GILT: An LLM-Free, Tuning-Free Graph Foundational Model for In-Context Learning

ArXi:2510.04567v2 Announce Type: replace-cross Graph Neural Networks (GNNs) are powerful tools for processing relational data but often struggle to generalize to unseen graphs, giving rise to the development of Graph Foundational Models (GFMs). However, current GFMs are challenged by the extreme heterogeneity of graph data, where each graph can possess a unique feature space, label set, and topology. To address this, two main paradigms have emerged.