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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.","url_abs":"https://arxiv.org/abs/1905.01669v2","url_pdf":"https://arxiv.org/pdf/1905.01669v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"190501669","repo_url":"https://github.com/cenyk1230/GATNE","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"190501669","repo_url":"https://github.com/SY1706203/GATNE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"190501669","repo_url":"https://github.com/THUDM/GATNE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"190501669","repo_url":"https://github.com/dmlc/dgl/tree/master/examples/pytorch/GATNE-T","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"graph-embedding","task_name":"Graph Embedding"},{"task_slug":"inductive-learning","task_name":"Inductive Learning"},{"task_slug":"link-prediction","task_name":"Link Prediction"},{"task_slug":"network-embedding","task_name":"Network Embedding"},{"task_slug":"product-recommendation","task_name":"Product Recommendation"},{"task_slug":"representation-learning","task_name":"Representation Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/link-prediction-on-alibaba","task":"Link Prediction","dataset":"Alibaba","model":"GATNE-I","rank_in_archive_order":1,"of":1,"metrics":{"F1-Score":"89.94","PR AUC":"95.04","ROC AUC":"84.2"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-alibaba-s","task":"Link Prediction","dataset":"Alibaba-S","model":"GATNE-T","rank_in_archive_order":1,"of":1,"metrics":{"F1-Score":"62.48","PR AUC":"67.55","ROC AUC":"66.71"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-amazon","task":"Link Prediction","dataset":"Amazon","model":"GATNE-T","rank_in_archive_order":1,"of":1,"metrics":{"F1-Score":"92.87","PR AUC":"97.05","ROC AUC":"97.44"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-twitter","task":"Link Prediction","dataset":"Twitter","model":"GATNE-T","rank_in_archive_order":1,"of":1,"metrics":{"F1-Score":"84.96","PR AUC":"91.77","ROC AUC":"92.3"},"uses_additional_data":false},{"leaderboard":"/sota/link-prediction-on-youtube","task":"Link Prediction","dataset":"YouTube","model":"GATNE-T","rank_in_archive_order":1,"of":2,"metrics":{"F1-Score":"76.83","PR AUC":"81.93","ROC AUC":"84.61"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1905.01669","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1905.01669"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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