3 repositorios
Awesome GitHub RepositoriesLarge-Scale Graph Training
Training neural networks on graphs with billions of edges using distributed systems and sampling.
Distinct from Large-Scale Training Frameworks: Specifically targets the scaling of graph-structured data training, rather than general LLM or cluster orchestration.
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Awesome Large-Scale Graph Training GitHub Repositories
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- rusty1s/pytorch_geometric
rusty1s/pytorch_geometric
23,848Ver en GitHubPyTorch Geometric is a library for building and training machine learning models on graph-structured data. It provides a framework for developing graph neural networks, including a specialized system for implementing node-to-node information exchange via customizable message passing, aggregation, and update functions. The library includes a sparse data processing toolkit that utilizes accelerated CPU and CUDA kernels to perform efficient reductions on large sparse datasets. It supports the creation of specialized architectures for structured data such as 3D meshes and point clouds. The proje
Runs learning processes on massive datasets with millions of nodes or complex heterogeneous structures.
Python - dmlc/dgl
dmlc/dgl
14,283Ver en GitHubDGL is a Python library for building and training graph neural networks. It functions as a graph message passing framework and a geometric deep learning tool, enabling the development of models that analyze graph-structured data. The library is designed for large-scale graph processing, utilizing distributed training and neighbor sampling to handle datasets with billions of edges. It provides specialized support for heterogeneous graph modeling, allowing for the representation of complex real-world entities with multiple node and edge types. Its capabilities cover a wide range of graph tasks
Provides distributed training and neighbor sampling to handle graphs with billions of edges.
Pythondeep-learninggraph-neural-networks - williamleif/graphsage
williamleif/GraphSAGE
3,657Ver en GitHubGraphSAGE is a graph neural network framework designed for inductive representation learning on large-scale graphs. It functions as an inductive graph embedding tool and neighborhood aggregation engine, enabling the generation of numerical node representations that generalize to previously unseen data. The system distinguishes itself by computing node embeddings through the aggregation of features from local neighborhoods rather than relying on a global lookup table. This approach allows the framework to operate as both a supervised graph classifier for predicting categorical node classes and
Scales graph training to massive datasets by aggregating information from local node neighborhoods.
Python
