Advanced architectures and techniques
From advanced transformer architectures to cutting-edge generative models and innovative reinforcement learning strategies, graph learning demonstrates immense potential across a diverse set of tasks.
Graph transformers and attention mechanisms
The success of transformer architectures in natural language processing (NLP), which we looked at in Chapter 8, is inspiring new approaches in graph learning.
Adapting transformer architectures for graph data
Researchers are modifying transformer models so that they work effectively with graph-structured data. This allows long-range dependencies and global context to be captured in graphs. Graph transformer networks (GTNs) adapt the self-attention mechanism to operate on graph-structured data, enabling the model to learn complex relationships between nodes. These models can dynamically adjust the graph structure during the learning process, potentially discovering hidden relationships that are...
