Summary of Higpt: Heterogeneous Graph Language Model, by Jiabin Tang et al.
HiGPT: Heterogeneous Graph Language Model
by Jiabin Tang, Yuhao Yang, Wei Wei, Lei Shi, Long Xia, Dawei Yin, Chao Huang
First submitted to arxiv on: 25 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 tackle the challenge of learning meaningful representations for nodes and edges in heterogeneous graphs. Heterogeneous graph neural networks (HGNNs) have made significant progress by considering relation heterogeneity and using specialized message functions and aggregation rules. However, existing frameworks have limitations when generalizing across diverse datasets. The authors propose HiGPT, a general large graph model that can learn from arbitrary heterogeneous graphs without fine-tuning. They also introduce an in-context heterogeneous graph tokenizer to capture semantic relationships and facilitate model adaptation. Additionally, they develop the Mixture-of-Thought (MoT) instruction augmentation paradigm to mitigate data scarcity by generating diverse instructions. The framework demonstrates exceptional performance in terms of generalization. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to solve a big problem: how to learn from different kinds of graphs that have lots of different relationships between things. Right now, most models are only good at learning from one specific type of graph. The authors want to make a model that can learn from any kind of graph and still be good at making predictions. They come up with a new way of training the model called HiGPT, which uses special instructions to help it understand the different relationships in each graph. They also find a way to generate more instructions so they don’t run out. This makes their model really good at generalizing to new graphs. |
Keywords
* Artificial intelligence * Fine tuning * Generalization * Tokenizer
