Graph Transformers (GTs) are increasingly applied to social network analysis, yet their deployment is often constrained by fairness concerns. This issue is particularly critical in incomplete social networks, where sensitive attributes are frequently missing due to privacy and ethical restrictions. Existing solutions commonly generate these incomplete attributes, which may introduce additional biases and further compromise user privacy. To address this challenge, FairGE (Fair Graph Encoding) is introduced as a fairness-aware framework for GTs in incomplete social networks. Instead of generating sensitive attributes, FairGE encodes fairness directly through spectral graph theory. By leveraging the principal eigenvector to represent structural information and padding incomplete sensitive attributes with zeros to maintain independence, FairGE ensures fairness without data reconstruction. Theoretical analysis demonstrates that the method suppresses the influence of non-principal spectral components, thereby enhancing fairness. Extensive experiments on seven real-world social network datasets confirm that FairGE achieves at least a 16% improvement in both statistical parity and equality of opportunity compared with state-of-the-art baselines.

Cross-domain sequential recommendation is increasingly important in modern Web ecosystems, where user behaviors span multiple independently operated services that maintain strict data isolation for privacy and regulatory compliance. Federated learning offers a practical paradigm for such cross-domain collaboration, but user preferences evolve asynchronously across services, creating a substantial distribution shift. This drift leads to unstable and unequal domain contributions: behaviorally rich domains dominate global updates, while low-resource or volatile domains exert limited influence. Such an imbalance degrades recommendation accuracy and raises fundamental fairness concerns. To address these challenges, we propose FairFRL, a fairness-aware federated representation learning framework designed to mitigate contribution imbalance under dynamic cross-domain drift. FairFRL mitigates contribution imbalance under dynamic cross-domain drift by jointly regulating domain influence during federated aggregation and disentangling domain-shared and domain-exclusive semantics, while preserving data locality. Experiments on real-world Amazon multi-domain datasets show that FairFRL consistently outperforms strong federated and centralized baselines across multiple metrics and achieves more equitable cross-domain contributions. These results position FairFRL as a principled step toward responsible, fair, and socially aligned Web recommendation systems.

Cross-domain sequential recommendation (CDSR) plays a critical role in decentralized Web applications by leveraging user behavior sequences across multiple platforms to alleviate data sparsity and capture dynamic preferences. However, existing federated CDSR frameworks face two fundamental challenges: (i) heterogeneous sequential interactions that encode domain-exclusive semantics and cannot be directly shared under privacy constraints, and (ii) strong trust assumptions that both servers and clients behave honestly, leaving federated training vulnerable to misreporting, malicious updates, and negative transfer. In this paper, we propose VeriFRL, a verifiable federated representation learning framework for cross-domain sequential recommendation. VeriFRL adopts a dual-module design that integrates representation learning with verifiable training: an attention-based variational encoder disentangles domain-shared and domain-exclusive representations to support transferable and privacy-preserving knowledge sharing, while a contribution evaluation module quantifies client-level and feature-level influences to enable verifiability, interpretability, and negative transfer detection. Extensive experiments on real-world multi-domain datasets demonstrate that VeriFRL achieves competitive or superior recommendation performance over state-of-the-art federated CDSR methods, while providing fine-grained insights into cross-domain knowledge transfer dynamics.

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.

Transformers, with their self-attention mechanisms and positional encoding, excel at modeling long-range dependencies. Such attribute has demonstrated significant potential in capturing complex disease patterns by integrating multimodal information, for example clinical notes and radiographs. However, their reliance on pre-trained deep neural networks to extract modality-specific features from large datasets makes them vulnerable to backdoor attacks, posing critical challenges for their deployment in healthcare applications. To address these vulnerabilities, we propose a robust multimodal Transformer-based framework, RMTrans, which mitigates the impact of malicious imaging data containing backdoor triggers while enhancing the model’s robustness. In the imaging data pre-processing stage, we introduce an efficient patch-based processing method that shifts the model’s focus toward learning global features rather than overfitting to localized (patch-level) patterns, thereby ensuring a more secure and reliable training process. Following this, we fuse multimodal representations and train a Vision Transformer (ViT) for disease prediction. Extensive experiments conducted on real-world datasets, including MIMIC-IV and MIMIC-CXR, validate the effectiveness of RMTrans. The proposed framework outperforms state-of-the-art baselines, demonstrating its potential as a secure and reliable solution for multimodal disease prediction.

