Data Augmentation for Supervised Graph Outlier Detection with Latent
  Diffusion Models
Kay Liu Hengrui Zhang Ziqing Hu Fangxin Wang Philip S. Yu
Graph outlier detection is a prominent task of research and application in
the realm of graph neural networks. It identifies the outlier nodes that
exhibit deviation from the majority in the graph. One of the fundamental
challenges confronting supervised graph outlier detection algorithms is the
prevalent issue of class imbalance, where the scarcity of outlier instances
compared to normal instances often results in suboptimal performance.
Conventional methods mitigate the imbalance by reweighting instances in the
estimation of the loss function, assigning higher weights to outliers and lower
weights to inliers. Nonetheless, these strategies are prone to overfitting and
underfitting, respectively. Recently, generative models, especially diffusion
models, have demonstrated their efficacy in synthesizing high-fidelity images.
Despite their extraordinary generation quality, their potential in data
augmentation for supervised graph outlier detection remains largely
underexplored.
  To bridge this gap, we introduce GODM, a novel data augmentation for
mitigating class imbalance in supervised Graph Outlier detection with latent
Diffusion Models. Specifically, our proposed method consists of three key
components: (1) Variantioanl Encoder maps the heterogeneous information
inherent within the graph data into a unified latent space. (2) Graph Generator
synthesizes graph data that are statistically similar to real outliers from
latent space, and (3) Latent Diffusion Model learns the latent space
distribution of real organic data by iterative denoising. Extensive experiments
conducted on multiple datasets substantiate the effectiveness and efficiency of
GODM. The case study further demonstrated the generation quality of our
synthetic data. To foster accessibility and reproducibility, we encapsulate
GODM into a plug-and-play package and release it at the Python Package Index
(PyPI).