For decades, machine learning has excelled at processing grid-like data, images, videos, audio, where information is neatly arranged in pixels or samples. But the world isn’t built on grids. It’s built on relationships. Social networks, molecular structures, knowledge graphs, financial transactions, these are all fundamentally relational datasets, where the connections between entities are as important as the entities themselves. Traditional neural networks struggle to capture these complex relationships. Enter Graph Neural Networks (GNNs), a rapidly evolving field poised to unlock insights from the vast, interconnected data that underpins much of modern life. GNNs don’t see pixels; they see connections.
GNNs represent a paradigm shift in machine learning, moving…