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Graph Neural Networks (GNNs) are neural models that leverage explicit graph structure and relational context to aggregate features across nodes and edges. They are applied to tasks such as node classification, link prediction, and graph classification. GNNs entail model assumptions, scalability challenges, and overfitting trade-offs.
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Graph Neural Networks (GNNs) are neural models for data with a node-and-edge structure. They use neighborhood relations to learn representations for nodes, edges, or whole graphs and to make predictions from them.
GNNs emerged from the need to model relationships directly as graphs, for example in molecules, citation networks, or social networks. Conventional neural networks usually expect ordered inputs; graph data do not have such an order. The modern GNN family therefore brings together message-passing approaches that aggregate local neighborhood information step by step and was systematized as a distinct research area, for example in Zhou et al. 2018.
A GNN works like a multi-step exchange on the graph: first, each node holds its own features. Then neighbors send messages in each layer, which are collected and combined with the current features. With every round, the node context expands by one more hop. For graph-level prediction, a readout or pooling stage compresses many node representations into one fixed-size representation.
GNNs are a specialized architecture within neural networks, with layers, connections, and activation functions.
Nodes exchange information with their neighbors and update their states step by step.
The order of nodes must not artificially change the result; reorderings are mapped consistently.
Each node receives a learned feature vector that summarizes local and contextual information.
Multiple node vectors are compressed into a compact representation for graph-level tasks.
Nodes, edges, and their relations form the input structure on which the model learns.
GNNs are useful when relationships themselves carry the signal, such as in fraud detection, recommender systems, knowledge graphs, or molecule and materials modeling. They are especially suited to irregular structures, but they require careful graph construction and can become compute- and memory-intensive on large graphs. Standard message-passing models also face expressive limits for some structural distinctions.
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