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Yuan Rao

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7 papers

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AAAI Conference 2026 Conference Paper

Game Theory Based Community-Aware Opinion Dynamics

Shanfan Zhang
Yongyi Lin
Xiaoting Shen
Zhan Bu
Yuan Rao

Understanding opinion evolution in complex social networks is crucial for modeling social influence and predicting collective behavior. Yet, most models overlook how community structures shape opinion updates, often assuming homogeneous influence. This abstraction neglects individuals’ stronger responsiveness to intra-community peers—an empirically observed driver of localized consensus and inter-group polarization. We propose GCAOFP, a co-evolutionary framework that jointly models opinion dynamics and community formation as an integrated process. In GCAOFP, agents strategically alternate between two coupled modules: (1) a Community Dynamics Module, where agents play a non-cooperative game to optimize community memberships based on opinion alignment and structural cohesion; and (2) an Opinion Adjustment Module, where agents revise opinions via a bounded-confidence mechanism modulated by community-induced influence weights. This dual-stage process captures the feedback loop between structure and opinion. We prove that GCAOFP converges to stable equilibria, ensuring intra-community consensus and inter-community diversity—dynamics that standard models fail to replicate. Experiments on real-world networks show that GCAOFP better reproduces localized opinion clusters, while offering strong scalability and interpretability, illuminating the strategic foundations of polarization.

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EAAI Journal 2025 Journal Article

Practical framework for generative on-branch soybean pod detection in occlusion and class imbalance scenes

Kanglei Wu
Tan Wang
Yuan Rao
Xiu Jin
Xiaobo Wang
Jiajia Li
Zhe Zhang
Zhaohui Jiang

The number of pods per plant can serve as an effective indicator of soybean yield, and accurately determining this is essential for evaluating high-quality soybean varieties. However, traditional manual pod counting is time-consuming and laborious. Although deep learning-based pod detection methods have attracted much attention, there are still considerable challenges for the effective detection of pods in occlusion and class imbalance scenes. As a remedy, this study proposes a framework that leverages synthetic pod image generation and multi-stage transfer learning to generate detection model of on-branch soybean pods in complex scenes. This framework employs a novel pipeline: initially separating individual pods from non-occluded pod images in an off-branch pod training set, then using these to generate synthetic datasets with diverse pod features. Next, a multi-stage transfer learning method is employed to train an on-branch pod detection model, leveraging both real and synthetic datasets to enhance pod feature extraction in complex scenes. The detection model of proposed framework, YOLOv7-tiny (tiny version of You Only Look Once v7), integrates an angle prediction module based on Circular Smooth Label for rotated object detection, Coordinate Attention modules for enhanced feature extraction and Minimum Point Distance Intersection over Union Loss for precise bounding box perception. Experimental results show that proposed framework achieves an 81. 1% mAP (mean Average Precision) for detecting on-branch pods in complex scenes, surpassing the best-performing model by 23. 7%. This proposed method presents an effective solution for complex on-branch pod detection, having great potential of serving as robust pipeline for similar agricultural tasks.

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NeurIPS Conference 2025 Conference Paper

VADTree: Explainable Training-Free Video Anomaly Detection via Hierarchical Granularity-Aware Tree

Wenlong Li
Yifei Xu
Yuan Rao
Zhenhua Wang
Shuiguang Deng

Video anomaly detection (VAD) focuses on identifying anomalies in videos. Su- pervised methods demand substantial in-domain training data and fail to deliver clear explanations for anomalies. In contrast, training-free methods leverage the knowledge reserves and language interactivity of large pre-trained models to detect anomalies. However, the current fixed-length temporal window sam- pling approaches struggle to accurately capture anomalies with varying temporal spans. Therefore, we propose VADTree that utilizes a Hierarchical Granularity- aware Tree (HGTree) structure for flexible sampling in VAD. VADTree leverages the knowledge embedded in a pre-trained Generic Event Boundary Detection (GEBD) model to characterize potential anomaly event boundaries. Specifically, VADTree decomposes the video into generic event nodes based on boundary confidence, and performs adaptive coarse-fine hierarchical structuring and re- dundancy removal to construct the HGTree. Then, the multi-dimensional priors are injected into the visual language models (VLMs) to enhance the node-wise anomaly perception, and anomaly reasoning for generic event nodes is achieved via large language models (LLMs). Finally, an inter-cluster node correlation method is used to integrate the multi-granularity anomaly scores. Extensive experiments on three challenging datasets demonstrate that VADTree achieves state-of-the-art performance in training-free settings while drastically reducing the number of sampled video segments. The code will be available at https: //github. com/wenlongli10/VADTree.

