Summary of Graphfm: a Scalable Framework For Multi-graph Pretraining, by Divyansha Lachi et al.
GraphFM: A Scalable Framework for Multi-Graph Pretraining
by Divyansha Lachi, Mehdi Azabou, Vinam Arora, Eva Dyer
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 The paper introduces Graph Foundation Model (GraphFM), a scalable multi-graph multi-task pretraining approach for node classification tasks. GraphFM employs a Perceiver-based encoder that compresses domain-specific features into a common latent space, enabling generalization across diverse graphs. The authors demonstrate the efficacy of GraphFM by training a model on 152 graph datasets, establishing scaling laws for multi-graph pretraining and showing improved adaptability and stability compared to state-of-the-art specialist models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Graph neural networks are used in many different areas, like science and technology. This paper talks about how to make these networks better. The problem is that each type of data has its own special features, making it hard to create one network that works well on all kinds of data. To fix this, the authors came up with a new way to train their networks. They made a special model called Graph Foundation Model (GraphFM) that can work well on many different types of data at once. This makes it easier and faster to use these networks in real life. |
Keywords
» Artificial intelligence » Classification » Encoder » Generalization » Latent space » Multi task » Pretraining » Scaling laws
