Node
In artificial intelligence, a node is the fundamental computational unit inside a structure such as a neural network or a graph. In neural networks, each node represents an artificial neuron that receives inputs, applies a mathematical transformation (usually a weighted sum plus an activation function), and produces an output passed to subsequent layers.
Role in neural networks
Nodes work together in layers:
- Input nodes capture raw data (pixels, words, numerical values).
- Hidden nodes perform transformations, progressively extracting more abstract features.
- Output nodes deliver predictions, such as a class label or a probability distribution.
Beyond neural networks
Nodes are also central in graph theory, where they represent entities (users in a social network, cities in a transport map, proteins in biology). AI models like Graph Neural Networks (GNNs) leverage this representation to learn patterns on relational data.
Why nodes matter
Understanding nodes helps to demystify how machine learning systems operate: each decision or prediction is not magic but the result of thousands—or millions—of these small computational units working in concert.
In artificial intelligence, a node is not just a computational unit—it is a point of representation and interaction. In neural networks, each node can be thought of as a miniature processing element: it aggregates inputs (often weighted), applies an activation function, and sends the result onward. The collective dynamics of thousands or millions of nodes give neural networks their expressive power.
Nodes also play a central role in graph-based learning, where they act as anchors of meaning. In social networks, a node might represent a person; in biology, a protein; in logistics, a city in a transport network. Graph Neural Networks (GNNs) learn by propagating information across nodes and their connections, enabling algorithms to capture relational context rather than just isolated features.
From a practical perspective, nodes are the building blocks of modern AI: they are the “atoms” that carry, transform, and connect information. Whether as neurons in deep learning or as entities in a graph, they embody the duality of computation and representation, bridging mathematics and real-world structure.
