# Graph-based deep learning for communication networks: A survey

**Type:** Papers  
**Canonical URL:** https://scholariq.org/papers/graph-based-deep-learning-for-communication-networks-a-survey/

## Facts

| Field | Value |
| --- | --- |
| Author Names | Weiwei Jiang |
| Citations | 278 |
| DOI | 10.1016/j.comcom.2021.12.015 |
| Fields | Computer Science,Physics and Astronomy |
| Open Access | true |
| OA Status | green |
| OA URL | https://arxiv.org/pdf/2106.02533 |
| OpenAlex ID | https://openalex.org/W3172408309 |
| Type | article |
| Year | 2021 |

## Paper authors

- [Weiwei Jiang](https://scholariq.org/researchers/weiwei-jiang/)

## 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/)
- [Complex Network Analysis Techniques](https://scholariq.org/topics/complex-network-analysis-techniques/)
- [Caching and Content Delivery](https://scholariq.org/topics/caching-and-content-delivery/)

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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.