Care sunt principalele funcționalități ale alibaba/graph-learn?

Principalele funcționalități ale alibaba/graph-learn sunt: Graph Neural Networks, Neighborhood Sampling, Distributed Graph Engines, Graph Neural Network Training Routines, Graph Neighborhood Sampling, Distributed Graph Storage, Graph-Augmented Inference, Real-Time Prediction Serving.

Care sunt câteva alternative open-source pentru alibaba/graph-learn?

Alternativele open-source pentru alibaba/graph-learn includ: mdeff/cnn_graph — Cnn_graph is a graph convolutional network framework and graph signal processing library designed for machine learning… rusty1s/pytorch_geometric — PyTorch Geometric is a library for building and training machine learning models on graph-structured data. It provides… thinkaurelius/titan — Titan is a distributed graph database and computing engine designed for storing and querying massive datasets of… dmlc/dgl — DGL is a Python library for building and training graph neural networks. It functions as a graph message passing… angel-ml/angel — Angel is a distributed machine learning framework and graph computation engine designed to train predictive models and… tkipf/pygcn — pygcn is a PyTorch library and framework for implementing graph convolutional networks. It provides tools for…

alibabagraph-learn

Name: alibaba/graph-learn
Author: alibaba

View on GitHub

1,341 stele266 fork-uriC++Apache-2.012 vizualizări

Graph Learn

Graph-learn is a distributed graph processing engine and graph neural network framework designed for large-scale graph data. It provides specialized query interfaces to extract training subgraphs and node neighborhoods, enabling the construction and training of complex graph neural network models on massive datasets. The system integrates a real-time inference server to serve live predictions by sampling dynamic graphs with low latency while processing streaming graph updates.

The project features a C++ core engine integration that executes graph sampling and tensor operations natively, coupled with a distributed graph partitioning engine that fragments graph topologies across multiple worker nodes for parallel traversal. It includes a neighbor sampling pipeline that feeds balanced mini-batches into neural network training loops, alongside a partition-based distributed storage layer with local neighborhood indexing. A high-level Python API bridges user-defined workflows with the underlying distributed execution engine.

Features

Graph Neural Networks - Enables the construction and training of complex graph neural network models on massive datasets.
Neighborhood Sampling - Extracts localized subgraphs and node neighborhoods on demand to feed balanced mini-batches into training loops.
Distributed Graph Engines - Fragments massive graph topologies across multiple cluster nodes for parallel traversal and model training.
Graph Neural Network Training Routines - Supports building and training complex graph neural network models on massive datasets using specialized sampling.
Graph Neighborhood Sampling - Extracts training subgraphs and node neighborhoods using specialized query interfaces for graph models.
Distributed Graph Storage - Stores fragmented graph topologies across worker nodes with local neighborhood indexing for fast parallel access.
Graph-Augmented Inference - Serves live predictions by sampling dynamic graphs with low latency and processing streaming updates.
Real-Time Prediction Serving - Serves live graph predictions with low latency by querying dynamic graph structures and streaming updates.
C++ Core Implementations - Executes heavy graph sampling and tensor operations natively in C++ for maximum execution throughput.

Istoric stele

alibabagraph-learn

View on GitHub

1,341 stele266 fork-uriC++Apache-2.012 vizualizări

Graph Learn

Features

Graph Neural Networks - Enables the construction and training of complex graph neural network models on massive datasets.
Neighborhood Sampling - Extracts localized subgraphs and node neighborhoods on demand to feed balanced mini-batches into training loops.
Distributed Graph Engines - Fragments massive graph topologies across multiple cluster nodes for parallel traversal and model training.
Graph Neural Network Training Routines - Supports building and training complex graph neural network models on massive datasets using specialized sampling.
Graph Neighborhood Sampling - Extracts training subgraphs and node neighborhoods using specialized query interfaces for graph models.
Distributed Graph Storage - Stores fragmented graph topologies across worker nodes with local neighborhood indexing for fast parallel access.
Graph-Augmented Inference - Serves live predictions by sampling dynamic graphs with low latency and processing streaming updates.
Real-Time Prediction Serving - Serves live graph predictions with low latency by querying dynamic graph structures and streaming updates.
C++ Core Implementations - Executes heavy graph sampling and tensor operations natively in C++ for maximum execution throughput.

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Vezi toate cele 30 alternative pentru Graph Learn

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Întrebări frecvente

Ce face alibaba/graph-learn?

Istoric stele

Alternative open-source pentru Graph Learn

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mdeff/cnn_graph
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Cnn_graph is a graph convolutional network framework and graph signal processing library designed for machine learning research. It provides computational notebooks and code to process and classify graph-structured data by combining node features with an underlying adjacency matrix representation. The framework performs spectral graph convolutions through localized filters and accelerates filtering operations using truncated Chebyshev polynomials to avoid explicit graph Laplacian diagonalization. It includes a graph-structured data pipeline and sparse adjacency representations to handle irreg
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rusty1s/pytorch_geometric
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PyTorch 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
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