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Chaoqun Wang

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9 papers

2 author rows

AAAI Conference 2026 Conference Paper

Multimodal Graph Representation Learning with Dynamic Information Pathways

Xiaobin Hong
Mingkai Lin
Xiaoli Wang
Chaoqun Wang
Wenzhong Li

Multimodal graphs, where nodes contain heterogeneous features such as images and text, are increasingly common in real-world applications. Effectively learning on such graphs requires both adaptive intra-modal message passing and efficient inter-modal aggregation. However, most existing approaches to multimodal graph learning are typically extended from conventional graph neural networks and rely on static structures or dense attention, which limit flexibility and expressive node embedding learning. In this paper, we propose a novel multimodal graph representation learning framework with Dynamic information Pathways (DiP). By introducing modality-specific pseudo nodes, DiP enables dynamic message routing within each modality via proximity-guided pseudo-node interactions and captures inter-modality dependence through efficient information pathways in a shared state space. This design achieves adaptive, expressive, and sparse message propagation across modalities with linear complexity. We conduct the link prediction and node classification tasks to evaluate performance and carry out full experimental analyses. Extensive experiments across multiple benchmarks demonstrate that DiP consistently outperforms baselines.

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AAAI Conference 2026 Conference Paper

S²Flow: Towards Fast and Authentic Training-Free High-Resolution Video Generation

Chaoqun Wang
Shaobo Min
Xu Yang

Rectified flow models have shown strong potential in high-fidelity video generation, yet extending them to high-resolution remains challenging due to the high cost of full attention and error accumulation in the ODE-solving process. In this paper, we propose S^2Flow, a training-free framework that enables efficient and authentic high-resolution video generation by jointly exploring Flow-guided Sparse attention and Second-order ODE solution. Specifically, S^2Flow exploits and transfers the semantic and structural information from the low-resolution flow trajectory to guide the high-resolution flow in two aspects. First, S^2Flow dynamically captures the sparse patterns of the spatio-temporal attention maps from low-resolution videos to construct localized 3D windows, enabling efficient window attention in high-resolution inference. This can significantly reduce redundant computation while preserving contextual dependencies. Second, S^2Flow adopts a second-order ODE solver based on Taylor expansion, where the high-order derivative is approximated via central difference from the low-resolution flow, facilitating accurate high-resolution denoising. Extensive experiments on VBench dataset demonstrate that S^2Flow outperforms prior methods in both visual quality and inference speed, enabling 4x acceleration on 2560x1536 video generation.

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IROS Conference 2025 Conference Paper

Capsizing-Guided Trajectory Optimization for Autonomous Navigation with Rough Terrain

Wei Zhang
Yinchuan Wang
Wangtao Lu
Pengyu Zhang
Xiang Zhang
Yue Wang
Chaoqun Wang

It is a challenging task for ground robots to autonomously navigate in harsh environments due to the presence of non-trivial obstacles and uneven terrain. This requires trajectory planning that balances safety and efficiency. The primary challenge is to generate a feasible trajectory that prevents robot from tip-over while ensuring effective navigation. In this paper, we propose a capsizing-aware trajectory planner (CAP) to achieve trajectory planning on the uneven terrain. The tip-over stability of the robot on rough terrain is analyzed. Based on the tip-over stability, we define the traversable orientation, which indicates the safe range of robot orientations. This orientation is then incorporated into a capsizing-safety constraint for trajectory optimization. We employ a graph-based solver to compute a robust and feasible trajectory while adhering to the capsizing-safety constraint. Extensive simulation and real-world experiments validate the effectiveness and robustness of the proposed method. The results demonstrate that CAP outperforms existing state-of-the-art approaches, providing enhanced navigation performance on uneven terrains.

