Scalable Learning-Based Community-Preserving Graph Generation

Graph generation plays an essential role in understanding the formation of complex network structures across various fields, such as biological and social networks. Recent studies have shifted towards employing deep learning methods to grasp the topology of graphs. Yet, most current graph generators fail to adequately capture the community structure, which stands out as a critical and distinctive aspect of graphs. Additionally, these generators are generally limited to smaller graphs because of their inefficiencies and scaling challenges. This paper introduces the Community-Preserving Graph Adversarial Network (CPGAN), designed to effectively simulate graphs. CPGAN leverages graph convolution networks within its encoder and maintains shared parameters during generation to encapsulate community structure data and ensure permutation invariance. We also present the Scalable Community-Preserving Graph Attention Network (SCPGAN), aimed at enhancing the scalability of our model. SCPGAN considerably cuts down on inference and training durations, as well as GPU memory usage, through the use of an ego-graph sampling approach and a short-pipeline autoencoder framework. Tests conducted on six real-world graph datasets reveal that CPGAN manages a beneficial balance between efficiency and simulation quality when compared to leading-edge baselines. Moreover, SCPGAN marks substantial strides in model efficiency and scalability, successfully increasing the size of generated graphs to the 10 million node level while maintaining competitive quality, on par with other advanced learning models.