How deeply does Graph Neural Networks in Action cover inductive inference?

Explains the learned feature-to-embedding mapping and contrasts it with fixed-graph embeddings, but does not give an inductive implementation.

Inductive inference

As explained in Graph Neural Networks in Action

Inductive learning learns a generalizable function or representation from observed data for application to unseen data.

Inductive inference is useful when a graph may gain nodes or when a learned model must work on another graph. Instead of learning an embedding tied to each training node, a graph neural network (GNN) learns a function that maps node features to embeddings. It can apply that function to unseen nodes, including nodes in a new graph, without retraining—provided their feature spaces are similar. section 2.2.2 section 2.1.4

The practical distinction is what carries over after training:

A GNN uses node features and graph structure to produce embeddings. Message passing is its graph-learning mechanism. section 2.4 section 2.2.2
Node2Vec (N2V) learns embeddings for a fixed graph through random walks. It does not easily handle unseen nodes or a new graph without being reapplied, although inductive extensions exist. section 2.2 section 2.5

Choose the approach for the data and task, not by assuming one always wins. Inductive GNN embeddings suit changing graphs and tasks where node features matter. Structure-only, random-walk methods can perform better when labels are scarce or noisy; embeddings from either approach can also serve as features in a conventional classifier. section 2.2 section 2.5

How Graph Neural Networks in Action covers it

Cover of Graph Neural Networks in Action

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Also explained in

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Knowledge Graphs and LLMs in ActionInductive reasoning is the derivation of general patterns, conclusions, or predictions from observed cases, including cases with incomplete information.
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Sutskever's ListInductive inference is the process of inferring the process or hypothesis that could have generated observed data.

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Related concepts

transductive learningcontrasts with · Transductive learning infers representations or predictions for specific entities and structures observed during training rather than learning rules that generalize to unseen entities.
generalizationproduces · Generalization is the extension of a pattern, model, solution, or conclusion beyond the specific examples or cases from which it was derived.
multi-step reasoningkind of · Multi-step reasoning derives a conclusion, solution, or decision through multiple connected intermediate inferential or problem-solving steps.
deductive reasoningcontrasts with · Deductive reasoning is the process of deriving conclusions that necessarily follow from stated general premises, rules, or hypotheses.
machine learninguses · Machine learning is a field of computational methods in which systems learn patterns or relationships from data to make predictions, decisions, or other outputs.

This explainer was generated by Manning's knowledge graph from the text of Graph Neural Networks in Action. The section links point to the parts of the book it draws on.