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Mingyang Zhou

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7 papers

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EAAI Journal 2026 Journal Article

Leveraging community context and frequency-adaptive aggregation for robust fraud detection

Zheng Zhang
Jun Wan
Jun Liu
Mingyang Zhou
Kezhong Lu
Claudio J. Tessone
Guoliang Chen
Hao Liao

As the main threat to the healthy development of major internet platforms, fraud is increasingly carried out in organized, group-based forms. Such collusive fraud activities are easier to obtain illegal benefits at a lower exposure risk. Recently, graph neural network-based fraud detection methods have attracted increasing attention due to their ability to solve camouflage problems in fraud scenarios. However fraudsters’ evolving camouflage strategies pose great challenges to the design of graph neural network (GNN)-based detection models. Furthermore, most existing GNN-based approaches focus on the representation learning of node-level and structural-level features, and often ignores the contextual high-order information of the fraud group where the fraud node is located. To address these limitations, this paper proposes a community context-driven and frequency-adaptive graph neural network (CCFA-GNN) for detecting collaborative camouflage review fraudsters. Specifically, a collusive reviewer graph is constructed to capture the deep collaborative relationship among fraudsters. Then we incorporate the high-order representation of collusive fraud into graph embedding learning for community context based on the maximization of the co-occurrence probability of fraudsters. Finally, a frequency-adaptive feature aggregation module is adopted to simultaneously leverage the high-frequency and low-frequency information of features to enhance the node embedding representation. Extensive experiments on real-world fraud datasets have been conducted to verify the effectiveness, robustness, and interpretability of the proposed model, rendering it highly suitable for fraud detection applications in e-commerce and financial transaction scenarios.

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NeurIPS Conference 2025 Conference Paper

Adaptive Preference Arithmetic: A Personalized Agent with Adaptive Preference Arithmetic for Dynamic Preference Modeling

Hongyi Nie
Yaqing Wang
Mingyang Zhou
Feiyang Pan
Quanming Yao
Zhen Wang

As large language models (LLMs) are increasingly used as personalized user assistants, effectively adapting to users' evolving preferences is critical for delivering high-quality personalized responses. While user preferences are often stable in content, their relative strengths shift over time due to changing goals and contexts. Therefore, modeling these dynamic preference strengths can enable finer-grained personalization. However, current methods face two major challenges: (i) limited user feedback makes it difficult to estimate preference strengths accurately, and (ii) natural language ambiguity limits the controllability of preference-guided generation. To address these issues, we propose AdaPA-Agent, a LLM-agent personalization framework that models dynamic preference strengths via Adaptive Preference Arithmetic. First, instead of requiring additional user feedback, AdaPA-Agent employs an alignment-based strength estimation module to estimate the strength of user preferences from the existing user-agent interaction. Then, it guides controllable personalized generation by linearly combining next-token distributions, weighted by the estimated strengths of individual preferences. Experiments on two personalization tasks-conversational recommendation and personalized web interaction-demonstrate that AdaPA-Agent better aligning with users' changing intents, and has achieved over 18. 9\% and 14. 2\% improvements compared to ReAct, the widely-used agent framework.

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TIST Journal 2025 Journal Article

Aspect-Enhanced Explainable Recommendation with Multi-modal Contrastive Learning

Hao Liao
Shuo Wang
Hao Cheng
Wei Zhang
Jiwei Zhang
Mingyang Zhou
Kezhong Lu
Rui Mao

Explainable recommender systems ( ERS ) aim to enhance users’ trust in the systems by offering personalized recommendations with transparent explanations. This transparency provides users with a clear understanding of the rationale behind the recommendations, fostering a sense of confidence and reliability in the system’s outputs. Generally, the explanations are presented in a familiar and intuitive way, which is in the form of natural language, thus enhancing their accessibility to users. Recently, there has been an increasing focus on leveraging reviews as a valuable source of rich information in both modeling user-item preferences and generating textual interpretations, which can be performed simultaneously in a multi-task framework. Despite the progress made in these review-based recommendation systems, the integration of implicit feedback derived from user-item interactions and user-written text reviews has yet to be fully explored. To fill this gap, we propose a model named SERMON (A s pect-enhanced E xplainable R ecommendation with M ulti-modal C o ntrast Lear n ing). Our model explores the application of multimodal contrastive learning to facilitate reciprocal learning across two modalities, thereby enhancing the modeling of user preferences. Moreover, our model incorporates the aspect information extracted from the review, which provides two significant enhancements to our tasks. Firstly, the quality of the generated explanations is improved by incorporating the aspect characteristics into the explanations generated by a pre-trained model with controlled textual generation ability. Secondly, the commonly used user-item interactions are transformed into user-item-aspect interactions, which we refer to as interaction triple, resulting in a more nuanced representation of user preference. To validate the effectiveness of our model, we conduct extensive experiments on three real-world datasets. The experimental results show that our model outperforms state-of-the-art baselines, with a 2.0% improvement in prediction accuracy and a substantial 24.5% enhancement in explanation quality for the TripAdvisor dataset.

