# The Future is Big Graphs! A Community View on Graph Processing Systems

arXiv: https://arxiv.org/abs/2012.06171 · HTML: https://mlsanity.com/paper/2012.06171/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2020.

## Built on by (most cited first, 5 of 5)

- [Are Large Language Models the New Interface for Data Pipelines?](https://mlsanity.com/paper/2406.06596/) (2024)
- [Construction of Knowledge Graphs: State and Challenges](https://mlsanity.com/paper/2302.11509/) (2023)
- [Common Foundations for SHACL, ShEx, and PG-Schema](https://mlsanity.com/paper/2502.01295/) (2025)
- [Graph-guided random forest for gene set selection](https://mlsanity.com/paper/2108.11674/) (2021)
- [Integrating connection search in graph queries](https://mlsanity.com/paper/2208.04802/) (2022)

## Abstract (arXiv)

Graphs are by nature unifying abstractions that can leverage interconnectedness to represent, explore, predict, and explain real- and digital-world phenomena. Although real users and consumers of graph instances and graph workloads understand these abstractions, future problems will require new abstractions and systems. What needs to happen in the next decade for big graph processing to continue to succeed?