GraphScope: A One-Stop Large-Scale Graph Computing System from Alibaba

GraphScope is a unified distributed graph computing platform that provides a one-stop environment for performing diverse graph operations on a cluster of computers through a user-friendly Python interface. GraphScope makes multi-staged processing of large-scale graph data on compute clusters simple by combining several important pieces of Alibaba technology: including GRAPE, GraphCompute, and Graph-Learn (GL) for analytics, interactive, and graph neural networks (GNN) computation, respectively, and the vineyard store that offers efficient in-memory data transfers.

Contents

• Installation
  • Install GraphScope locally without Kubernetes
  • Install GraphScope Client for Kubernetes
• Getting Started
  • Example: Node clustering on Citation Network
  • Launching Session
  • Loading Graph
  • Interactive Query
  • Graph Analytics
  • Graph Neural Networks (GNNs)
  • Closing Session
• Tutorials
  • Node Classification on citation network
  • Graph Manipulations with NetowrkX Compatible APIs
  • Run GraphScope like NetworkX
  • Loading Graphs
  • Built-in Analytical Algorithms
  • Writing Your Own Algorithms
  • Interactive Query with Gremlin
  • Unsupervised Learning with GraphSage
  • Supervised Learning with GCN
  • Revisit Classification on Citation Network on K8s
  • Unsupervised Learning with Bipartite GraphSage
• Deployment
  • Deployment with Helm
  • Deploy with AWS/Aliyun
  • Deployment on local
• Loading Graphs
  • Load Built-in Datasets
  • Loading Your Own Datasets
  • Advanced Usage
  • Loading From Pandas or Numpy
  • Loader Variants
• Graph Transformations
  • Attach new labels
  • Projection
  • Transform to simple graph implicitly
  • Add results back to graph as a property
• GraphScope Analytical Engine
  • Built-in Algorithms
  • Result Processing
  • Writing Your Own Algorithms in PIE
  • Writing Algorithms in Pregel
  • Using math.h Functions in Algorithms
  • Run Your Own Algorithms
  • Run Algorithm in Java
  • Glossory
• GraphScope Interactive Engine
  • Apache TinkerPop
  • Connecting Gremlin within Python
  • Connecting Gremlin within Java
  • Gremlin Console
  • Programming with Gremlin–101
  • Compatibility with TinkerPop
• GraphScope Learning Engine
  • Graph Learning Model
  • Developing Your Own Model
• Persistent Graph Store
  • Overview
  • Deploying Store Service
  • Defining Graph Schema
  • Running Gremlin Queries
  • Realtime Write
  • Bulk Loading
• Frequently Asked Questions
• Developer Guide
  • Building and Testing GraphScope locally with Docker
  • Building and Testing GraphScope on Kubernetes
  • Building and Testing GraphScope on local
  • Build Python Wheels
  • Code Format

Reference

• Python API Reference
  • Session
  • Graph
  • Analytical App
  • Context
  • Selector
  • Operation
  • Cython SDK API
  • Interactive Query
  • Graph Learning
  • NetworkX-style API
• Analytical Engine API Reference
  • Analytical Engine
  • Fragments in GraphScope
  • Analytical Apps
  • Analytical Workers
  • App’s Context
• Analytical Engine Java API Reference

Indices and tables

• Index

• Module Index

• Search Page