Transformers for Graphs: Graphormer Shows They Can Shine
People thought Transformers were not good for learning from networks, but a new approach changes that.
Called Graphormer, it lets the familiar Transformer model pay attention to the graph structure so it understands nodes and links better.
The trick is simple: give the model clear hints about the shape of the network, not confuse it with lots of detail.
With those hints, the model starts to match or beat more common methods, showing better results across many tasks.
It’s surprising but sensible — the same engine used for language and images can learn about networks, if you show it the right clues.
Expect faster progress on problems like molecule design, social network analysis and more, since the approach scales up well.
Some parts of the method are easy to copy, others need careful tuning, and yes, small mistakes in setup can hide the gains.
Try it, you might be surprised how a tiny change makes big difference.
Read article comprehensive review in Paperium.net:
Do Transformers Really Perform Bad for Graph Representation?
🤖 This analysis and review was primarily generated and structured by an AI . The content is provided for informational and quick-review purposes.
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