Summary of Langgfm: a Large Language Model Alone Can Be a Powerful Graph Foundation Model, by Tianqianjin Lin et al.
LangGFM: A Large Language Model Alone Can be a Powerful Graph Foundation Model
by Tianqianjin Lin, Pengwei Yan, Kaisong Song, Zhuoren Jiang, Yangyang Kang, Jun Lin, Weikang Yuan, Junjie Cao, Changlong Sun, Xiaozhong Liu
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Social and Information Networks (cs.SI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers propose a unified benchmark for evaluating graph foundation models (GFMs), aiming to enhance consistency and diversity in the field. They introduce LangGFM, a novel GFM that leverages large language models to achieve state-of-the-art performance on 26 datasets. The authors argue that current research focuses too much on specific tasks and specialized modules, limiting the applicability of GFMs across different domains. By revisiting graph textualization principles and repurposing techniques from graph augmentation and self-supervised learning in the language space, LangGFM outperforms or matches state-of-the-art results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to test computers that can learn from graphs. Graphs are like maps that show connections between things. Right now, different researchers use different tests to see how well these computer models work. This makes it hard to compare the results and understand which models are really good. The authors of this paper want to change that by creating a single test that covers many different types of graph learning tasks. They also introduce a new type of model called LangGFM, which uses large language models to analyze graphs. This new approach works just as well or even better than the best current models on 26 different tests. |
Keywords
* Artificial intelligence * Self supervised
