PyTorch Geometric
PyTorch Geometric is an open-source Python library that extends PyTorch with data structures and operators for graph and geometric machine learning.
Graph Neural Networks in Action
PyTorch Geometric is an open-source Python library for developing and training graph neural networks with PyTorch.
- How it is used
- Supports preparing graph data and implementing graph neural networks with PyTorch.
- How deep it goes
- Works through graph conversion, tensors, layers, masks, and aggregation choices; leaves larger-than-single-machine handling to separate strategies.
- Examples
- Uses a converted NetworkX graph, a two-layer embedding model, aggregation comparisons, and masked node classification.
- What you can do after
- Apply it via PyTorch Geometric to prepare graph tensors and train graph neural networks.
Read how Graph Neural Networks in Action explains PyTorch Geometric →
Knowledge Graphs and LLMs in Action
PyTorch Geometric is an open-source library that provides data structures and neural-network layers for geometric and graph machine learning with PyTorch.
- How it is used
- Builds PyTorch-based GNN pipelines for node classification and link prediction on homogeneous and heterogeneous graphs.
- How deep it goes
- Explains Data construction, masking, dataset partitioning, embeddings, heterogeneous conversion, and encoder–decoder implementation.
- Examples
- Uses an Elliptic financial transaction graph and a MovieLens user–movie rating graph as implementation examples.
- What you can do after
- Build it from scratch by preparing PyG graph objects, defining masks, and assembling homogeneous or heterogeneous GNN models.
Read how Knowledge Graphs and LLMs in Action explains PyTorch Geometric →
Related concepts
- graph neural networkuses · A graph neural network is a neural-network model that learns representations or predictions from graph-structured data by combining information from connected entities.
- PyTorchuses · PyTorch is an open-source machine-learning framework for Python that provides tensor computation, automatic differentiation, and tools for building and training neural networks.
- edge indexuses · An edge index is a tensor-based representation that records graph edges as pairs of source and destination node indices.
- remote backenduses · A remote backend is a graph-data storage and retrieval mechanism that keeps data in an external backend and fetches computational subsets as needed.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.