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

Overview

Euler is the first domestically open‑sourced graph deep‑learning framework that has been validated in large‑scale core business scenarios at Alibaba. It integrates graph learning with deep learning to improve marketing efficiency and is applicable to finance, telecom, medical, and other complex network analysis tasks.

Core Capabilities

Euler provides four main capabilities:

Massive distributed learning for graphs with billions of nodes and hundreds of billions of edges.

Support for complex heterogeneous graphs, handling diverse node/edge types and rich attributes.

Seamless integration of graph representations into deep‑learning models via mini‑batch training.

A three‑layer architecture (graph engine, operator layer, algorithm layer) that enables rapid extension of new graph algorithms.

Built‑in Algorithms

Euler ships with many popular algorithms, including DeepWalk, Node2Vec, LINE, GCN, GraphSAGE, GAT, Scalable‑GCN, LsHNE, and LasGNN, covering random‑walk and neighbor‑aggregation methods. Some are original contributions that address heterogeneous graph embedding and large‑scale training efficiency.

System Design

The system consists of a distributed graph engine, a set of flexible graph operators, and high‑level algorithm implementations. The engine partitions massive graphs across nodes, supports replication for scalability, and optimizes storage for heterogeneous data. Operators expose C++ APIs for global weighted sampling, neighbor queries, and attribute lookup, and can be bound to deep‑learning frameworks such as TensorFlow and X‑DeepLearning.

High‑Level Algorithms

Scalable‑GCN reduces GCN training complexity from exponential to linear per mini‑batch, enabling three‑layer GCNs on Alibaba’s massive data. LsHNE is an unsupervised heterogeneous network embedding method that incorporates attribute information and novel negative‑sampling strategies. LasGNN is a semi‑supervised large‑scale heterogeneous graph convolutional network that combines metapath‑based GCN and efficient neighbor sampling.

Application Example

In Alibaba’s advertising matching pipeline, Euler’s LsHNE learns embeddings for queries, items, and ads from user behavior logs. These embeddings are used for fast vector‑based nearest‑neighbor retrieval, dramatically improving match relevance and downstream ranking performance.

Euler is publicly available on GitHub at .