Generating post hoc causal explanations for graph neural network-based recommender systems is vital for enhancing the credibility and interpretability of recommendations. Existing model-agnostic explainers primarily capture statistical correlations between topological information and recommendation outcomes. However, they often fail to identify true causal relationships due to their model-agnostic design and the challenges posed by heterogeneous graph structures. To address these limitations, we propose a causality-inspired graph neural network explainer for recommender systems, namely CaGE, which generates explanations reflecting causality in recommendation scenarios without accessing the internal parameters of the recommender system. Unlike previous explainers that rely on correlation-based learning, CaGE leverages heterogeneous interventional distributions to eliminate backdoor paths of non-causal variables in the structural causal model of the recommendation task, ensuring causation is accurately captured. Specifically, CaGE incorporates backdoor adjustment based on heterogeneous interventional distributions and causal contrastive learning to optimize a set of heterogeneous soft masks that disentangle causation from non-causation. Additionally, a causality-inspired meta-path search strategy is employed to represent causation as paths between users and recommended items, further enhancing explanation readability. Extensive experiments are conducted on three recommendation datasets, and the experimental results illustrate the superior fidelity of CaGE as compared to state-of-the-art baselines.

Recommender systems have achieved great success in our daily life. In recent years, the ethical concerns of AI systems have gained lots of attention. At the same time, graph learning techniques are powerful in modelling the complex relations among users and items under recommender system applications. These graph learning- based methods are data hungry, which brought a significant data efficiency challenge. In this proposal, I introduce my PhD research from three aspects: 1) Efficient privacy-preserving recommendation for imbalanced data. 2) Efficient recommendation model training for Insufficient samples. 3) Explainability in the social recommendation. Challenges and solutions of the above research problems have been proposed in this proposal.

Graph Convolutional Networks (GCNs) have achieved impressive performance in a wide variety of areas, attracting considerable attention. The core step of GCNs is the information-passing framework that considers all information from neighbors to the central vertex to be equally important. Such equal importance, however, is inadequate for scale-free networks, where hub vertices propagate more dominant information due to vertex imbalance. In this paper, we propose a novel centrality-based framework named CenGCN to address the inequality of information. This framework first quantifies the similarity between hub vertices and their neighbors by label propagation with hub vertices. Based on this similarity and centrality indices, the framework transforms the graph by increasing or decreasing the weights of edges connecting hub vertices and adding self-connections to vertices. In each non-output layer of the GCN, this framework uses a hub attention mechanism to assign new weights to connected non-hub vertices based on their common information with hub vertices. We present two variants CenGCN_D and CenGCN_E, based on degree centrality and eigenvector centrality, respectively. We also conduct comprehensive experiments, including vertex classification, link prediction, vertex clustering, and network visualization. The results demonstrate that the two variants significantly outperform state-of-the-art baselines.

Finding both efficient and effective quantitative representations for scholars in scientific digital libraries has been a focal point of research. The unprecedented amounts of scholarly datasets, combined with contemporary machine learning and big data techniques, have enabled intelligent and automatic profiling of scholars from this vast and ever-increasing pool of scholarly data. Meanwhile, recent advance in network embedding techniques enables us to mitigate the challenges of large scale and sparsity of academic collaboration networks. In real-world academic social networks, scholars are accompanied with various attributes or features, such as co-authorship and publication records, which result in attributed collaboration networks. It has been observed that both network topology and scholar attributes are important in academic relationship mining. However, previous studies mainly focus on network topology, whereas scholar attributes are overlooked. Moreover, the influence of different scholar attributes are unclear. To bridge this gap, in this work, we present a novel framework of Attributed Collaboration Network Embedding (ACNE) for academic relationship mining. ACNE extracts four types of scholar attributes based on the proposed scholar profiling model, including demographics, research, influence, and sociability. ACNE can learn a low-dimensional representation of scholars considering both scholar attributes and network topology simultaneously. We demonstrate the effectiveness and potentials of ACNE in academic relationship mining by performing collaborator recommendation on two real-world datasets and the contribution and importance of each scholar attribute on scientific collaborator recommendation is investigated. Our work may shed light on academic relationship mining by taking advantage of attributed collaboration network embedding.