Graph Neural Networks

Graph Neural Networks (GNNs) specialize in learning effective representation learning for large-scale complex graph-structured data, with particular emphasis on massive multi-layer heterogeneous graph networks. This research domain focuses on developing scalable GNN architectures tailored for complex graph networks with billions of nodes/edges, while enhancing performance across diverse graph mining tasks such as recommendation systems, anomaly detection, and beyond.