Improving Graph Machine Learning Performance Through Feature Augmentation Based on Network Control Theory

arXiv:2405.03706v1 Announce Type: new
Abstract: Network control theory (NCT) offers a robust analytical framework for understanding the influence of network topology on dynamic behaviors, enabling researchers to decipher how certain patterns of external control measures can steer system dynamics towards desired states. Distinguished from other structure-function methodologies, NCT's predictive capabilities can be coupled with deploying Graph Neural Networks (GNNs), which have demonstrated exceptional utility in various network-based learning tasks. However, the performance of GNNs heavily relies on the expressiveness of …

abstract arxiv augmentation control cs.lg dynamic dynamics enabling feature framework function graph improving influence machine machine learning network patterns performance researchers robust theory through topology type understanding