Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines

Summary: Lotan decouples graph processing from GNN training, recasting full-batch GNNs as query-plan dataflows over GraphX/PyTorch with plan rewriting, data-movement optimization, partitioning, and model batching. It outscales custom GNN systems while retaining competitive time-to-accuracy. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID: 13305
Venue: VLDB
Year: 2023
Pagerank: 5.833338e-05
Overall Rank: 6,588 | 54.81%
DOI: 10.14778/3611479.3611483

Incoming Non-self Citations Over Time

Authors

1. Yuhao Zhang (University of California San Diego)
2. Arun Kumar (University of California San Diego)

BibTeX Citation

@article{zhang_vldb23,
        title = {{Lotan: Bridging the Gap between GNNs and Scalable Graph Analytics Engines}},
        author = {Zhang, Yuhao and Kumar, Arun},
        journal = {PVLDB},
        series = {{VLDB} '23},
        volume = {16},
        number = {11},
        pages = {2728--2741},
        doi = {10.14778/3611479.3611483},
        url = {https://doi.org/10.14778/3611479.3611483},
        year = {2023}
}

Incoming Citations (Sorted by Pagerank)

Showing 6 of 6 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
8,504	Enabling Window-Based Monotonic Graph Analytics with Reusable Transitional Results for Pattern-Consistent Queries	2024	VLDB	5.4132367e-05
10,323	NeutronHeter: Optimizing Distributed Graph Neural Network Training for Heterogeneous Clusters	2026	SIGMOD	5.093636e-05
10,452	Enumerating Graph Pattern Matches with ML Oracles	2026	SIGMOD	5.093636e-05
10,835	NeutronTask: Scalable and Efficient Multi-GPU GNN Training with Task Parallelism	2025	VLDB	5.093636e-05
10,975	Faster Convergence in Mini-batch Graph Neural Networks Training with Pseudo Full Neighborhood Compensation	2025	VLDB	5.093636e-05
13,375	Reimagining Deep Learning Systems Through the Lens of Data Systems	2024	VLDB	-

Outgoing Citations (Sorted by Pagerank)

Showing 14 of 14 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	Pregel: A System for Large-Scale Graph Processing	2010	SIGMOD	0.0012250108
223	AliGraph: A Comprehensive Graph Neural Network Platform	2019	VLDB	0.00024182473
1,132	SANCUS: Staleness-Aware Communication-Avoiding Full-Graph Decentralized Training in Large-Scale Graph Neural Networks	2022	VLDB	0.00012041292
1,157	Cerebro: A Data System for Optimized Deep Learning Model Selection	2020	VLDB	0.00011924049
2,126	Kuzu* Graph Database Management System	2023	CIDR	9.1329991e-05
3,751	G3: When Graph Neural Networks Meet Parallel Graph Processing Systems on GPUs	2020	VLDB	7.1531889e-05
4,067	Distributed Deep Learning on Data Systems: A Comparative Analysis of Approaches	2021	VLDB	6.9293511e-05
4,323	TurboGraph++: A Scalable and Fast Graph Analytics System	2018	SIGMOD	6.7608401e-05
6,433	ALG: Fast and Accurate Active Learning Framework for Graph Convolutional Networks	2021	SIGMOD	5.8801048e-05
7,656	Nautilus: An Optimized System for Deep Transfer Learning over Evolving Training Datasets	2022	SIGMOD	5.5740571e-05
7,796	GraphScope: A One-Stop Large Graph Processing System	2021	VLDB	5.5427726e-05
8,979	Cerebro: A Layered Data Platform for Scalable Deep Learning	2021	CIDR	5.3399615e-05
9,371	Towards an Optimized GROUP BY Abstraction for Large-Scale Machine Learning	2021	VLDB	5.275595e-05
9,737	Saturn: An Optimized Data System for Multi-Large-Model Deep Learning Workloads	2024	VLDB	5.227679e-05

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