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AAAI Conference 2023 Conference Paper

HG-SL: Jointly Learning of Global and Local User Spreading Behavior for Fake News Early Detection

Ling Sun
Yuan Rao
Yuqian Lan
Bingcan Xia
Yangyang Li

Recently, fake news forgery technology has become more and more sophisticated, and even the profiles of participants may be faked, which challenges the robustness and effectiveness of traditional detection methods involving text or user identity. Most propagation-only approaches mainly rely on neural networks to learn the diffusion pattern of individual news, which is insufficient to describe the differences in news spread ability, and also ignores the valuable global connections of news and users, limiting the performance of detection. Therefore, we propose a joint learning model named HG-SL, which is blind to news content and user identities, but capable of catching the differences between true and fake news in the early stages of propagation through global and local user spreading behavior. Specifically, we innovatively design a Hypergraph-based Global interaction learning module to capture the global preferences of users from their co-spreading relationships, and introduce node centrality encoding to complement user influence in hypergraph learning. Moreover, the designed Self-attention-based Local context learning module first introduce spread status to highlight the propagation ability of news and users, thus providing additional signals for verifying news authenticity. Experiments on real-world datasets indicate that our HG-SL, which solely relies on user behavior, outperforms SOTA baselines utilizing multidimensional features in both fake news detection and early detection task.

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AAAI Conference 2022 Conference Paper

MS-HGAT: Memory-Enhanced Sequential Hypergraph Attention Network for Information Diffusion Prediction

Ling Sun
Yuan Rao
Xiangbo Zhang
Yuqian Lan
Shuanghe Yu

Predicting the diffusion cascades is a critical task to understand information spread on social networks. Previous methods usually focus on the order or structure of the infected users in a single cascade, thus ignoring the global dependencies of users and cascades, limiting the performance of prediction. Current strategies to introduce social networks only learn the social homogeneity among users, which is not enough to describe their interaction preferences, let alone the dynamic changes. To address the above issues, we propose a novel information diffusion prediction model named Memory-enhanced Sequential Hypergraph Attention Networks (MS-HGAT). Specifically, to introduce the global dependencies of users, we not only take advantages of their friendships, but also consider their interactions at the cascade level. Furthermore, to dynamically capture users’ preferences, we divide the diffusion hypergraph into several sub graphs based on timestamps, develop Hypergraph Attention Networks to learn the sequential hypergraphs, and connect them with gated fusion strategy. In addition, a memoryenhanced embedding lookup module is proposed to capture the learned user representations into the cascade-specific embedding space, thus highlighting the feature interaction within the cascade. The experimental results over four realistic datasets demonstrate that MS-HGAT significantly outperforms the state-of-the-art diffusion prediction models in both Hits@K and MAP@k metrics.

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AAAI Conference 2021 Conference Paper

Evidence Inference Networks for Interpretable Claim Verification

Lianwei Wu
Yuan Rao
Ling Sun
Wangbo He

Existing approaches construct appropriate interaction models to explore semantic conflicts between claims and relevant articles, which provides practical solutions for interpretable claim verification. However, these conflicts are not necessarily all about questioning the false part of claims, which makes considerable semantic conflicts difficult to be used as evidence to explain the results of claim verification, especially those that cannot identify the core semantics of claims. In this paper, we propose evidence inference networks (EVIN), which focus on the conflicts questioning the core semantics of claims and serve as evidence for interpretable claim verification. Specifically, EVIN first captures the core semantic segments of claims and the users’ principal opinions in relevant articles. Then, it finely-grained identifies the semantic conflicts contained in each relevant article from these opinions. Finally, EVIN constructs coherence modeling to match the conflicts that queries the core semantic fragments of claims as explainable evidence. Experiments on two widely used datasets demonstrate that EVIN not only achieves satisfactory performance but also provides explainable evidence for end-users.

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IJCAI Conference 2020 Conference Paper

Evidence-Aware Hierarchical Interactive Attention Networks for Explainable Claim Verification

Lianwei Wu
Yuan Rao
Xiong Yang
Wanzhen Wang
Ambreen Nazir

Exploring evidence from relevant articles to confirm the veracity of claims is a trend towards explainable claim verification. However, most strategies capture the top-k check-worthy articles or salient words as evidence, but this evidence is difficult to focus on the questionable parts of unverified claims. Besides, they utilize relevant articles indiscriminately, ignoring the source credibility of these articles, which may cause quiet a few unreliable articles to interfere with the assessment results. In this paper, we propose Evidence-aware Hierarchical Interactive Attention Networks (EHIAN) by considering the capture of evidence fragments and the fusion of source credibility to explore more credible evidence semantics discussing the questionable parts of claims for explainable claim verification. EHIAN first designs internal interaction layer (IIL) to strengthen deep interaction and matching between claims and relevant articles for obtaining key evidence fragments, and then proposes global inference layer (GIL) that fuses source features of articles and interacts globally with the average semantics of all articles and finally earns the more credible evidence semantics discussing the questionable parts of claims. Experiments on two datasets demonstrate that EHIAN not only achieves the state-of-the-art performance but also secures effective evidence to explain the results.

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