# Representation Learning for Attributed Multiplex Heterogeneous Network

## TL;DR

This paper introduces a unified embedding framework for large, attributed, multiplex heterogeneous networks, improving link prediction accuracy and scalability over previous methods, with successful deployment in Alibaba's recommendation system.

## Contribution

It formalizes the embedding problem for complex networks and proposes a scalable, expressive framework supporting both transductive and inductive learning, with theoretical analysis and practical validation.

## Key findings

- Achieves 5.99-28.23% lift in F1 scores over state-of-the-art methods.
- Demonstrates effectiveness on datasets from Amazon, YouTube, Twitter, Alibaba.
- Successfully deployed in Alibaba's product recommendation system.

## Abstract

Network embedding (or graph embedding) has been widely used in many real-world applications. However, existing methods mainly focus on networks with single-typed nodes/edges and cannot scale well to handle large networks. Many real-world networks consist of billions of nodes and edges of multiple types, and each node is associated with different attributes. In this paper, we formalize the problem of embedding learning for the Attributed Multiplex Heterogeneous Network and propose a unified framework to address this problem. The framework supports both transductive and inductive learning. We also give the theoretical analysis of the proposed framework, showing its connection with previous works and proving its better expressiveness. We conduct systematical evaluations for the proposed framework on four different genres of challenging datasets: Amazon, YouTube, Twitter, and Alibaba. Experimental results demonstrate that with the learned embeddings from the proposed framework, we can achieve statistically significant improvements (e.g., 5.99-28.23% lift by F1 scores; p<<0.01, t-test) over previous state-of-the-art methods for link prediction. The framework has also been successfully deployed on the recommendation system of a worldwide leading e-commerce company, Alibaba Group. Results of the offline A/B tests on product recommendation further confirm the effectiveness and efficiency of the framework in practice.