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Yangming Guo

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5 papers

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AAAI Conference 2026 Conference Paper

Transferable Hypergraph Attack via Injecting Nodes into Pivotal Hyperedges

Meixia He
Peican Zhu
Le Cheng
Yangming Guo
Manman Yuan
Keke Tang

Recent studies have demonstrated that hypergraph neural networks (HGNNs) are susceptible to adversarial attacks. However, existing methods rely on the specific information mechanisms of target HGNNs, overlooking the common vulnerability caused by the significant differences in hyperedge pivotality along aggregation paths in most HGNNs, thereby limiting the transferability and effectiveness of attacks. In this paper, we present a novel framework, i.e., Transferable Hypergraph Attack via Injecting Nodes into Pivotal Hyperedges (TH-Attack), to address these limitations. Specifically, we design a hyperedge recognizer via pivotality assessment to obtain pivotal hyperedges within the aggregation paths of HGNNs. Furthermore, we introduce a feature inverter based on pivotal hyperedges, which generates malicious nodes by maximizing the semantic divergence between the generated features and the pivotal hyperedges features. Lastly, by injecting these malicious nodes into the pivotal hyperedges, TH-Attack improves the transferability and effectiveness of attacks. Extensive experiments are conducted on six authentic datasets to validate the effectiveness of TH-Attack and the corresponding superiority to state-of-the-art methods.

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IJCAI Conference 2025 Conference Paper

HyperDet: Source Detection in Hypergraphs via Interactive Relationship Construction and Feature-rich Attention Fusion

Le Cheng
Peican Zhu
Yangming Guo
Keke Tang
Chao Gao
Zhen Wang

Hypergraphs offer superior modeling capabilities for social networks, particularly in capturing group phenomena that extend beyond pairwise interactions in rumor propagation. Existing approaches in rumor source detection predominantly focus on dyadic interactions, which inadequately address the complexity of more intricate relational structures. In this study, we present a novel approach for Source Detection in Hypergraphs (HyperDet) via Interactive Relationship Construction and Feature-rich Attention Fusion. Specifically, our methodology employs an Interactive Relationship Construction module to accurately model both the static topology and dynamic interactions among users, followed by the Feature-rich Attention Fusion module, which autonomously learns node features and discriminates between nodes using a self-attention mechanism, thereby effectively learning node representations under the framework of accurately modeled higher-order relationships. Extensive experimental validation confirms the efficacy of our HyperDet approach, showcasing its superiority relative to current state-of-the-art methods.

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AAAI Conference 2025 Conference Paper

Hypergraph Attacks via Injecting Homogeneous Nodes into Elite Hyperedges

Meixia He
Peican Zhu
Keke Tang
Yangming Guo

Recent studies have shown that Hypergraph Neural Networks (HGNNs) are vulnerable to adversarial attacks. Existing approaches focus on hypergraph modification attacks guided by gradients, overlooking node spanning in the hypergraph and the group identity of hyperedges, thereby resulting in limited attack performance and detectable attacks. In this manuscript, we present a novel framework, i.e., Hypergraph Attacks via Injecting Homogeneous Nodes into Elite Hyperedges (IE-Attack), to tackle these challenges. Initially, utilizing the node spanning in the hypergraph, we propose the elite hyperedges sampler to identify hyperedges to be injected. Subsequently, a node generator utilizing Kernel Density Estimation (KDE) is proposed to generate the homogeneous node with the group identity of hyperedges. Finally, by injecting the homogeneous node into elite hyperedges, IE-Attack improves the attack performance and enhances the imperceptibility of attacks. Extensive experiments are conducted on five authentic datasets to validate the effectiveness of IE-Attack and the corresponding superiority to state-of-the-art methods.

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IJCAI Conference 2025 Conference Paper

SourceDetMamba: A Graph-aware State Space Model for Source Detection in Sequential Hypergraphs

Le Cheng
Peican Zhu
Yangming Guo
Chao Gao
Zhen Wang
Keke Tang

Source detection on graphs has demonstrated high efficacy in identifying rumor origins. Despite advances in machine learning-based methods, many fail to capture intrinsic dynamics of rumor propagation. In this work, we present SourceDetMamba: A Graph-aware State Space Model for Source Detection in Sequential Hypergraphs, which harnesses the recent success of the state space model Mamba, known for its superior global modeling capabilities and computational efficiency, to address this challenge. Specifically, we first employ hypergraphs to model high-order interactions within social networks. Subsequently, temporal network snapshots generated during the propagation process are sequentially fed in reverse order into Mamba to infer underlying propagation dynamics. Finally, to empower the sequential model to effectively capture propagation patterns while integrating structural information, we propose a novel graph-aware state update mechanism, wherein the state of each node is propagated and refined by both temporal dependencies and topological context. Extensive evaluations on eight datasets demonstrate that SourceDetMamba consistently outperforms state-of-the-art approaches.

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EAAI Journal 2024 Journal Article

Smooth fusion of multi-spectral images via total variation minimization for traffic scene semantic segmentation

Ying Li
Aiqing Fang
Yangming Guo
Wei Sun
Xiaobao Yang
Xiaodong Wang

Achieving precise semantic segmentation for traffic scenes relies on adopting multi-spectral image fusion techniques to attain high-quality images. Many existing fusion solutions often aim to enhance the similarity between the input and fusion results at the pixel intensity and texture details stage. However, this can result in smoothness issues that limit semantic segmentation performance. To address these issues, we present a smooth representation learning optimization mechanism (SFLM) that conducts image fusion on two dimensions: inter- and intra-image levels. The former overcomes over- or under-smoothing problems via the mutual information maximization between the fusion result and image samples (i. e. , negative and positive). The latter balances under and over-smoothing for fusion results by minimizing the total variation in pixel space and maximizing the total variation in gradient space based on contrast learning. In this way, the proposed method effectively overcomes the fusion quality issues, providing better feature representations for semantic segmentation in autonomous vehicles. Experimental results on four public datasets validate our method’s effectiveness, robustness, and overall superiority.

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