5 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…
GraphVec: Cross-Domain Graph Vectorization for Graph-Level Representation Learning
Qi Feng, Jicong Fan
Learning universal graph representations across heterogeneous domains is difficult because graph datasets differ in topology, node-attribute semantics, feature dimensions, and even…
An Interdisciplinary and Cross-Task Review on Missing Data Imputation
Jicong Fan
Missing data is a fundamental challenge in data science, significantly hindering analysis and decision-making across a wide range of disciplines, including healthcare, bioinformati…
Medical Test-free Disease Detection Based on Big Data
Haokun Zhao, Yingzhe Bai, Qingyang Xu +3
Accurate disease detection is of paramount importance for effective medical treatment and patient care. However, the process of disease detection is often associated with extensive…
Graph Classification via Reference Distribution Learning: Theory and Practice
Zixiao Wang, Jicong Fan
Graph classification is a challenging problem owing to the difficulty in quantifying the similarity between graphs or representing graphs as vectors, though there have been a few m…