Summary of Unigraph: Learning a Unified Cross-domain Foundation Model For Text-attributed Graphs, by Yufei He et al.
UniGraph: Learning a Unified Cross-Domain Foundation Model for Text-Attributed Graphs
by Yufei He, Yuan Sui, Xiaoxin He, Bryan Hooi
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 This paper presents a novel framework for learning foundation models that can generalize to unseen graph structures and tasks across diverse domains. The UniGraph framework leverages text-based features to unify node representations, even for graphs without inherent textual information. A cascaded architecture of Language Models (LMs) and Graph Neural Networks (GNNs) serves as the backbone network. The proposed pre-training algorithm, Masked Graph Modeling, is designed for large-scale self-supervised learning on Text-Attributed Graphs (TAGs). Additionally, graph instruction tuning using Large Language Models (LLMs) enables zero-shot prediction ability. Comprehensive experiments demonstrate the model’s effectiveness in self-supervised representation learning, few-shot transfer, and zero-shot transfer, outperforming or matching GNNs trained on target datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating a new way to learn from graphs that can be used for many different tasks. Graphs are like maps that show connections between things. Right now, we have ways to learn from specific types of graphs, but this new method lets us learn from any graph and use it for many different purposes. The new method uses text-based information to help understand the graph. It’s like having a map with extra information that helps you navigate. This approach allows us to teach computers to learn from graphs without needing special training data. The results show that this new way of learning is effective and can be used in many situations. |
Keywords
* Artificial intelligence * Few shot * Instruction tuning * Representation learning * Self supervised * Zero shot
