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Dawei Zhou

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11 papers

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NeurIPS Conference 2025 Conference Paper

HeroFilter: Adaptive Spectral Graph Filter for Varying Heterophilic Relations

Shuaicheng Zhang
Haohui Wang
Junhong Lin
Xiaojie Guo
Yada Zhu
Si Zhang
Dongqi Fu
Dawei Zhou

Graph heterophily, where connected nodes have different labels, has attracted significant interest recently. Most existing works adopt a simplified approach - using low-pass filters for homophilic graphs and high-pass filters for heterophilic graphs. However, we discover that the relationship between graph heterophily and spectral filters is more complex - the optimal filter response varies across frequency components and does not follow a strict monotonic correlation with heterophily degree. This finding challenges conventional fixed filter designs and suggests the need for adaptive filtering to preserve expressiveness in graph embeddings. Formally, natural questions arise: Given a heterophilic graph $\mathcal{G}$, how and to what extent will the varying heterophily degree of $\mathcal{G}$ affect the performance of GNNs? How can we design adaptive filters to fit those varying heterophilic connections? Our theoretical analysis reveals that the average frequency response of GNNs and graph heterophily degree do not follow a strict monotonic correlation, necessitating adaptive graph filters to guarantee good generalization performance. Hence, we propose HeroFilter, a simple yet powerful GNN, which extracts information across the heterophily spectrum and combines salient representations through adaptive mixing. HeroFilter's superior performance achieves up to 9. 2% accuracy improvement over leading baselines across homophilic and heterophilic graphs.

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AAAI Conference 2025 Conference Paper

Mitigating Feature Gap for Adversarial Robustness by Feature Disentanglement

Nuoyan Zhou
Dawei Zhou
Decheng Liu
Nannan Wang
Xinbo Gao

Adversarial fine-tuning methods enhance adversarial robustness via fine-tuning the pre-trained model in an adversarial training manner. However, we identify that some specific latent features of adversarial samples are confused by adversarial perturbation and lead to an unexpectedly increasing gap between features in the last hidden layer of natural and adversarial samples. To address this issue, we propose a disentanglement-based approach to explicitly model and further remove the specific latent features. We introduce a feature disentangler to separate out the specific latent features from the features of the adversarial samples, thereby boosting robustness by eliminating the specific latent features. Besides, we align clean features in the pre-trained model with features of adversarial samples in the fine-tuned model, to benefit from the intrinsic features of natural samples. Empirical evaluations on three benchmark datasets demonstrate that our approach surpasses existing adversarial fine-tuning methods and adversarial training baselines.

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AAAI Conference 2025 Conference Paper

Motion Artifact Removal in Pixel-Frequency Domain via Alternate Masks and Diffusion Model

Jiahua Xu
Dawei Zhou
Lei Hu
Jianfeng Guo
Feng Yang
Zaiyi Liu
Nannan Wang
Xinbo Gao

Motion artifacts present in magnetic resonance imaging (MRI) can seriously interfere with clinical diagnosis. Removing motion artifacts is a straightforward solution and has been extensively studied. However, paired data are still heavily relied on in recent works and the perturbations in k-space (frequency domain) are not well considered, which limits their applications in the clinical field. To address these issues, we propose a novel unsupervised purification method which leverages pixel-frequency information of noisy MRI images to guide a pre-trained diffusion model to recover clean MRI images. Specifically, considering that motion artifacts are mainly concentrated in high-frequency components in k-space, we utilize the low-frequency components as the guide to ensure correct tissue textures. Additionally, given that high-frequency and pixel information are helpful for recovering shape and detail textures, we design alternate complementary masks to simultaneously destroy the artifact structure and exploit useful information. Quantitative experiments are performed on datasets from different tissues and show that our method achieves superior performance on several metrics. Qualitative evaluations with radiologists also show that our method provides better clinical feedback.

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IJCAI Conference 2024 Conference Paper

3D-FuM: Benchmarking 3D Molecule Learning with Functional Groups

Tingwei Chen
Jianpeng Chen
Dawei Zhou

Molecular graph representation learning plays a crucial role in various domains, such as drug discovery and chemical reaction prediction, where molecular graphs are typically depicted as 2D topological structures. However, recent insights highlight the critical role of 3D geometric information and functional groups in accurately predicting molecular properties, aspects often neglected in existing molecular graph benchmark datasets. To bridge the research gap, we introduce a comprehensive molecular learning benchmark named 3D-FUM, which incorporates both 3D geometric information and functional groups of a large number of molecules. 3D-FUM integrates 18 state-of-the-art algorithms and 19 evaluation metrics on three molecular learning tasks, including general molecule generation, conditional molecule generation, and property predictions. 3D-FUM, for the first time, take into consideration both 3D geometric information and molecular functional groups, which enables researchers and practitioners to effectively and impartially evaluate newly proposed methods in comparison to existing baselines across diverse datasets. Furthermore, we design a user interface for user-friendly interaction and development with the benchmark for evaluation metrics selection, parameter adjustment, and leaderboard comparison. To ensure accessibility and reproducibility, we opensource our benchmark 3D-FUM and experimental results at https: //3dfunctiongroupmoleculedataset. github. io/3D-FuM/#/Home.

