Title: Unlocking Universal Graph Knowledge in the View Space
Primary area: learning on graphs and other geometries & topologies
Keywords: Graph Neural Networks, Graph Foundation Models, View Space, Universal Graph Representation Learning

Abstract: Unlike recent foundation models in NLP and CV, which generalize across datasets through standardized inputs, graph foundation models remain limited to datasets with compatible feature spaces. Node features vary in both dimension and semantics across datasets, and many graphs lack features altogether, making parameter reuse for a graph-based model infeasible. To address this limitation, we introduce the view space, a new axis of graph representation where graphs of arbitrary sizes and feature specifications can be universally encoded. Building on this foundation, we propose Graph View Transformation (GVT), a parametric mapping that can transform the node-feature matrix of any graph through this shared space. We then construct Recurrent GVT (RGVT), a graph foundation model that serves as a substrate for universally shareable graph knowledge. Pretrained on OGBN-Arxiv and evaluated on 27 node-classification benchmarks, RGVT outperforms GraphAny, the only universal graph model to date, by +8.93\% and surpasses 12 individually tuned GNNs by at least +3.30\%. These results demonstrate that learning in the view space is a theoretically grounded and practically effective framework for universal graph representation learning (UGRL). Code and datasets are available at https://anonymous.4open.science/r/RGVT.

TL;DR: We introduce the view space, a third axis of graph representation, where transformations along this axis capture universal graph knowledge transferable across graphs with arbitrary features.