GNN Architect-GNN Code Generation Tool

Example

Given a citation network dataset, GNN Architect helps choose an appropriate model like Graph Convolutional Network (GCN) for node classification tasks.

Scenario

A research team working on identifying influential authors in citation networks can use GNN Architect to determine and implement a GCN model best suited for this application.

Example

GNN Architect provides suggestions on the number of layers, hidden units per layer, and activation functions, optimizing architectures for the graph classification task.

Scenario

In a healthcare scenario, when diagnosing diseases from patient data represented as graphs, the user receives optimized architecture parameters from GNN Architect for the best performance.

Example

Guides users in converting structured or semi-structured data into graph data suitable for training and testing in GNNs.

Scenario

An online retailer, trying to understand customer purchasing behavior, is instructed to convert transaction data into a graph format to accurately represent product relationships.

Example

Provides ready-to-use PyTorch Geometric code snippets to build and train models.

Scenario

In materials science, where predicting material properties involves complex relationships, GNN Architect provides sample code to train a graph neural network on atomic data.

Example

Offers guidance on implementing cross-validation, early stopping, and various evaluation metrics.

Scenario

A financial institution assessing credit risk can follow GNN Architect's advice on testing and evaluation strategies to accurately gauge model performance.