Unseen Graph

Unseen graph research focuses on developing graph neural networks (GNNs) and algorithms capable of generalizing to new, unseen graph structures, a crucial challenge in many applications. Current efforts concentrate on improving GNN generalization through techniques like pre-training, in-context learning, and the development of foundation models that can adapt to diverse graph characteristics. This work is significant because it addresses the limitations of existing GNNs, which often struggle to perform well on graphs differing from their training data, thereby expanding the applicability of graph-based methods to real-world scenarios where data is heterogeneous and incomplete.