Learnable Kernel Density Estimation for Graphs
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Learnable Kernel Density Estimation for Graphs and Its Application to Graph-Level Anomaly Detection
arXiv:2505.21285v5 Announce Type: replace Abstract: 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.