Trend Worth 📖: Why Graph Neural Networks Will Dominate in 2024 & Beyond
Traditional Deep Learning Models Excel at Grids, but Real World is Messy !
Traditional deep learning models like convolutional neural networks are great at processing grid-like data such as images. However, the real world is messy and filled with complex relationships and interconnected data that doesn’t fit into neat grids. These traditional models struggle to capture the intricate patterns, relationships and dependencies found in real-world data.
This is where graph neural networks come in. Graph neural networks are designed to model, analyze, and learn from data that has an underlying graph structure — with nodes representing entities and edges representing the relationships between them. By embracing this graph representation, graph neural networks can effectively understand and leverage the complex web of connections that exist in many real-world systems and datasets. From social networks to biology, transportation to finance, graph neural networks unlock new potential to gain insights from highly interconnected data.
That’s why I feel, graph neural networks are poised to dominate and drive innovation across industries in 2024 and beyond.
