Representation Learning for Attributed Multiplex Heterogeneous Network

5 May 2019  ·  Yukuo Cen, Xu Zou, Jianwei Zhang, Hongxia Yang, Jingren Zhou, Jie Tang ·

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.

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Datasets


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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Link Prediction Alibaba GATNE-I F1-Score 89.94 # 1
PR AUC 95.04 # 1
ROC AUC 84.2 # 1
Link Prediction Alibaba-S GATNE-T F1-Score 62.48 # 1
PR AUC 67.55 # 1
ROC AUC 66.71 # 1
Link Prediction Amazon GATNE-T F1-Score 92.87 # 1
PR AUC 97.05 # 1
ROC AUC 97.44 # 1
Link Prediction Twitter GATNE-T F1-Score 84.96 # 1
PR AUC 91.77 # 1
ROC AUC 92.3 # 1
Link Prediction YouTube GATNE-T F1-Score 76.83 # 1
PR AUC 81.93 # 1
ROC AUC 84.61 # 1

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