9 papers
A Robust Watermark-based Fingerprint Framework for GNNs Ownership Verification
Han Zhang, Yan Wang, Guanfeng Liu +3
The high training cost of Graph Neural Networks (GNNs) has raised growing concerns regarding model ownership infringement, such as model stealing and unauthorized misuse. To verify…
Re-understanding Graph Unlearning through Memorization
Pengfei Ding, Yan Wang, Guanfeng Liu
Graph unlearning (GU), which removes nodes, edges, or features from trained graph neural networks (GNNs), is crucial in Web applications where graph data may contain sensitive, mis…
Towards Comprehensive and Prerequisite-Free Explainer for Graph Neural Networks
Han Zhang, Yan Wang, Guanfeng Liu +3
To enhance the reliability and credibility of graph neural networks (GNNs) and improve the transparency of their decision logic, a new field of explainability of GNNs (XGNN) has em…
Adaptive Graph Unlearning
Pengfei Ding, Yan Wang, Guanfeng Liu +1
Graph unlearning, which deletes graph elements such as nodes and edges from trained graph neural networks (GNNs), is crucial for real-world applications where graph data may contai…
Causal-Invariant Cross-Domain Out-of-Distribution Recommendation
Jiajie Zhu, Yan Wang, Feng Zhu +3
Cross-Domain Recommendation (CDR) aims to leverage knowledge from a relatively data-richer source domain to address the data sparsity problem in a relatively data-sparser target do…
Few-shot Learning on Heterogeneous Graphs: Challenges, Progress, and Prospects
Pengfei Ding, Yan Wang, Guanfeng Liu
Few-shot learning on heterogeneous graphs (FLHG) is attracting more attention from both academia and industry because prevailing studies on heterogeneous graphs often suffer from l…
Few-Shot Causal Representation Learning for Out-of-Distribution Generalization on Heterogeneous Graphs
Pengfei Ding, Yan Wang, Guanfeng Liu +2
Heterogeneous graph few-shot learning (HGFL) has been developed to address the label sparsity issue in heterogeneous graphs (HGs), which consist of various types of nodes and edges…
Cross-heterogeneity Graph Few-shot Learning
Pengfei Ding, Yan Wang, Guanfeng Liu
In recent years, heterogeneous graph few-shot learning has been proposed to address the label sparsity issue in heterogeneous graphs (HGs), which contain various types of nodes and…
Few-Shot Semantic Relation Prediction across Heterogeneous Graphs
Pengfei Ding, Yan Wang, Guanfeng Liu +1
Semantic relation prediction aims to mine the implicit relationships between objects in heterogeneous graphs, which consist of different types of objects and different types of lin…