Emerging trends and directions
The new trends in graph learning reflect both the growing capabilities of graph-based models and the expanding range of applications where they’re being deployed. From advances in model architectures to novel training techniques, the following developments are at the forefront of graph learning research and practice.
Scalability and efficiency
As we saw in Chapter 5, the ability to handle increasingly large and complex graphs is becoming a crucial challenge as data volumes grow exponentially. Researchers are developing innovative approaches to tackle this challenge.
Handling larger and more complex graphs
New algorithms are being designed to process graphs with billions of nodes and edges efficiently (for more details on node- and edge-level learning, please refer to Chapter 2). These methods often leverage the sparsity and locality properties of real-world graphs. For example, sampling-based approaches such as GraphSAGE (see Chapter...
