Graph Neural Network Expert

Designs and implements graph neural network architectures for relational and structured data problems. Covers GCN, GAT, GraphSAGE, knowledge graphs, message passing, and graph-level tasks including node classification, link prediction, and graph generation.

Gold

v1.0.00 activationsAI & Machine LearningTechnologyexpert

SupaScore

84.1

Research Quality (15%)

8.6

Prompt Engineering (25%)

8.5

Practical Utility (15%)

8.2

Completeness (10%)

8.3

User Satisfaction (20%)

8.3

Decision Usefulness (15%)

8.5

Best for

▸Design graph neural networks for molecular property prediction and drug discovery
▸Build recommendation systems using user-item interaction graphs with GCN/GAT
▸Implement knowledge graph reasoning systems for question answering
▸Create social network analysis pipelines with node classification and link prediction
▸Develop fraud detection systems using heterogeneous transaction graphs

What you'll get

●Architecture comparison matrix showing GCN vs GAT vs GraphSAGE trade-offs for specific graph properties like homophily, scale, and heterogeneity
●PyTorch Geometric code structure with custom message passing functions, loss design, and evaluation metrics for the specific graph task
●End-to-end pipeline design covering graph preprocessing, feature engineering, model architecture, training strategy, and production deployment considerations

Not designed for ↓

×Traditional tabular data machine learning (use standard ML approaches instead)
×Text processing without explicit graph structure (use NLP transformers)
×Time series forecasting without graph relationships
×Computer vision tasks on regular image grids (use CNNs)

Expects

Graph-structured data description including node types, edge relationships, available features, scale, and specific graph learning task requirements.

Returns

Complete GNN architecture recommendations with model selection rationale, PyTorch Geometric implementation guidance, and evaluation strategies tailored to the graph problem.

Evidence Policy

Enabled: this skill cites sources and distinguishes evidence from opinion.

graph-neural-networksgnngcngatgraphsageknowledge-graphsmessage-passingnode-classificationlink-predictiongraph-learningrelational-data

Research Foundation: 8 sources (6 paper, 2 official docs)

This skill was developed through independent research and synthesis. SupaSkills is not affiliated with or endorsed by any cited author or organisation.

Version History

v1.0.02/15/2026

Initial version

Works well with

Feature Engineering StrategistGold Knowledge Graph ArchitectGold PyTorch Deep Learning EngineerPlatinum Recommendation System DesignerPlatinum

Need more depth?

Specialist skills that go deeper in areas this skill touches.

PyTorch Deep Learning EngineerPlatinum Neural Network DebuggerPlatinum ML Model Evaluation ExpertGold

Common Workflows

Graph ML Production Pipeline

Complete workflow from graph problem formulation through architecture design, feature engineering, evaluation, and production deployment

graph-neural-network-expert→Feature Engineering Strategist→ML Model Evaluation Expert→model-deployment-optimizer

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