Semantic graphs are fundamental to the Web, enabling applications such as semantic search, recommendation, and knowledge-intensive reasoning. In decentralized Web environments, however, these graphs are distributed across organizations and constrained by strict privacy policies, making centralized training infeasible. Federated learning provides a promising solution, yet its effectiveness is severely limited by the dual incompleteness of real-world semantic graphs: missing node attributes and incomplete relational structures. Such dual missingness, often heterogeneous and unobserved across clients, causes substantial degradation in model performance. We present FedCL, a missingness-aware federated contrastive learning framework for dual-incomplete semantic graphs. FedCL introduces two key components: a topology estimation module, grounded in rate–distortion theory, that privately quantifies structural incompleteness across clients, and a federated reconstruction module that leverages these estimations to generate plausible relations without inferring sensitive attributes. To further improve robustness, FedCL integrates graph contrastive learning across reconstructed subgraphs, ensuring semantic consistency across heterogeneous and incomplete client graphs. Experiments on benchmark citation and Web datasets demonstrate that FedCL consistently outperforms state-of-the-art baselines in accuracy and robustness under heterogeneous missingness, while preserving strong privacy guarantees. These results highlight FedCL as a scalable and trustworthy approach for federated learning on incomplete semantic graphs, advancing privacy-preserving knowledge sharing on the Web.

Fairness-aware Graph Neural Networks (GNNs) often face a challenging trade-off, where prioritizing fairness may require compromising utility. In this work, we re-examine fairness through the lens of spectral graph theory, aiming to reconcile fairness and utility within the framework of spectral graph learning. We explore the correlation between sensitive features and spectrum in GNNs, using theoretical analysis to delineate the similarity between original sensitive features and those after convolution under different spectra. Our analysis reveals a reduction in the impact of similarity when the eigenvectors associated with the largest magnitude eigenvalue exhibit directional similarity. Based on these theoretical insights, we propose FUGNN, a novel spectral graph learning approach that harmonizes the conflict between fairness and utility. FUGNN ensures algorithmic fairness and utility by truncating the spectrum and optimizing eigenvector distribution during the encoding process. The fairness-aware eigenvector selection reduces the impact of convolution on sensitive features while concurrently minimizing the sacrifice of utility. FUGNN further optimizes the distribution of eigenvectors through a transformer architecture. By incorporating the optimized spectrum into the graph convolution network, FUGNN effectively learns node representations. Experiments on six real-world datasets demonstrate the superiority of FUGNN over baseline methods. The codes are available at https://github.com/yushuowiki/FUGNN.

Extracting latent disease patterns from electronic health records (EHRs) is a crucial solution for disease analysis, significantly facilitating healthcare decision-making. Multiview learning presents itself as a promising approach that offers a comprehensive exploration of both structured and unstructured EHRs. However, the intrinsic uncertainty among disease features presents a significant challenge for multiview feature alignment. Besides, the sparsity of real-world EHRs also exacerbates the difficulty of feature alignment. To address these challenges, we introduce a novel fuzzy multiview graph learning framework named FuzzyMVG, which is designed for mitigating the impacts of uncertainty in disease features derived from sparse EHRs. First, we utilize auxiliary information from sparse EHRs to construct a multiview EHR graph using the structured and unstructured records. Then, for efficient feature alignment, we specially design the fuzzy logic-enhanced graph convolutional networks to obtain the fuzzy representations of time-invariant node features. Thereby, we implement a random walk strategy and long short-term memory networks to capture the distinct features of static and dynamic nodes, respectively. Extensive experiments have been conducted on the real-world MIMIC III dataset to validate the effectiveness of FuzzyMVG. Results in the diagnosis prediction task demonstrate that FuzzyMVG outperforms other state-of-the-art baselines.

Understanding the latent disease patterns embedded in electronic health records (EHRs) is crucial for making precise and proactive healthcare decisions. Federated graph learning-based methods are commonly employed to extract complex disease patterns from the distributed EHRs without sharing the client-side raw data. However, the intrinsic characteristics of the distributed EHRs are typically non-independent and identically distributed (Non-IID), significantly bringing challenges related to data imbalance and leading to a notable decrease in the effectiveness of making healthcare decisions derived from the global model. To address these challenges, we introduce a novel personalized federated learning framework named PEARL, which is designed for disease prediction on Non-IID EHRs. Specifically, PEARL incorporates disease diagnostic code attention and admission record attention to extract patient embeddings from all EHRs. Then, PEARL integrates self-supervised learning into a federated learning framework to train a global model for hierarchical disease prediction. To improve the performance of the client model, we further introduce a fine-tuning scheme to personalize the global model using local EHRs. During the global model updating process, a differential privacy (DP) scheme is implemented, providing a high-level privacy guarantee. Extensive experiments conducted on the real-world MIMIC-III dataset validate the effectiveness of PEARL, demonstrating competitive results when compared with baselines.