Neural Graph Generator: Feature-Conditioned Graph Generation using Latent Diffusion Models
This addresses the challenge of efficient graph generation for machine learning applications, representing a shift in methodologies but likely incremental as it builds on existing diffusion models.
The paper tackles the problem of generating graphs with specific properties by introducing the Neural Graph Generator (NGG), which uses conditioned latent diffusion models to model complex graph patterns and offer control over generation, demonstrating versatility across various tasks.
Graph generation has emerged as a crucial task in machine learning, with significant challenges in generating graphs that accurately reflect specific properties. Existing methods often fall short in efficiently addressing this need as they struggle with the high-dimensional complexity and varied nature of graph properties. In this paper, we introduce the Neural Graph Generator (NGG), a novel approach which utilizes conditioned latent diffusion models for graph generation. NGG demonstrates a remarkable capacity to model complex graph patterns, offering control over the graph generation process. NGG employs a variational graph autoencoder for graph compression and a diffusion process in the latent vector space, guided by vectors summarizing graph statistics. We demonstrate NGG's versatility across various graph generation tasks, showing its capability to capture desired graph properties and generalize to unseen graphs. We also compare our generator to the graph generation capabilities of different LLMs. This work signifies a shift in graph generation methodologies, offering a more practical and efficient solution for generating diverse graphs with specific characteristics.