torch-geometric-graph-neural-networks
CommunityUnlock insights from graph data with GNNs.
Data & Analytics#deep learning#graph neural networks#gnn#pytorch geometric#relational data#node classification#graph classification
Authorjaechang-hits
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill enables the analysis and modeling of complex relational data, such as social networks, molecular structures, and recommendation systems, by leveraging the power of Graph Neural Networks (GNNs).
Core Features & Use Cases
- Node Classification: Predict labels for individual nodes in a graph (e.g., user categorization).
- Graph Classification: Predict a label for an entire graph (e.g., molecular property prediction).
- Link Prediction: Predict the existence of edges between nodes (e.g., recommending connections).
- Heterogeneous Graphs: Handles graphs with multiple types of nodes and edges.
- Use Case: Predict the toxicity of a molecule by training a GNN on a dataset of known molecular structures and their properties.
Quick Start
Use the torch-geometric-graph-neural-networks skill to perform node classification on the Cora dataset.
Dependency Matrix
Required Modules
torchtorch_geometric
Components
references
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: torch-geometric-graph-neural-networks Download link: https://github.com/jaechang-hits/SciAgent-Skills/archive/main.zip#torch-geometric-graph-neural-networks Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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