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AAAI Conference 2024 Conference Paper

Combating Insider Threat in the Open-World Environments: Identification, Monitoring, and Data Augmentation

Dawei Zhou

Recent years have witnessed a dramatic increase in a class of security threats known as "insider threats". These threats occur when individuals with authorized access to an organization's network engage in harmful activities, potentially leading to the disclosure of vital information or adversely affecting the organization's systems (e.g., financial loss, system crashes, and national security challenges). Distinct from other types of terror attacks, combating insider threats exhibits several unique challenges, including (1) rarity, (2) non-separability, (3) label scarcity, (4) dynamics, and (5) heterogeneity, making themselves extremely difficult to identify and mitigate. We target the challenging problem of combating insider threats in open-world environments by leveraging a variety of data sources (e.g., internal system logs, employee networks, human trafficking, and smuggling networks). To effectively combat these intricate threats, we introduce an interactive learning mechanism that is composed of three mutually beneficial learning modules: insider identification, insider monitoring, and data augmentation. Each module plays a crucial role in enhancing our ability to detect and mitigate insider threats, thereby contributing to a more secure and resilient organizational environment.

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JBHI Journal 2024 Journal Article

Protecting Prostate Cancer Classification From Rectal Artifacts via Targeted Adversarial Training

Lei Hu
Dawei Zhou
Jiahua Xu
Cheng Lu
Chu Han
Zhenwei Shi
Qikui Zhu
Xinbo Gao

Magnetic resonance imaging (MRI)-based deep neural networks (DNN) have been widely developed to perform prostate cancer (PCa) classification. However, in real-world clinical situations, prostate MRIs can be easily impacted by rectal artifacts, which have been found to lead to incorrect PCa classification. Existing DNN-based methods typically do not consider the interference of rectal artifacts on PCa classification, and do not design specific strategy to address this problem. In this study, we proposed a novel Targeted adversarial training with Proprietary Adversarial Samples (TPAS) strategy to defend the PCa classification model against the influence of rectal artifacts. Specifically, based on clinical prior knowledge, we generated proprietary adversarial samples with rectal artifact-pattern adversarial noise, which can severely mislead PCa classification models optimized by the ordinary training strategy. We then jointly exploited the generated proprietary adversarial samples and original samples to train the models. To demonstrate the effectiveness of our strategy, we conducted analytical experiments on multiple PCa classification models. Compared with ordinary training strategy, TPAS can effectively improve the single- and multi-parametric PCa classification at patient, slice and lesion level, and bring substantial gains to recent advanced models. In conclusion, TPAS strategy can be identified as a valuable way to mitigate the influence of rectal artifacts on deep learning models for PCa classification.

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NeurIPS Conference 2024 Conference Paper

Towards Heterogeneous Long-tailed Learning: Benchmarking, Metrics, and Toolbox

Haohui Wang
Weijie Guan
Jianpeng Chen
Zi Wang
Dawei Zhou

Long-tailed data distributions pose challenges for a variety of domains like e-commerce, finance, biomedical science, and cyber security, where the performance of machine learning models is often dominated by head categories while tail categories are inadequately learned. This work aims to provide a systematic view of long-tailed learning with regard to three pivotal angles: (A1) the characterization of data long-tailedness, (A2) the data complexity of various domains, and (A3) the heterogeneity of emerging tasks. We develop HeroLT, a comprehensive long-tailed learning benchmark integrating 18 state-of-the-art algorithms, 10 evaluation metrics, and 17 real-world datasets across 6 tasks and 4 data modalities. HeroLT with novel angles and extensive experiments (315 in total) enables effective and fair evaluation of newly proposed methods compared with existing baselines on varying dataset types. Finally, we conclude by highlighting the significant applications of long-tailed learning and identifying several promising future directions. For accessibility and reproducibility, we open-source our benchmark HeroLT and corresponding results at https: //github. com/SSSKJ/HeroLT.

