Graph Deep Learning: Methods, Frameworks, and Industrial Applications

Graph deep learning, extending deep models to irregular graph data via spatial and spectral GNNs such as GCN, GAT, and GraphSAGE, has matured into frameworks like Alibaba’s open‑source Euler, which scales to billions of nodes, powers a heterogeneous query‑item‑ad graph for search advertising, and demonstrably boosts click‑through rates by over 1.5%.

Distributed TrainingEuler frameworkgraph embeddings

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Graph Deep Learning: Methods, Frameworks, and Industrial Applications

Alibaba Cloud Developer

Jan 18, 2019 · Artificial Intelligence

How Alibaba’s Open‑Source Euler Framework Powers Large‑Scale Graph Deep Learning

Euler, Alibaba's newly open‑sourced graph deep‑learning framework, combines distributed graph processing with neural network training to handle billions of nodes and edges, supports heterogeneous graphs, offers built‑in algorithms, and has already boosted advertising, fraud detection, and other industry applications.

AI InfrastructureEuler frameworkdistributed computing

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How Alibaba’s Open‑Source Euler Framework Powers Large‑Scale Graph Deep Learning