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EAAI Journal 2025 Journal Article

Key node propagation-based overlapping spammer group detection algorithm on e-commerce platforms

Chaoqun Wang
Ning Li
Shuang Chen
Xiaoqing Bu
Shujuan Ji

With the rapid growth of e-commerce platforms, spammer groups have increasingly used fake reviews to influence consumer decisions, posing significant challenges to platform governance. This issue has become even more pronounced with the widespread use of large language models, which have made fake reviews harder to detect. However, existing spammer group detection algorithms have certain limitations. For example, they often overlook the core–periphery structure within spammer groups, failing to adequately focus on the core reviewers who play a crucial role in group operations. Additionally, these algorithms struggle to detect spammers who are active across multiple groups. To address these challenges, we propose an overlapping spammer group detection algorithm based on key node propagation (KNP-OSG). First, we model the review data as a co-review graph and use the Deep Q-Network algorithm combined with an action filtering mechanism to identify key reviewers, or key spammers, who have a critical impact on spammer group detection. Subsequently, based on the structural relationships among pivotal spammers, an improved label propagation algorithm, copra-g, is proposed to further identify spammer groups. Experimental results show that the KNP-OSG algorithm outperforms existing methods on real-world datasets, demonstrating its effectiveness in detecting overlapping spammer groups.

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AAAI Conference 2024 Conference Paper

Label Attentive Distillation for GNN-Based Graph Classification

Xiaobin Hong
Wenzhong Li
Chaoqun Wang
Mingkai Lin
Sanglu Lu

Graph Neural Networks (GNNs) have emerged as a powerful tool for modeling graph-structured data, exhibiting remarkable potential in applications such as social networks, recommendation systems, and molecular structures. However, the conventional GNNs perform node-level feature aggregation from neighbors without considering graph-label information, which leads to the misaligned embedding problem that may cause a detrimental effect on graph-level tasks such as graph classification. In this paper, we propose a novel label-attentive distillation method called LAD-GNN for graph representation learning to solve this problem. It alternatively trains a teacher model and a student GNN with a distillation-based approach. In the teacher model, a label-attentive encoder is proposed to encode the label information fusing with the node features to generate ideal embedding. In the student model, the ideal embedding is used as intermediate supervision to urge the student GNN to learn class-friendly node embedding to facilitate graph-level tasks. Generally, LAD-GNN is an enhanced GNN training approach that can be incorporated with arbitrary GNN backbone to improve performance without significant increase of computational cost. Extensive experiments with 7 GNN backbones based on 10 benchmark datasets show that LAD-GNN improves the SOTA GNNs in graph classification accuracy. The source codes of LAD-GNN are publicly available on https://github.com/XiaobinHong/LAD-GNN.

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AAAI Conference 2024 Conference Paper

The Causal Impact of Credit Lines on Spending Distributions

Yijun Li
Cheuk Hang Leung
Xiangqian Sun
Chaoqun Wang
Yiyan Huang
Xing Yan
Qi Wu
Dongdong Wang

Consumer credit services offered by electronic commerce platforms provide customers with convenient loan access during shopping and have the potential to stimulate sales. To understand the causal impact of credit lines on spending, previous studies have employed causal estimators, (e.g., direct regression (DR), inverse propensity weighting (IPW), and double machine learning (DML)) to estimate the treatment effect. However, these estimators do not treat the spending of each individual as a distribution that can capture the range and pattern of amounts spent across different orders. By disregarding the outcome as a distribution, valuable insights embedded within the outcome distribution might be overlooked. This paper thus develops distribution valued estimators which extend from existing real valued DR, IPW, and DML estimators within Rubin’s causal framework. We establish their consistency and apply them to a real dataset from a large electronic commerce platform. Our findings reveal that credit lines generally have a positive impact on spending across all quantiles, but consumers would allocate more to luxuries (higher quantiles) than necessities (lower quantiles) as credit lines increase.

