What are the advantages of graph neural networks?

What are the advantages of graph neural networks?

Graph neural networks (GNNs) excel in capturing relationships in data structured as graphs, making them powerful for tasks like social network analysis and recommendation systems.

Can you give me some specific applications where GNNs are particularly useful?

Sure! GNNs are used in social network analysis, molecular chemistry for drug discovery, and traffic prediction in smart cities.

How do GNNs compare to traditional neural networks?

Unlike traditional neural networks that process data in fixed structures, GNNs can handle variable-sized graphs, making them more flexible for relational data.

What challenges do GNNs face?

Some challenges include scalability to large graphs, over-smoothing of node features, and the need for labeled data for supervised learning.