@aibutsimple: Graph Neural Networks (GNNs) extend neural networks to graph data. Graphs are found everywhere in our daily lives. Many real-world systems are naturally represented as graphs, where entities are connected through relationships. Examples include molecular structures, social networks, road networks, citation networks, and knowledge graphs. A graph consists of nodes (entities) and edges (relationships). They vary in size and structure and are constantly updating. So why can't we use a regular neural network? Traditional neural networks assume inputs have a fixed structure. Images are grids of pixels. Text is a sequence of tokens. GNNs allow us to work with this dynamic, non-fixed structure and leverage the features of the individual nodes and the connectivity between them. The core mechanism behind most GNNs is message passing. There are 3 main stages at each message pass (layer): - Gather: obtain information from its neighboring nodes. - Aggregate: combine that information, typically through summation, averaging, or another permutation-invariant operation. - Update: modify its own representation by combining the aggregated information with its current features through a neural network. Each additional layer expands the neighborhood reached from every node. One layer means immediate neighbors. Two layers means neighbors of neighbors. This iterative process allows GNNs to learn increasingly rich representations that encode both local and higher-order graph structure. The resulting node embeddings support a wide range of downstream tasks. - Node classification: Predict label of an individual node - Link prediction: Predict whether two nodes should be connected - Graph classification: Learn a representation of an entire graph to predict properties (e.g. molecular toxicity) In industry, GNNs are increasingly used in fraud detection, traffic forecasting, physical simulation, recommender systems, and computational biology. Surprisingly as well, GNNs are more similar to Convolutional Neural Networks (CNNs) than you think. CNNs aggregate information from neighboring pixels. GNNs aggregate information from neighboring nodes. The result is a framework for reasoning over relational data (graphs) that traditional neural networks cannot naturally represent. Some open research areas include graph transformers, geometric GNNs, oversmoothing, and many more. Want to read the full article? We write high-quality visual articles that make AI actually easy to understand. Join 10,000+ others learning AI intuitively. Link in bio. Follow @aibutsimple for more posts like these. #deeplearning #machinelearning #datascience #math #coding

To see more videos from user @aibutsimple, please go to the Tikwm homepage.