AI RESEARCH
Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection
arXiv CS.LG
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ArXi:2505.21285v4 Announce Type: replace 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 patterns and semantic variations while maintaining theoretical guarantees. Combining graph kernels and kernel density estimation (KDE) is a standard approach to graph density estimation, but has unsatisfactory performance due to the handcrafted and fixed features of kernels.