Synthetic Network

Synthetic networks are artificial representations of real-world networks, used to study network properties and test algorithms without the limitations of real-world data, such as privacy concerns or data scarcity. Current research focuses on generating realistic synthetic networks using generative models like Graph Neural Networks (GNNs), including variations like Graph Attention Networks (GATs), and evaluating their performance across various network topologies and feature types. This work is crucial for advancing graph machine learning, enabling robust algorithm development and evaluation in domains like social network analysis, intrusion detection, and power system modeling, where access to real data is often restricted.