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IJCAI Conference 2025 Conference Paper

Denoising Diffusion Models are Good General Gaze Feature Learners

Guanzhong Zeng
Jingjing Wang
Pengwei Yin
Zefu Xu
Mingyang Zhou

Since the collection of labeled gaze data is laborious and time-consuming, methods which can learn generalizable features by leveraging large-scale available unlabeled data are desirable. In recent years, we have witnessed the tremendous capabilities of diffusion models in generating images as well as their potential in feature representation learning. In this paper, we investigate whether they can acquire discriminative representations for gaze estimation via generative pre-training. To achieve this goal, we propose a self-supervised learning framework with diffusion models for gaze estimation, called GazeDiff. Specifically, we utilize a conditional diffusion model to generate target image with gaze direction specified by the reference image as the pre-training task. To facilitate the diffusion model to learn gaze related features as condition, we propose a disentangling feature learning strategy, which first learns appearance feature, head pose feature, and eye direction feature respectively, and then combines them as the conditional features. Extensive experiments demonstrate denoising diffusion models are also good general gaze feature learners.

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NeurIPS Conference 2025 Conference Paper

IPSI: Enhancing Structural Inference with Automatically Learned Structural Priors

Zhongben Gong
Xiaoqun Wu
Mingyang Zhou

We propose IPSI, a general iterative framework for structural inference in interacting dynamical systems. It integrates a pretrained structural estimator and a joint inference module based on the Variational Autoencoder (VAE); these components are alternately updated to progressively refine the inferred structures. Initially, the structural estimator is trained on labels from either a meta-dataset or a baseline model to extract features and generate structural priors, which provide multi-level guidance for training the joint inference module. In subsequent iterations, pseudolabels from the joint module replace the initial labels. IPSI is compatible with various VAE-based models. Experiments on synthetic datasets of physical systems demonstrate that IPSI significantly enhances the performance of structural inference models such as Neural Relational Inference (NRI). Ablation studies reveal that feature and structural prior inputs to the joint module offer complementary improvements from representational and generative perspectives.

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IJCAI Conference 2024 Conference Paper

Modeling Personalized Retweeting Behaviors for Multi-Stage Cascade Popularity Prediction

Mingyang Zhou
Yanjie Lin
Gang Liu
Zuwen Li
Hao Liao
Rui Mao

Predicting the size of message cascades is critical in various applications, such as online advertising and early detection of rumors. However, most existing deep learning approaches rely on cascade observation, which hinders accurate cascade prediction before message posting. Besides, these approaches overlook personalized retweeting behaviors that reflect users' inclination to retweeting specific types of information. In this study, we propose a universal cascade prediction framework, namely Cascade prediction regarding Multiple Stage (CasMS), that effectively predicts cascade popularity across message generation stage as well as short-term and long-term stages. Unlike previous methods, our approach not only captures users' personalized retweeting behaviors but also incorporates temporal cascade features. We perform the experiments in datasets collected ourselves as well as public datasets. The results show that our method significantly surpasses existing approaches in predicting the cascade during the message generation stage and different time periods in the cascade dynamics.

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NeurIPS Conference 2024 Conference Paper

Motif-oriented influence maximization for viral marketing in large-scale social networks

Mingyang Zhou
Weiji Cao
Hao Liao
Rui Mao

The influence maximization (IM) problem aims to identify a budgeted set of nodes with the highest potential to influence the largest number of users in a cascade model, a key challenge in viral marketing. Traditional \emph{IM} approaches consider each user/node independently as a potential target customer. However, in many scenarios, the target customers comprise motifs, where activating only one or a few users within a motif is insufficient for effective viral marketing, which, nevertheless, receives little attention. For instance, if a motif of three friends planning to dine together, targeting all three simultaneously is crucial for a restaurant advertisement to succeed. In this paper, we address the motif-oriented influence maximization problem under the linear threshold model. We prove that the motif-oriented IM problem is NP-hard and that the influence function is neither supermodular nor submodular, in contrast to the classical \emph{IM} setting. To simplify the problem, we establish the submodular upper and lower bounds for the influence function. By leveraging the submodular property, we propose a natural greedy strategy that simultaneously maximizes both bounds. Our algorithm has an approximation ratio of $\tau\cdot (1-1/e-\varepsilon)$ and a near-linear time complexity of $O((k+l)(m+\eta)\log \eta/\varepsilon^2)$. Experimental results on diverse datasets confirm the effectiveness of our approach in motif maximization.

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