GOLD: Graph Out-of-Distribution Detection via Implicit Adversarial Latent Generation

TL;DR

GOLD introduces an implicit adversarial learning framework for graph out-of-distribution detection that synthesizes OOD data without pre-trained models, significantly improving detection performance on benchmark datasets.

Contribution

The paper proposes a novel implicit adversarial training method for graph OOD detection that does not require external OOD data or pre-trained generative models.

Findings

Outperforms state-of-the-art OOD detection methods on benchmark datasets.

Effectively simulates OOD scenarios without auxiliary data.

Demonstrates robustness across five graph datasets.

Abstract

Despite graph neural networks' (GNNs) great success in modelling graph-structured data, out-of-distribution (OOD) test instances still pose a great challenge for current GNNs. One of the most effective techniques to detect OOD nodes is to expose the detector model with an additional OOD node-set, yet the extra OOD instances are often difficult to obtain in practice. Recent methods for image data address this problem using OOD data synthesis, typically relying on pre-trained generative models like Stable Diffusion. However, these approaches require vast amounts of additional data, as well as one-for-all pre-trained generative models, which are not available for graph data. Therefore, we propose the GOLD framework for graph OOD detection, an implicit adversarial learning pipeline with synthetic OOD exposure without pre-trained models. The implicit adversarial training process employs a novel alternating optimisation framework by training: (1) a latent generative model to regularly imitate the in-distribution (ID) embeddings from an evolving GNN, and (2) a GNN encoder and an OOD detector to accurately classify ID data while increasing the energy divergence between the ID embeddings and the generative model's synthetic embeddings. This novel approach implicitly transforms the synthetic embeddings into pseudo-OOD instances relative to the ID data, effectively simulating exposure to OOD scenarios without auxiliary data. Extensive OOD detection experiments are conducted on five benchmark graph datasets, verifying the superior performance of GOLD without using real OOD data compared with the state-of-the-art OOD exposure and non-exposure baselines.