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Manman Yuan

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3 papers

2 author rows

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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ECAI Conference 2025 Conference Paper

Cross-Modality Disentanglement and Fusion via Hyperedge-Centric Graph Learning for Brain Network Connectivity Analysis

Manman Yuan
Jiapei Li
Junlin Li
Jiacheng Wang
Ting Xu
Can Yin

Analyzing brain network connectivity (BNC) using multimodal neuroimaging to identify neurodegenerative diseases has attracted increasing attention. However, current methods largely rely on node-centric graphs and assume structural or semantic alignment across modalities, limiting the capture of high-order interactions and modality-specific patterns critical for accurate disease identification. In this paper, we propose a novel Hyperedge-Centric Graph Learning Network (HCGLNet) to address these limitations. Specifically, we present a hyperedge-centric graph construction strategy (HGC) that represents each modality as a hyperedge-centric graph, explicitly modelling high-order connectivity unique to each modality. Moreover, we design a disentangled latent learning module (DLM) that factorize shared and specific representations, preserving modality-specific features from dilution while enabling the extraction of shared cross-modal representations. Finally, we develop a representation-aware routing (RAR) algorithm to adaptively fuse modality-specific and shared features based on learned weights, enhancing discriminability for downstream tasks. Experiments on three real-world datasets show that our HCGLNet abstraction reduces graph size by over 80%, lowers computation, and achieves state-of-the-art performance in neurodegenerative disease classification.

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ECAI Conference 2025 Conference Paper

D-HyperNet: Brain Disorder Identification in Directed Hypergraph via Effective Network Construction and Flow-Aware Feature Aggregation

Manman Yuan
Weiming Jia
Jiejie Fan
Junlin Li
Jiazhen Ye
Can Yin

Hypergraphs provide excellent modeling ability for brain disorder identification, especially in capturing high-order interactions among regions of interest (ROIs). Nevertheless, existing methods overlook the impact of directional hyperedges learning on the brain network, leading to wasteful functional connectivity and poor identification performance. To address the above issue, this paper proposes a novel Brain Disorder Identification method via Directed Hypergraph Networks (D-HyperNet). Specifically, our methodology employs an Effective Network Construction module to capture causal dependencies and infer directional functional connectivity among ROIs. Followed by the Flow-aware Feature Aggregation module, which designs a novel directed hypergraph encoder that directionally aggregates node features, effectively improving the accuracy and reliability of brain network representations. Additionally, we are integrating the proposed encoder into a contrastive learning program to obtain a more robust whole-brain representation. Extensive experiments demonstrate the efficacy of our D-HyperNet approach. The code is available at https: //github. com/Jia-Weiming/D-HyperNet.

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