concept#Machine Learning#Data#Analytics#Platform

Graph Neural Networks (GNNs)

Neural network architectures for processing and analyzing graph-structured data via relational message passing.

Graph Neural Networks (GNNs) are neural models that leverage explicit graph structure and relational context to aggregate features across nodes and edges.

Maturity

Emerging

Cognitive loadHigh

360° overview

Graph Neural Networks (GNNs)

Six perspectives place the building block in context. The numbers show where each perspective continues in the reading path.

The building block at a glance

Graph Neural Networks (GNNs)

360°

Classification

2

Complexity: High
Impact area: Technical

Technical context

2

Integrations

Feature stores and feature serving

+2

Principles & goals

1

Leverage relational context explicitly as an inductive bias.

Value stream stage: Build

Use cases & scenarios

4

Use cases: 3
Scenarios: 3

Compromises

3

Risks: 3
Best practices: 3

I/O & resources

4

Inputs: 3
Outputs: 3