GraphGem: Optimized Scalable System for Graph Convolutional Networks
Summary: GraphGem is an optimized, scalable end-to-end system for Graph Convolutional Networks, addressing memory blow-ups, latency, and I/O. Uses declarative inputs and DB/ML-inspired techniques to accelerate GCNs and align data management with DL pipelines. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Advitya Gemawat (University of California San Diego)
BibTeX Citation
@inproceedings{gemawat_sigmod21,
title = {{GraphGem: Optimized Scalable System for Graph Convolutional Networks}},
author = {Gemawat, Advitya},
series = {{SIGMOD} '21},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3448016.3450573},
url = {https://dl.acm.org/doi/10.1145/3448016.3450573},
year = {2021}
}Incoming Citations (Sorted by Pagerank)
Showing 2 of 2 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 6,526 | CompressGraph: Efficient Parallel Graph Analytics with Rule-Based Compression | 2023 | SIGMOD | 5.7559739e-05 |
| 10,638 | Enabling Efficient Direct Update on Rule-Based Compressed Graph | 2026 | SIGMOD | 4.9793485e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 2 of 2 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 3,630 | VISTA: Optimized System for Declarative Feature Transfer from Deep CNNs at Scale | 2020 | SIGMOD | 7.1496182e-05 |
| 9,144 | Cerebro: A Layered Data Platform for Scalable Deep Learning | 2021 | CIDR | 5.2201471e-05 |
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