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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →Which graph neural network (GNN) library should you use? Start with the deep-learning framework and version already in your project, then check the library’s current compatibility and whether its tools fit your graph structure and training workflow. PyTorch Geometric is a natural candidate for PyTorch projects; TensorFlow GNN and Spektral are TensorFlow-oriented; and DGL describes support for several frameworks. The seven options below are not interchangeable graph databases, and the available evidence does not support a universal “best” or fastest choice.
Compare the seven libraries at a glance
| Library | Framework fit | What the available documentation or paper establishes | Evidence to check before adopting |
|---|---|---|---|
| PyTorch Geometric (PyG) | Built on PyTorch | Graph and other irregular-structure learning; batching many small graphs and a single large graph; multi-GPU support; datasets and transforms; meshes and point clouds; documentation on sampling, distributed training, and compiled GNN topics. PyG documentation | Installation requirements for your environment and the specific layers and workflows you need. |
| Deep Graph Library (DGL) | Describes support for PyTorch, TensorFlow, and Apache MXNet | Graph operations and message passing, multi-GPU and distributed training; domain projects include DGL-KE and DGL-LifeSci. DGL project site | Verify the backend and release against your framework version; a listed framework is not a guarantee of compatibility with every version. |
| TensorFlow GNN (TF-GNN) | TensorFlow-oriented | GraphTensor, heterogeneous schemas, graph preparation, subgraph sampling, model layers, and training orchestration; sampling guidance covers in-memory and Apache Beam-based distributed workflows. TF-GNN guide | The repository describes release 1.0 as requiring TensorFlow 2.12 or later and Keras v2; for TensorFlow 2.16+, it describes installing tf-keras and setting TF_USE_LEGACY_KERAS=1. Check current requirements. TF-GNN repository |
| Spektral | TensorFlow and Keras | A paper describes message-passing and pooling operators, graph processing, and benchmark dataset loaders, with use cases ranging from prototyping to more experienced practice. Spektral paper | Current release status and framework compatibility were not established by the cited paper. |
| Jraph | Not established by the cited source | Named among graph-learning libraries in the CogDL paper’s related-work discussion. CogDL paper | Investigate current primary documentation for features, maintenance, and compatibility. |
| Graph Nets | Not established by the cited source | Named among graph-library projects in the CogDL paper’s discussion. CogDL paper | Verify current documentation, support, and maintenance before choosing it. |
| CogDL | Not established by the cited source | The paper presents a graph deep-learning library oriented toward graph representation learning, with model implementations, training and evaluation APIs, and reproducible benchmark configurations. Its discussion characterizes PyG and DGL as well-known libraries in 2023, not as a current comparative ranking. CogDL paper | Check the current project documentation and support for the exact task and environment. |
Choose by the framework already in your project
Framework fit is a practical first filter: changing the surrounding stack or resolving version conflicts can matter more than a feature that sounds attractive on a project page.
- Using PyTorch: PyG is built on PyTorch, while DGL describes a multi-framework design that includes PyTorch. Check each project’s installation steps and supported versions for your hardware and Python environment. PyG documentation · DGL project site
- Using TensorFlow or Keras: TF-GNN and Spektral are candidates to investigate. For TF-GNN, account for the repository’s release-specific TensorFlow and Keras guidance; for Spektral, the cited paper does not establish current compatibility. TF-GNN repository · Spektral paper
- Considering MXNet or multiple backends: DGL lists Apache MXNet alongside PyTorch and TensorFlow. Confirm that the backend and release you need remain supported for your intended setup. DGL project site
Match the library to the graph and training workload
Heterogeneous graphs
If nodes or relationships have several types, TF-GNN explicitly documents heterogeneous schemas with multiple node and edge types. Validate how a candidate represents your actual schema; do not assume the other libraries expose identical abstractions. TF-GNN guide
Many small graphs or one large graph
PyG documents mini-batch loaders for both many small graphs and a single large graph, as well as sampling and distributed-training topics. DGL highlights multi-GPU and distributed training. TF-GNN describes both in-memory and Apache Beam-based distributed subgraph sampling. These are different workflow capabilities, not evidence that one library will be faster for your data. PyG documentation · DGL project site · TF-GNN guide
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What is and is not established about current support
The available material is uneven in age and detail: Spektral is represented by a 2020 paper, CogDL by a 2023 paper, and Jraph and Graph Nets only by a later paper’s related-work list. Those references can identify candidates, but they do not establish a comparable current maintenance or compatibility picture across all seven.
Before adopting any option, inspect its current release notes, supported Python and framework versions, platform requirements, and open issues. For TF-GNN in particular, treat the TensorFlow and Keras details above as guidance tied to the repository’s described release, not a guarantee for later releases. No performance ranking follows from the features listed here; benchmark your own representative workload if speed is decisive.
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