Evolutionary Architecture Search for Graph Neural Networks

Understand

Automated machine learning (AutoML) has seen a resurgence in interest with the boom of deep learning over the past decade.

• In particular, Neural Architecture Search (NAS) has seen significant attention throughout the AutoML research community, and has pushed forward the state-of-the-art in a number of neural models to address grid-like data such as texts and images.
• However, very litter work has been done about Graph Neural Networks (GNN) learning on unstructured network data.
• Given the huge number of choices and combinations of components such as aggregator and activation function, determining the suitable GNN structure for a specific problem normally necessitates tremendous expert knowledge and laborious trails.