The Future is Big Graphs! A Community View on Graph Processing Systems
Not on the map. Why?
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?
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Dec 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 1; 2024: 1; 2025: 1.
5 papers.
- 2024Are Large Language Models the New Interface for Data Pipelines?cited by 13
- 2023Construction of Knowledge Graphs: State and Challengescited by 12
- 2025Common Foundations for SHACL, ShEx, and PG-Schemacited by 4
- 2021Graph-guided random forest for gene set selectioncited by 2
- 2022Integrating connection search in graph queriescited by 0
- 2025Common Foundations for SHACL, ShEx, and PG-Schema3 Feb 2025
- 2024Are Large Language Models the New Interface for Data Pipelines?6 Jun 2024
- 2023Construction of Knowledge Graphs: State and Challenges22 Feb 2023
- 2022Integrating connection search in graph queries9 Aug 2022
- 2021Graph-guided random forest for gene set selection26 Aug 2021