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SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization

Summary: SCARA introduces feature-oriented optimization to scale GNNs by reusing computed features instead of purely node-centric propagation. It offers sub-linear propagation with guaranteed precision, delivering up to 100x speedups on billion-scale Papers100M. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID
12992
Venue
VLDB
Year
2022
Pagerank
6.0248475e-05
Overall Rank
5,968 | 59.06%
DOI
10.14778/3551793.3551866

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{liao_vldb22,
        title = {{SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization}},
        author = {Liao, Ningyi and Mo, Dingheng and Luo, Siqiang and Li, Xiang and Yin, Pengcheng},
        journal = {PVLDB},
        series = {{VLDB} '22},
        volume = {15},
        number = {11},
        pages = {3240--3248},
        doi = {10.14778/3551793.3551866},
        url = {https://doi.org/10.14778/3551793.3551866},
        year = {2022}
}

Incoming Citations (Sorted by Pagerank)

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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
2,173 Unifying the Global and Local Approaches: An Efficient Power Iteration with Forward Push 2021 SIGMOD 9.0375634e-05
3,896 Scaling Attributed Network Embedding to Massive Graphs 2021 VLDB 7.0406415e-05
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