🕸️ Graph Neural Networks
Learning from interconnected data with GNNs
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Learning from Graph-Structured Data
Graph Neural Networks (GNNs) are deep learning models designed to work with graph-structured data—networks of nodes connected by edges. Unlike images (grids) or text (sequences), graphs capture complex relationships and interactions between entities.
GNNs learn by passing messages between connected nodes, allowing each node to aggregate information from its neighborhood. This makes them perfect for social networks, molecular structures, knowledge graphs, and recommendation systems.
Non-Euclidean Data
Graphs represent relationships that don't fit into regular grids or sequences, capturing the true structure of interconnected data.
Message Passing
Nodes exchange information with their neighbors through iterative message passing, building rich representations.
Permutation Invariant
GNNs produce the same output regardless of node ordering, respecting the symmetric nature of graphs.
Inductive Learning
Can generalize to unseen nodes and graphs, learning the underlying patterns rather than memorizing structure.
Why GNNs?
Traditional neural networks struggle with graph data because they require fixed input sizes and don't capture relational structure. GNNs are specifically designed to handle variable-sized graphs and leverage connectivity patterns for better predictions.