Xianxian Li

A Multi-Level Privacy-Preserving Approach to Hierarchical Data Based on Fuzzy Set Theory

2018 · Symmetry

Nowadays, more and more applications are dependent on storage and management of semi-structured information. For scientific research and knowledge-based decision-making, such data often needs to be published, e.g., medical data is released to implement a …

POI Recommendation with Federated Learning and Privacy Preserving in Cross Domain Recommendation

2021

Point-of-Interest (POI) recommendation is one of the most popular recommendation methodologies. However, POI data is very sensitive and sparse. Users' reluctance to share their context information due to privacy concerns, along with the cold-start problem …

A Trustworthy and Consistent Blockchain Oracle Scheme for Industrial Internet of Things

2023 · arXiv (Cornell University)

Blockchain provides decentralization and trustlessness features for the Industrial Internet of Things (IIoT), which expands the application scenarios of IIoT. To address the problem that the blockchain cannot actively obtain off-chain data, the blockchain oracle …

Relaxed Graph Semi-Supervised Contrastive Learning for Node Classification

2023

Graph Neural Networks (GNNs) have emerged as promising tools in graph semi-supervised learning. They acquire low-dimensional node embeddings for downstream tasks by aggregating and updating features from neighboring nodes. However, in a semi-supervised setting, the …

Mitigating Message Imbalance in Fraud Detection with Dual-View Graph Representation Learning

2024

Graph representation learning has become a mainstream method for fraud detection due to its strong expressive power, which focuses on enhancing node representations through improved neighborhood knowledge capture. However, the focus on local interactions leads …

Higher-order Semantic-aware Adaptive Graph Contrastive Learning

2024

Graph Contrastive Learning (GCL) has gained extensive attentions due to its success in label scarcity. GCL methods usually utilizes the graph neural network to learn node representation. However, the graph neural network can only aggregate …

Mitigating Message Imbalance in Fraud Detection with Dual-View Graph Representation Learning

2025 · arXiv (Cornell University)

Graph representation learning has become a mainstream method for fraud detection due to its strong expressive power, which focuses on enhancing node representations through improved neighborhood knowledge capture. However, the focus on local interactions leads …