variance of predictions in intervened distributions so that our model can make predictions based on invariant patterns with stable predictive abilities and therefore handle distribution shifts. Experiments on three real-world datasets and one synthetic dataset demonstrate the superiority of our method over state-of-the-art baselines under distribution shifts. Our work is the first study of spatio-temporal distribution shifts in dynamic graphs, to the best of our knowledge.

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AAAI Conference 2022 Conference Paper

Learning to Solve Travelling Salesman Problem with Hardness-Adaptive Curriculum

Zeyang Zhang
Ziwei Zhang
Xin Wang
Wenwu Zhu

Various neural network models have been proposed to tackle combinatorial optimization problems such as the travelling salesman problem (TSP). Existing learningbased TSP methods adopt a simple setting that the training and testing data are independent and identically distributed. However, the existing literature fails to solve TSP instances when training and testing data have different distributions. Concretely, we find that different training and testing distribution will result in more difficult TSP instances, i. e. , the solution obtained by the model has a large gap from the optimal solution. To tackle this problem, in this work, we study learning-based TSP methods when training and testing data have different distributions using adaptivehardness, i. e. , how difficult a TSP instance can be for a solver. This problem is challenging because it is nontrivial to (1) define hardness measurement quantitatively; (2) efficiently and continuously generate sufficiently hard TSP instances upon model training; (3) fully utilize instances with different levels of hardness to learn a more powerful TSP solver. To solve these challenges, we first propose a principled hardness measurement to quantify the hardness of TSP instances. Then, we propose a hardness-adaptive generator to generate instances with different hardness. We further propose a curriculum learner fully utilizing these instances to train the TSP solver. Experiments show that our hardness-adaptive generator can generate instances ten times harder than the existing methods, and our proposed method achieves significant improvement over state-of-the-art models in terms of the optimality gap.

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NeurIPS Conference 2022 Conference Paper

NAS-Bench-Graph: Benchmarking Graph Neural Architecture Search

Yijian Qin
Ziwei Zhang
Xin Wang
Zeyang Zhang
Wenwu Zhu

Graph neural architecture search (GraphNAS) has recently aroused considerable attention in both academia and industry. However, two key challenges seriously hinder the further research of GraphNAS. First, since there is no consensus for the experimental setting, the empirical results in different research papers are often not comparable and even not reproducible, leading to unfair comparisons. Secondly, GraphNAS often needs extensive computations, which makes it highly inefficient and inaccessible to researchers without access to large-scale computation. To solve these challenges, we propose NAS-Bench-Graph, a tailored benchmark that supports unified, reproducible, and efficient evaluations for GraphNAS. Specifically, we construct a unified, expressive yet compact search space, covering 26, 206 unique graph neural network (GNN) architectures and propose a principled evaluation protocol. To avoid unnecessary repetitive training, we have trained and evaluated all of these architectures on nine representative graph datasets, recording detailed metrics including train, validation, and test performance in each epoch, the latency, the number of parameters, etc. Based on our proposed benchmark, the performance of GNN architectures can be directly obtained by a look-up table without any further computation, which enables fair, fully reproducible, and efficient comparisons. To demonstrate its usage, we make in-depth analyses of our proposed NAS-Bench-Graph, revealing several interesting findings for GraphNAS. We also showcase how the benchmark can be easily compatible with GraphNAS open libraries such as AutoGL and NNI. To the best of our knowledge, our work is the first benchmark for graph neural architecture search.

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NeurIPS Conference 2021 Conference Paper

Graph Differentiable Architecture Search with Structure Learning

Yijian Qin
Xin Wang
Zeyang Zhang
Wenwu Zhu

Discovering ideal Graph Neural Networks (GNNs) architectures for different tasks is labor intensive and time consuming. To save human efforts, Neural Architecture Search (NAS) recently has been used to automatically discover adequate GNN architectures for certain tasks in order to achieve competitive or even better performance compared with manually designed architectures. However, existing works utilizing NAS to search GNN structures fail to answer the question: how NAS is able to select the desired GNN architectures? In this paper, we investigate this question to solve the problem, for the first time. We conduct a measurement study with experiments to discover that gradient based NAS methods tend to select proper architectures based on the usefulness of different types of information with respect to the target task. Our explorations further show that gradient based NAS also suffers from noises hidden in the graph, resulting in searching suboptimal GNN architectures. Based on our findings, we propose a Graph differentiable Architecture Search model with Structure Optimization (GASSO), which allows differentiable search of the architecture with gradient descent and is able to discover graph neural architectures with better performance through employing graph structure learning as a denoising process in the search procedure. The proposed GASSO model is capable of simultaneously searching the optimal architecture and adaptively adjusting graph structure by jointly optimizing graph architecture search and graph structure denoising. Extensive experiments on real-world graph datasets demonstrate that our proposed GASSO model is able to achieve state-of-the-art performance compared with existing baselines.

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