René's URL Explorer Experiment


Title: GraphSAINT (Scalable GRL) · GitHub

Open Graph Title: GraphSAINT - Overview

X Title: GraphSAINT - Overview

Description: Goal: to improve the scalability of graph representation learning by algorithm and architecture innovations. - GraphSAINT

Open Graph Description: Goal: to improve the scalability of graph representation learning by algorithm and architecture innovations. - GraphSAINT

X Description: Goal: to improve the scalability of graph representation learning by algorithm and architecture innovations. - GraphSAINT

Mail addresses
zengh@usc.edu
hongkuaz@usc.edu

Opengraph URL: https://github.com/GraphSAINT

X: @github

direct link

Domain: patch-diff.githubusercontent.com

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GraphSAINThttps://patch-diff.githubusercontent.com/GraphSAINT/GraphSAINT
https://patch-diff.githubusercontent.com/GraphSAINT#graphsaint-graph-sampling-based-inductive-learning-method
Hanqing Zenghttps://sites.google.com/a/usc.edu/zengh/home
Hongkuan Zhouhttps://tedzhouhk.github.io/about/
Ajitesh Srivastavahttp://www-scf.usc.edu/~ajiteshs/
Viktor Prasannahttps://sites.usc.edu/prasanna/
https://patch-diff.githubusercontent.com/GraphSAINT#overview
https://patch-diff.githubusercontent.com/GraphSAINT/GraphSAINT/blob/master/overview_diagram.png
DropEdgehttps://arxiv.org/abs/1907.10903
hardware accelerator developmenthttps://dl.acm.org/doi/abs/10.1145/3373087.3375312
shaDow-GNNhttps://github.com/facebookresearch/shaDow_GNN
https://patch-diff.githubusercontent.com/GraphSAINT#news-check-out-our-new-work-that-generalizes-subgraph-sampling-to-both-the-training-and-inference-shadow-gnn-neurips21
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reference implementationhttps://pytorch-geometric.readthedocs.io/en/latest/modules/data.html#torch_geometric.data.GraphSAINTSampler
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