# GNNs (Graph Neural Networks) made easy with PyTorch Geometric

*Traditional models like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) have been highly successful for structured data types — images, text, and tabular data. However, many real-world datasets are better represented as graphs, where entities (nodes) are connected through relationships (edges). For instance, social networks, molecular structures, and e-commerce interactions can all be modeled as graphs. This is where **Graph Neural Networks (GNNs)** shine, as they allow deep learning to extend beyond fixed grids and sequences into more complex, interconnected data structures.*

# Why Graph Neural Networks?

GNNs help solve problems where the relationships between entities matter. Consider a few use cases:

**Social Networks**: Detect fake accounts by analyzing the interactions between users.**E-commerce**: Represent product-user interactions as a graph to offer personalized recommendations.**Molecular Graphs**: Analyze the structure of chemical compounds to predict properties such as toxicity.

By capturing both the attributes of nodes and the structure of the graph, GNNs provide a more comprehensive understanding of the data compared to traditional models.