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Yanglan Gan

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3 papers

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TAAS Journal 2025 Journal Article

Combining Personalized Federated Hypernetworks and Shared Residual Learning for Distributed QoS Prediction

Guobing Zou
Shiyi Lin
Shaogang Wu
Shengxiang Hu
Song Yang
Yanglan Gan
Bofeng Zhang
Yixin Chen

Connected vehicles due to the high mobility and dynamic network topologies of connected vehicles require accurate QoS that includes high throughput and low latency to assess satisfactory QoE. Existing methods mainly focus on centralized QoS prediction while paying little attention to distributed mobile QoS prediction, making it challenging to protect user privacy information when invoking Web services. Moreover, even though some advanced centralized methods can be transformed into federated architectures, they often face difficulty in capturing latent feature representations of users and services and learning personalized prediction layers between them due to the heterogeneity of the QoS dataset. To address the above issues, we propose a novel framework for distributed QoS prediction, called Combining Personalized Federated Hypernetworks and Shared Residual Learning for Distributed QoS Prediction (FHR-DQP). FHR-DQP adopts the federated averaging (FedAvg) to aggregate location-aware residual shared feature information across all clients. Additionally, a hypernetwork is leveraged to generate personalized networks for user-service QoS prediction in each client. These components are integrated as a hybrid framework that performs training using a federated approach and makes personalized QoS predictions within each client. Extensive experiments are conducted on a real-world benchmark QoS dataset called WS-DREAM, containing nearly 2,000,000 historical QoS invocation records. Compared with both centralized and federated competing baselines, the results demonstrate that FHR-DQP achieves the highest performance for distributed QoS prediction, when it provides privacy-preserving of users’ QoS invocations.

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JBHI Journal 2025 Journal Article

HCMAF: Hierarchical Feature Aggregation and Cross-Modal Attention Fusion Framework for Multi-Omics patient classification

Yanglan Gan
Hangkai Zhao
Kaili Wang
Cairong Yan
Guobing Zou

The accumulation of large-scale multi-omics datasets has brought new opportunities for precise disease treatment. However, the inherent complexity of inter- and intra-omics relationships presents considerable obstacles to the precise integration of multi-omics data. Here, we propose a Hierarchical Feature Aggregation and Cross-Modal Attention Fusion (HCMAF) framework to integrate multi-omics data for patient classification and biomarker identification. Specifically, to capture both the specific information inherent in each omics data and complex cross-omics interactions, HCMAF incorporates three innovative modules. The hierarchical feature aggregation graph attention (HGAT) module captures intra-omics topological features through adaptive neighborhood aggregation. The cross-modal attention (CMA) module pinpoints inter-omics complementarity by modeling cross-omics dependencies. Finally, the confidence-driven multi-omics fusion (CMF) module dynamically integrates omics-specific predictions through learnable reliability weights. Comprehensive experiments on four public benchmark datasets show that HCMAF achieves better classification performance and consistently surpasses leading existing methods. Further component analysis confirms the crucial role of the HGAT, CMA and CMF modules in ensuring overall model effectiveness. HCMAF is implemented in Python using the Pytorch machine-learning library, and it is freely available at https://github.com/DHUDBlab/HCMAF.

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AAAI Conference 2025 Conference Paper

Large Language Model Meets Graph Neural Network in Knowledge Distillation

Shengxiang Hu
Guobing Zou
Song Yang
Shiyi Lin
Yanglan Gan
Bofeng Zhang
Yixin Chen

While Large Language Models (LLMs) show promise for Text-Attributed Graphs (TAGs) learning, their deployment is hindered by computational demands. Graph Neural Networks (GNNs) are efficient but struggle with TAGs' complex semantics. We propose LinguGKD, a novel LLM-to-GNN knowledge distillation framework that enables transferring both local semantic details and global structural information from LLMs to GNNs. First, it introduces TAG-oriented instruction tuning, enhancing LLMs with graph-specific knowledge through carefully designed prompts. Next, it develops a layer-adaptive multi-scale contrastive distillation strategy aligning LLM and GNN features at multiple granularities, from node-level to graph-level. Finally, the distilled GNNs combine the semantic richness of LLMs with the computational efficiency of traditional GNNs. Experiments demonstrate that LinguGKD outperforms existing graph distillation frameworks, the distilled simple GNNs achieve comparable or superior performance to more complex GNNs and teacher LLMs, while maintaining computational efficiency. This work bridges the gap between LLMs and GNNs, facilitating advanced graph learning in resource-constrained environments and providing a framework to leverage ongoing LLM advancements for GNN improvement.

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