# GraphFormers: GNN-nested Transformers for Representation Learning on\n Textual Graph

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/graphformers-gnn-nested-transformers-for-representation-learning-on-n-textual/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Junhan Yang,Zheng Liu,Shitao Xiao,Chaozhuo Li,Defu Lian,Sanjay Agrawal,Amit Prakash Singh,Guangzhong Sun,Xing Xie |
| Citations | 51 |
| DOI | 10.48550/arxiv.2105.02605 |
| Fields | Computer Science |
| Open Access | true |
| OA Status | green |
| OA URL | https://arxiv.org/pdf/2105.02605 |
| OpenAlex ID | https://openalex.org/W3212640459 |
| Type | preprint |
| Year | 2021 |

## Paper authors

- [Amit Prakash Singh](https://scholariq.org/researchers/amit-prakash-singh/)

## Paper journal

- [arXiv (Cornell University)](https://scholariq.org/journals/arxiv-cornell-university/)

## Paper primary topic

- [Advanced Graph Neural Networks](https://scholariq.org/topics/advanced-graph-neural-networks/)

## Paper topics

- [Advanced Graph Neural Networks](https://scholariq.org/topics/advanced-graph-neural-networks/)
- [Topic Modeling](https://scholariq.org/topics/topic-modeling/)
- [Recommender Systems and Techniques](https://scholariq.org/topics/recommender-systems-and-techniques/)

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Source: ScholarIQ — public research metadata, principally OpenAlex. See https://scholariq.org/sources/ for provenance and https://scholariq.org/methodology/ for what these figures mean.