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StoGCN

1 paper tagged archive 2025-07-28

Introduced by Jianfei Chen et al. in Stochastic Training of Graph Convolutional Networks with Variance Reduction

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

StoGCN is a control variate based algorithm which allow sampling an arbitrarily small neighbor size. Presents new theoretical guarantee for the algorithms to converge to a local optimum of GCN.

Paper Source

Papers archive 2025-07-28

1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Stochastic Training of Graph Convolutional Networks with Variance Reduction
29 Oct 2017 · 2 repositories · arXiv:1710.10568

Syntology ran 6 of 11 samples · 5 unverified

Tasks archive 2025-07-28

The archive attaches no task to a paper tagged with this method.

Usage over time archive 2025-07-28

Papers per year the archive tags with this method, by the paper's archive date (1 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Graph Models

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