On Generalization of Graph Autoencoders with Adversarial Training
This work addresses generalization for graph data applications, but it is incremental as it adapts existing adversarial training methods to graph autoencoders.
The paper tackles the problem of improving generalization in graph autoencoders by applying adversarial training, showing that both L2 and L1 versions boost performance in link prediction, node clustering, and graph anomaly detection.
Adversarial training is an approach for increasing model's resilience against adversarial perturbations. Such approaches have been demonstrated to result in models with feature representations that generalize better. However, limited works have been done on adversarial training of models on graph data. In this paper, we raise such a question { does adversarial training improve the generalization of graph representations. We formulate L2 and L1 versions of adversarial training in two powerful node embedding methods: graph autoencoder (GAE) and variational graph autoencoder (VGAE). We conduct extensive experiments on three main applications, i.e. link prediction, node clustering, graph anomaly detection of GAE and VGAE, and demonstrate that both L2 and L1 adversarial training boost the generalization of GAE and VGAE.