LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model
This work addresses the need for standardized evaluation in graph learning to advance GFMs, offering a new approach that could benefit researchers and practitioners in the field.
The authors tackled the inconsistency and limited scope in evaluating graph foundation models (GFMs) by proposing GFMBench, a comprehensive benchmark with 26 datasets, and introduced LangGFM, a GFM based solely on large language models that achieves state-of-the-art or better performance across this benchmark.
Graph foundation models (GFMs) have recently gained significant attention. However, the unique data processing and evaluation setups employed by different studies hinder a deeper understanding of their progress. Additionally, current research tends to focus on specific subsets of graph learning tasks, such as structural tasks, node-level tasks, or classification tasks. As a result, they often incorporate specialized modules tailored to particular task types, losing their applicability to other graph learning tasks and contradicting the original intent of foundation models to be universal. Therefore, to enhance consistency, coverage, and diversity across domains, tasks, and research interests within the graph learning community in the evaluation of GFMs, we propose GFMBench-a systematic and comprehensive benchmark comprising 26 datasets. Moreover, we introduce LangGFM, a novel GFM that relies entirely on large language models. By revisiting and exploring the effective graph textualization principles, as well as repurposing successful techniques from graph augmentation and graph self-supervised learning within the language space, LangGFM achieves performance on par with or exceeding the state of the art across GFMBench, which can offer us new perspectives, experiences, and baselines to drive forward the evolution of GFMs.