Summary of Taglas: An Atlas Of Text-attributed Graph Datasets in the Era Of Large Graph and Language Models, by Jiarui Feng et al.
TAGLAS: An atlas of text-attributed graph datasets in the era of large graph and language models
by Jiarui Feng, Hao Liu, Lecheng Kong, Mingfang Zhu, Yixin Chen, Muhan Zhang
First submitted to arxiv on: 20 Jun 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 The proposed TAGLAS (Text-Attributed Graph LAS) atlas is a comprehensive collection of text-attributed graph (TAG) datasets and benchmarks, designed for training graph-language or graph foundation models. The dataset comprises over 23 TAGs spanning various domains, including citation graphs, molecule graphs, and tasks such as node classification and graph question-answering. A unified node and edge text feature format enables simultaneous model training and evaluation across multiple datasets from diverse domains. The project provides standardized tools for loading datasets, converting text to embeddings, and vice versa, facilitating different evaluation scenarios. Additionally, the TAGLAS framework includes standard and easy-to-use evaluation utilities. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TAGLAS is a new way of organizing and using graphs with words or texts attached to each node or edge. Imagine having many different kinds of graphs that can be used for training special types of AI models called graph-language or graph foundation models. The TAGLAS team collected over 23 of these graphs, which are from different areas like citation graphs (where nodes represent papers and edges show how they’re related), molecule graphs (for studying chemical structures), and more. Each graph has words or texts attached to the nodes and edges, making it easier for AI models to understand and work with them. The team also made tools that make it simple to use these graphs and compare different AI models on them. |
Keywords
» Artificial intelligence » Classification » Question answering
