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)

Paper ID: 6011
Venue: SIGMOD
Year: 2021
Pagerank: 4.3845844e-05
Overall Rank: 9,172 | 36.20%
DOI: 10.1145/3448016.3450573

Incoming Non-self Citations Over Time

Authors

1. Advitya Gemawat

Incoming Citations (Sorted by Pagerank)

Showing 2 of 2 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
6,985	CompressGraph: Efficient Parallel Graph Analytics with Rule-Based Compression	2023	SIGMOD	4.8729387e-05
10,161	Enabling Efficient Direct Update on Rule-Based Compressed Graph	2026	SIGMOD	4.1945683e-05

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
2,886	VISTA: Optimized System for Declarative Feature Transfer from Deep CNNs at Scale	2020	SIGMOD	7.9612767e-05
8,864	Cerebro: A Layered Data Platform for Scalable Deep Learning	2021	CIDR	4.4326439e-05

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