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Paper: Do Transformers Really Perform Bad for Graph Representation?
In this video, I cover Graphormer a new transformer model that achieved SOTA results on the OGB large-scale challenge benchmark.
It achieved that by introducing 3 novel types of structural biases/encodings into the classic transformer encoder.
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✅ Paper: https://arxiv.org/abs/2106.05234
✅ What can GNNs learn blog: https://andreasloukas.blog/2019/12/27...
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⌚️ Timetable:
00:00 Key points of the paper
02:15 Graph-level predictions and why we need structural information
05:35 GNNs basics
09:40 Centrality encoding explained in depth
14:15 Spatial encoding explained in depth
18:00 Edge encoding explained in depth
22:30 Results on OGB LSC
23:30 Graphormer handles over-smoothing
25:10 Other results and ablation study
28:20 Graphormer is more expressive than WL
30:30 Mean aggregation as a special case of Graphormer
35:00 Sum aggregation as a special case of Graphormer
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#graphormer #graphs #transformers