Summary of Multi-scale Heterogeneous Text-attributed Graph Datasets From Diverse Domains, by Yunhui Liu et al.
Multi-Scale Heterogeneous Text-Attributed Graph Datasets From Diverse Domains
by Yunhui Liu, Qizhuo Xie, Jinwei Shi, Jiaxu Shen, Tieke He
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 addresses the lack of research on heterogeneous text-attributed graphs (HTAGs) by introducing a collection of challenging benchmark datasets for evaluating machine learning models on HTAGs. HTAGs feature different types of entities connected by diverse relationships, unlike homogeneous graphs which have only one node and edge type. The introduced datasets are multi-scale, spanning years in duration, and cover various domains such as movie, community question answering, academic, literature, and patent networks. The paper conducts benchmark experiments on these datasets using graph neural networks. The authors provide public access to the source data, dataset construction codes, processed HTAGs, data loaders, benchmark codes, and evaluation setup at GitHub and Hugging Face. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps us understand how machines can learn from different types of information connected by various relationships. Right now, most research focuses on simple connections between similar things. But real-life situations often involve many different kinds of data linked in complex ways. To fill this gap, the authors created a set of large datasets that contain diverse types of text-based information. They used these datasets to test how well machine learning models can handle these complex relationships. |
Keywords
» Artificial intelligence » Machine learning » Question answering
