Systems for Scalable Graph Analytics and Machine Learning: Trends and Methods
Summary: Tutorial surveys graph-parallel systems beyond Pregel-style vertex-centric iteration, emphasizing task-centric execution for expensive subgraph search and scalable GNN training. It categorizes design techniques enabling heterogeneous, composable graph analytics pipelines. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Da Yan (Indiana University)
- 2. Lyuheng Yuan (Indiana University)
- 3. Akhlaque Ahmad (Indiana University)
- 4. Saugat Adhikari (Indiana University)
BibTeX Citation
@article{yan_vldb25,
title = {{Systems for Scalable Graph Analytics and Machine Learning: Trends and Methods}},
author = {Yan, Da and Yuan, Lyuheng and Ahmad, Akhlaque and Adhikari, Saugat},
journal = {PVLDB},
series = {{VLDB} '25},
volume = {18},
number = {12},
pages = {5460--5465},
doi = {10.14778/3750601.3750695},
url = {https://doi.org/10.14778/3750601.3750695},
year = {2025}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 9,309 | Revisiting Graph Analytics Benchmark | 2025 | SIGMOD | 5.1969334e-05 |
| 9,804 | Property Graph Standards: State of the Art & Open Challenges | 2025 | VLDB | 5.1257999e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 21 of 21 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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