[Paper Review] Distribution-induced Bidirectional Generative Adversarial Network for Graph Representation Learning

Shuai Zheng, Zhenfeng Zhu+4 more|arXiv (Cornell University)|Dec 4, 2019

Advanced Graph Neural Networks40 references4 citations

TL;DR

This paper proposes DBGAN, a distribution-induced bidirectional generative adversarial network for graph representation learning that estimates structure-aware prior distributions via prototype learning, enabling robust and discriminative node representations. By enforcing bidirectional adversarial learning between graph and latent spaces, DBGAN achieves superior performance in link prediction and node clustering with improved generalization and dimension efficiency.

ABSTRACT

Graph representation learning aims to encode all nodes of a graph into low-dimensional vectors that will serve as input of many compute vision tasks. However, most existing algorithms ignore the existence of inherent data distribution and even noises. This may significantly increase the phenomenon of over-fitting and deteriorate the testing accuracy. In this paper, we propose a Distribution-induced Bidirectional Generative Adversarial Network (named DBGAN) for graph representation learning. Instead of the widely used normal distribution assumption, the prior distribution of latent representation in our DBGAN is estimated in a structure-aware way, which implicitly bridges the graph and feature spaces by prototype learning. Thus discriminative and robust representations are generated for all nodes. Furthermore, to improve their generalization ability while preserving representation ability, the sample-level and distribution-level consistency is well balanced via a bidirectional adversarial learning framework. An extensive group of experiments are then carefully designed and presented, demonstrating that our DBGAN obtains remarkably more favorable trade-off between representation and robustness, and meanwhile is dimension-efficient, over currently available alternatives in various tasks.

Motivation & Objective

• Address the limitation of existing graph representation methods that rely on arbitrary normal distribution priors, which can lead to overfitting and poor robustness.
• Improve generalization and robustness of learned node representations by modeling the true underlying data distribution in a structure-aware manner.
• Balance sample-level and distribution-level consistency through a bidirectional adversarial learning framework to preserve both local and global graph structure.
• Develop a method that maintains high representation capability while being dimension-efficient, outperforming existing approaches under low-dimensional settings.

Proposed method

• Replace the standard Gaussian prior with a structure-aware prior distribution estimated via cross-domain prototypes learned from graph and feature spaces.
• Introduce a bidirectional adversarial framework: a generator maps latent representations back to graph data, while a discriminator distinguishes real from generated samples in both directions.
• Use prototype learning to implicitly align graph space and feature space, enabling the model to capture structural consistency in the latent space.
• Train the encoder and generator jointly via adversarial loss to enforce distribution consistency and improve robustness against noise and over-smoothing.
• Balance multi-level consistency (sample and distribution) through adversarial training, ensuring that both individual node representations and global data distribution are preserved.
• Apply t-SNE visualization to demonstrate that DBGAN preserves class separability and adjacency relationships in low-dimensional space.

Experimental results

Research questions

• RQ1 Can a structure-aware prior distribution estimation improve the robustness and generalization of graph representation learning compared to standard Gaussian priors?
• RQ2 Does bidirectional adversarial learning between latent space and graph space enhance both representation quality and distribution consistency?
• RQ3 How does the proposed method perform in comparison to state-of-the-art methods in link prediction and node clustering tasks?
• RQ4 To what extent does the dimensionality of the latent representation affect the performance of the proposed model?
• RQ5 Can the model preserve structural and feature relationships in low-dimensional space without supervision?

Key findings

• DBGAN achieves a 94.5% AUC on link prediction and 75.9% NMI on node clustering for the Cora dataset, outperforming all ablated variants and baselines.
• The ablation study shows that removing either bidirectional adversarial learning (BAL) or prior distribution estimation (PDE) leads to significant performance drops, with AUC decreasing by 2.1% and 1.5% respectively.
• DBGAN maintains strong performance even with low-dimensional latent representations (e.g., 128 dimensions), demonstrating superior dimension efficiency compared to methods requiring higher dimensions.
• Visualization results show that DBGAN-generated representations exhibit clearer class separation and reduced within-class scatter compared to GAE, DGI, and raw features.
• The model with both BAL and PDE achieves the best performance across all metrics, confirming the complementary role of both components in enhancing robustness and representation quality.
• Performance improves with increasing latent dimension, but DBGAN still achieves state-of-the-art results even at low dimensions (e.g., 64–128), confirming its efficiency.