【论文解读 KDD 2018 | HEER】Easing Embedding Learning by Comprehensive Transcription of HIN

时间 2021-01-18

标签论文 NLP NE&GNN 繁體版

原文原文链接

论文链接：https://dl.acm.org/doi/10.1145/3219819.3220006 代码链接：https://github.com/GentleZhu/HEER 会议：KDD 2018 目录 1 摘要 2 介绍 2.1 动机 2.2 作者分析了现有的一些方法 2.3 挑战 2.4 贡献 3 HEER 3.1 一些定义 3.2 The HEER Model 4 总结参考文献 1

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1. HEER-Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks
2. 【论文解读 WSDM 2018 | SHINE】Signed HIN Embedding for Sentiment Link Prediction
3. 【论文解读 WSDM 2018 | DRL】Curriculum Learning for Heterogeneous Star Network Embedding via DRL
4. 不一样的论文解读：2018 KDD best paper「Embeddings at Airbnb」
5. 【论文解读 KDD 2019 | GATNE 】Representation Learning for Attributed Multiplex Heterogeneous Network
6. NRL: network representation learning & NE: network embedding. 相关论文总结
7. KDD 18 & AAAI 19 | 异构信息网络表示学习论文解读
8. 【论文解读 KDD 2019 | MEIRec】Metapath-guided Heterogeneous Graph Neural Network for Intent Recommendation
9. 论文阅读：Pixels to Graphs by Associative Embedding
10. 论文阅读理解 - Zero-shot Image Tagging by Hierarchical Semantic Embedding

>>更多相关文章<<