# The World of Graph Databases from An Industry Perspective

arXiv: https://arxiv.org/abs/2211.13170 · HTML: https://mlsanity.com/paper/2211.13170/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Nov 2022.

## Built on by (most cited first, 7 of 7)

- [OpenFGL: A Comprehensive Benchmark for Federated Graph Learning](https://mlsanity.com/paper/2408.16288/) (2024)
- [Top Ten Challenges Towards Agentic Neural Graph Databases](https://mlsanity.com/paper/2501.14224/) (2025)
- [Construction of Knowledge Graphs: State and Challenges](https://mlsanity.com/paper/2302.11509/) (2023)
- [Knowledge Graphs Querying](https://mlsanity.com/paper/2305.14485/) (2023)
- [D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural Networks](https://mlsanity.com/paper/2409.09079/) (2024)
- [IRSKG: Unified Intrusion Response System Knowledge Graph Ontology for Cyber Defense](https://mlsanity.com/paper/2411.15672/) (2024)
- [From SQL to Knowledge Graphs: An LLM-Driven Multi-Agent Approach with Data Schema Improvement](https://mlsanity.com/paper/2608.26117/) (2026)

## Abstract (arXiv)

Rapidly growing social networks and other graph data have created a high demand for graph technologies in the market. A plethora of graph databases, systems, and solutions have emerged, as a result. On the other hand, graph has long been a well studied area in the database research community. Despite the numerous surveys on various graph research topics, there is a lack of survey on graph technologies from an industry perspective. The purpose of this paper is to provide the research community with an industrial perspective on the graph database landscape, so that graph researcher can better understand the industry trend and the challenges that the industry is facing, and work on solutions to help address these problems.