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TMLR Journal 2022 Journal Article

Towards Accurate Subgraph Similarity Computation via Neural Graph Pruning

Linfeng Liu
Xu Han
Dawei Zhou
Liping Liu

Subgraph similarity search, one of the core problems in graph search, concerns whether a target graph approximately contains a query graph. The problem is recently touched by neural methods. However, current neural methods do not consider pruning the target graph, though pruning is critically important in traditional calculations of subgraph similarities. One obstacle to applying pruning in neural methods is the discrete property of pruning. In this work, we convert graph pruning to a problem of node relabeling and then relax it to a differentiable problem. Based on this idea, we further design a novel neural network to approximate a type of subgraph distance: the subgraph edit distance (SED). In particular, we construct the pruning component using a neural structure, and the entire model can be optimized end-to-end. In the design of the model, we propose an attention mechanism to leverage the information about the query graph and guide the pruning of the target graph. Moreover, we develop a multi-head pruning strategy such that the model can better explore multiple ways of pruning the target graph. The proposed model establishes new state-ofthe-art results across seven benchmark datasets. Extensive analysis of the model indicates that the proposed model can reasonably prune the target graph for SED computation.

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AAAI Conference 2020 Conference Paper

Towards Fine-Grained Temporal Network Representation via Time-Reinforced Random Walk

Zhining Liu
Dawei Zhou
Yada Zhu
Jinjie Gu
Jingrui He

Encoding a large-scale network into a low-dimensional space is a fundamental step for various network analytic problems, such as node classiﬁcation, link prediction, community detection, etc. Existing methods focus on learning the network representation from either the static graphs or timeaggregated graphs (e. g. , time-evolving graphs). However, many real systems are not static or time-aggregated as the nodes and edges are timestamped and dynamically changing over time. For examples, in anti-money laundering analysis, cycles formed with time-ordered transactions might be red ﬂags in online transaction networks; in novelty detection, a star-shaped structure appearing in a short burst might be an underlying hot topic in social networks. Existing embedding models might not be able to well preserve such ﬁnegrained network dynamics due to the incapability of dealing with continuous-time and the negligence of ﬁne-grained interactions. To bridge this gap, in this paper, we propose a ﬁne-grained temporal network embedding framework named FiGTNE, which aims to learn a comprehensive network representation that preserves the rich and complex network context in the temporal network. In particular, we start from the notion of ﬁne-grained temporal networks, where the temporal network can be represented as a series of timestamped nodes and edges. Then, we propose the time-reinforced random walk (TRRW) with a bi-level context sampling strategy to explore the essential structures and temporal contexts in temporal networks. Extensive experimental results on real graphs demonstrate the efﬁcacy of our FiGTNE framework.

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IJCAI Conference 2015 Conference Paper

MUVIR: Multi-View Rare Category Detection

Dawei Zhou
Jingrui He
K. Seluk Candan
Hasan Davulcu

Rare category detection refers to the problem of identifying the initial examples from underrepresented minority classes in an imbalanced data set. This problem becomes more challenging in many real applications where the data comes from multiple views, and some views may be irrelevant for distinguishing between majority and minority classes, such as synthetic ID detection and insider threat detection. Existing techniques for rare category detection are not best suited for such applications, as they mainly focus on data with a single view. To address the problem of multi-view rare category detection, in this paper, we propose a novel framework named MUVIR. It builds upon existing techniques for rare category detection with each single view, and exploits the relationship among multiple views to estimate the overall probability of each example belonging to the minority class. In particular, we study multiple special cases of the framework with respect to their working conditions, and analyze the performance of MUVIR in the presence of irrelevant views. For problems where the exact priors of the minority classes are unknown, we generalize the MUVIR algorithm to work with only an upper bound on the priors. Experimental results on both synthetic and real data sets demonstrate the effectiveness of the proposed framework, especially in the presence of irrelevant views.

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AAAI Conference 2015 Conference Paper

Tackling Mental Health by Integrating Unobtrusive Multimodal Sensing

Dawei Zhou
Jiebo Luo
Vincent Silenzio
Yun Zhou
Jile Hu
Glenn Currier
Henry Kautz

Mental illness is becoming a major plague in modern societies and poses challenges to the capacity of current public health systems worldwide. With the widespread adoption of social media and mobile devices, and rapid advances in artificial intelligence, a unique opportunity arises for tackling mental health problems. In this study, we investigate how users’ online social activities and physiological signals detected through ubiquitous sensors can be utilized in realistic scenarios for monitoring their mental health states. First, we extract a suite of multimodal time-series signals using modern computer vision and signal processing techniques, from recruited participants while they are immersed in online social media that elicit emotions and emotion transitions. Next, we use machine learning techniques to build a model that establishes the connection between mental states and the extracted multimodal signals. Finally, we validate the effectiveness of our approach using two groups of recruited subjects.

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