GLEX: Executable Program Vocabularies for Text-Free Graph Foundation Models

Few-shot transfer across heterogeneous graphs is challenging in the text-free setting, where source and target domains may differ in feature semantics, structural statistics, and label spaces. Existing transfer interfaces are often built on representations, aligned coordinates, or structural patterns whose usefulness can depend on the graph on which they are formed. We introduce GLEX (Graph Lexicon of Executable Programs), which instead builds a transferable vocabulary of reusable graph computations. The same computation can be instantiated differently on each graph, providing a common interface while naturally adapting to heterogeneous features and structures. On an unseen target, the pretrained encoder remains fixed, and a few labeled examples determine which computations are most useful for the target task and refine the resulting predictions. Extensive experiments on seven heterogeneous target graphs cover both node and graph classification from one-shot to ten-shot settings. GLEX improves average one-shot node-classification accuracy from 46.52% to 52.95% over the strongest text-free multi-domain baseline BRIDGE, while also improving average graph-classification accuracy from 51.97% to 53.54%. Ablation studies identify the executable vocabulary and support-conditioned adaptation as the main contributors, while efficiency, sensitivity, and program-use analyses further characterize how GLEX behaves across target graphs.