Graph Sampling for GNNs: A Tutorial

I skimmed through the main papers in Graph Neural Network (GNN) sampling with a bit of history on graph sampling. At the end, I went through a visual guide on how to do GNN sampling in pytorch geometric.
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🔗 notes+code: https://mashaan14.github.io/YouTube-channel/graph_neural_networks/2024_05_27_GNN_sampling
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📹 Video edit: Adobe Premiere Rush
🎧 Audio enhancement: Adobe Podcast
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Chapters:
00:00 start
00:15 I was confused in the beginning
01:22 sampling on graphs
01:42 History: Jarvis-Patrick paper
04:10 History: spectral clustering
08:54 Our research on graphs
12:11 why do need sampling in GNN?
14:59 GraphSAGE paper
16:25 FastGCN paper
17:05 LADIES paper
18:16 ClusterGCN paper
19:38 a problem that might occur in ClusterGCN
20:38 GraphSAINT paper
21:48 start of the code
24:21 GraphSAGE in pytorch-geometric
28:18 ClusterGCN in pytorch-geometric
33:12 GraphSAINT in pytorch-geometric
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#graphneuralnetwork #GNN #GCN #graphconvolution #GAT #attention #convolution #jraph #graph #GCN #pytorchgeometric #GCNConv #GATConv #NeighborLoader #ClusterLoader #GraphSAGE #ClusterGCN #GraphSAINT #DeepLearningTutorial #MachineLearningProject #AIResearch #CodingTutorial