4 papers
Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection
Xudong Wang, Ziheng Sun, Chris Ding +1
This work proposes a framework LGKDE that learns kernel density estimation for graphs. The key challenge in graph density estimation lies in effectively capturing both structural p…
GraphProp: Training the Graph Foundation Models using Graph Properties
Ziheng Sun, Qi Feng, Lehao Lin +2
This work focuses on training graph foundation models (GFMs) that have strong generalization ability in graph-level tasks such as graph classification. Effective GFM training requi…
Explainable Graph Representation Learning via Graph Pattern Analysis
Xudong Wang, Ziheng Sun, Chris Ding +1
Explainable artificial intelligence (XAI) is an important area in the AI community, and interpretability is crucial for building robust and trustworthy AI models. While previous wo…
K-means Derived Unsupervised Feature Selection using Improved ADMM
Ziheng Sun, Chris Ding, Jicong Fan
Feature selection is important for high-dimensional data analysis and is non-trivial in unsupervised learning problems such as dimensionality reduction and clustering. The goal of…