Representation Learning for Attributed Multiplex Heterogeneous Network

By Yukuo Cen, Xu Zou, Jianwei Zhang, Hongxia Yang, Jingren Zhou, Jie Tang. Knowledge Discovery and Data Mining, 2019

485 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Information Systems, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/3c64b7a74c749d43b1a4b96dd1a00620ba613ee0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Jie Tang
2. Hongxia Yang
3. Yuxiao Dong
4. Ming Ding
5. Chang Zhou
6. Qiang Liu
7. Shuo Zhang
8. Mark J. Coates

In Information Systems

1. Yuxiao Dong
2. Jie Tang
3. Chang Zhou
4. Ming Ding
5. Jianwei Zhang
6. Ruiming Tang
7. Huifeng Guo
8. Hongxia Yang

In Statistical and Nonlinear Physics

1. Hongxia Yang
2. Jie Tang
3. Yuxiao Dong
4. Chang Zhou
5. Mark J. Coates
6. Ming Ding
7. Ruiming Tang
8. Huifeng Guo

Citations in the corpus

• MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding (2020, unknown): “• GATNE [5] is a heterogeneous GNN.”
• GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems (2020, background): “Another line of work deals with the complex and heterogeneous interaction types between user and item in large-scale e-commerce networks [2, 4].”
• Efficient and Accurate Physics-aware Multiplex Graph Neural Networks for 3D Small Molecules and Macromolecule Complexes (2022, methodology): “In practice, various methods have been proposed to learn the embedding of the multiplex graph [3, 25, 45] and the multiplex graph can be applied in many fields [17, 39].”
• AliGraph: A Comprehensive Graph Neural Network Platform (2019, unknown): “, sampling (AHEP), multiplex (GATNE[7]), multimode (Mixture GNN[41]), hierarchy (Hierarchical GNN[35]), dynamic (Evolving GNN[61]) and multi-sourced information (Bayesian GNN).”
• Controllable Multi-Interest Framework for Recommendation (2020, background): “The attention mechanism is also adapted to recommender systems [6, 59] and is rather useful on real-world recommendation tasks.”
• Are we really making much progress?: Revisiting, benchmarking and refining heterogeneous graph neural networks (2021, methodology): “C.12 GATNE C.12.1 Link prediction.”