Representation Learning for Attributed Multiplex Heterogeneous Network
This work addresses the challenge of scaling network embedding to large, complex real-world networks with multiple types and attributes, which is crucial for applications like recommendation systems, though it appears incremental as it builds on existing methods with a unified approach.
The paper tackles the problem of embedding learning for attributed multiplex heterogeneous networks, which involve multiple node/edge types and attributes, by proposing a unified framework that supports transductive and inductive learning; it achieves statistically significant improvements, such as 5.99-28.23% lift in F1 scores for link prediction, and has been successfully deployed in Alibaba's recommendation system.
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.