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IJCAI Conference 2023 Conference Paper

DeLELSTM: Decomposition-based Linear Explainable LSTM to Capture Instantaneous and Long-term Effects in Time Series

Chaoqun Wang
Yijun Li
Xiangqian Sun
Qi Wu
Dongdong Wang
Zhixiang Huang

Time series forecasting is prevalent in various real-world applications. Despite the promising results of deep learning models in time series forecasting, especially the Recurrent Neural Networks (RNNs), the explanations of time series models, which are critical in high-stakes applications, have received little attention. In this paper, we propose a Decomposition-based Linear Explainable LSTM (DeLELSTM) to improve the interpretability of LSTM. Conventionally, the interpretability of RNNs only concentrates on the variable importance and time importance. We additionally distinguish between the instantaneous influence of new coming data and the long-term effects of historical data. Specifically, DeLELSTM consists of two components, i. e. , standard LSTM and tensorized LSTM. The tensorized LSTM assigns each variable with a unique hidden state making up a matrix h(t), and the standard LSTM models all the variables with a shared hidden state H(t). By decomposing the H(t) into the linear combination of past information h(t-1) and the fresh information h(t)-h(t-1), we can get the instantaneous influence and the long-term effect of each feature. In addition, the advantage of linear regression also makes the explanation transparent and clear. We demonstrate the effectiveness and interpretability of DeLELSTM on three empirical datasets. Extensive experiments show that the proposed method achieves competitive performance against the baseline methods and provides a reliable explanation relative to domain knowledge.

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NeurIPS Conference 2021 Conference Paper

Dual Progressive Prototype Network for Generalized Zero-Shot Learning

Chaoqun Wang
Shaobo Min
Xuejin Chen
Xiaoyan Sun
Houqiang Li

Generalized Zero-Shot Learning (GZSL) aims to recognize new categories with auxiliary semantic information, e. g. , category attributes. In this paper, we handle the critical issue of domain shift problem, i. e. , confusion between seen and unseen categories, by progressively improving cross-domain transferability and category discriminability of visual representations. Our approach, named Dual Progressive Prototype Network (DPPN), constructs two types of prototypes that record prototypical visual patterns for attributes and categories, respectively. With attribute prototypes, DPPN alternately searches attribute-related local regions and updates corresponding attribute prototypes to progressively explore accurate attribute-region correspondence. This enables DPPN to produce visual representations with accurate attribute localization ability, which benefits the semantic-visual alignment and representation transferability. Besides, along with progressive attribute localization, DPPN further projects category prototypes into multiple spaces to progressively repel visual representations from different categories, which boosts category discriminability. Both attribute and category prototypes are collaboratively learned in a unified framework, which makes visual representations of DPPN transferable and distinctive. Experiments on four benchmarks prove that DPPN effectively alleviates the domain shift problem in GZSL.

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AAAI Conference 2021 Conference Paper

Task-Independent Knowledge Makes for Transferable Representations for Generalized Zero-Shot Learning

Chaoqun Wang
Xuejin Chen
Shaobo Min
Xiaoyan Sun
Houqiang Li

Generalized Zero-Shot Learning (GZSL) targets recognizing new categories by learning transferable image representations. Existing methods find that, by aligning image representations with corresponding semantic labels, the semanticaligned representations can be transferred to unseen categories. However, supervised by only seen category labels, the learned semantic knowledge is highly task-specific, which makes image representations biased towards seen categories. In this paper, we propose a novel Dual-Contrastive Embedding Network (DCEN) that simultaneously learns taskspecific and task-independent knowledge via semantic alignment and instance discrimination. First, DCEN leverages task labels to cluster representations of the same semantic category by cross-modal contrastive learning and exploring semantic-visual complementarity. Besides task-specific knowledge, DCEN then introduces task-independent knowledge by attracting representations of different views of the same image and repelling representations of different images. Compared to high-level seen category supervision, this instance discrimination supervision encourages DCEN to capture low-level visual knowledge, which is less biased toward seen categories and alleviates the representation bias. Consequently, the task-specific and task-independent knowledge jointly make for transferable representations of DCEN, which obtains averaged 4. 1% improvement on four public benchmarks.

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