Torch Geometric is used in Python projects. Graph Neural Network Library for PyTorch It has 9 direct runtime dependencies. Check its dependency graph on PyDeps to understand the full transitive dependency tree, reverse dependents, known CVEs, and license compatibility before installing.
Graph Neural Network Library for PyTorch
torch-geometric declares 9 direct runtime dependencies on PyPI. Each one is resolved into the full dependency tree below:
Beyond its direct dependencies, torch-geometric can pull in further packages through its dependency tree. PyDeps resolves the entire chain from PyPI and deps.dev so you can see every transitive (nested) dependency of torch-geometric, expand any node on demand, and understand the full set of code that ships when you run pip install torch-geometric.
PyDeps checks torch-geometric and every package in its dependency tree against the OSV vulnerability database in real time. For each CVE you can see the severity, the affected version ranges, and the first fixed version, so you know exactly which torch-geometric version is safe to install before you ship.
torch-geometric is distributed under the MIT license. PyDeps also shows the license of every dependency in the tree so you can audit license compatibility across your whole torch-geometric install, not just the top-level package.
Install from PyPI with pip install torch-geometric. For offline or air-gapped environments, PyDeps can download torch-geometric together with every resolved dependency as wheel files in a single bundle, matched to your target Python version and operating system.
Switch to the dependents view to see the reverse dependencies of torch-geometric — the PyPI packages that list torch-geometric as a requirement. Reverse dependencies are a strong signal of how widely a package is trusted and how disruptive a breaking change would be.