Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks-Reference-Cited by-同舟云学术

Easing Embedding Learning by Comprehensive Transcription of Heterogeneous Information Networks

Published:2018-07-19 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
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Author:

Shi Yu¹,Zhu Qi¹,Guo Fang¹,Zhang Chao¹,Han Jiawei¹

Affiliation:

1. University of Illinois at Urbana-Champaign, Urbana, IL, USA

Funder

Army Research Laboratory

National Institute of General Medical Sciences

National Science Foundation

Defense Advanced Research Projects Agency

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3219819.3220006

Reference38 articles.

1. Sami Abu-El-Haija Bryan Perozzi and Rami Al-Rfou . 2017. Learning Edge Representations via Low-Rank Asymmetric Projections CIKM. 10.1145/3132847.3132959 Sami Abu-El-Haija Bryan Perozzi and Rami Al-Rfou . 2017. Learning Edge Representations via Low-Rank Asymmetric Projections CIKM. 10.1145/3132847.3132959

2. Shiyu Chang Wei Han Jiliang Tang Guo-Jun Qi Charu C Aggarwal and Thomas S Huang . 2015. Heterogeneous network embedding via deep architectures KDD. 10.1145/2783258.2783296 Shiyu Chang Wei Han Jiliang Tang Guo-Jun Qi Charu C Aggarwal and Thomas S Huang . 2015. Heterogeneous network embedding via deep architectures KDD. 10.1145/2783258.2783296

3. Ting Chen and Yizhou Sun . 2017. Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification WSDM. 10.1145/3018661.3018735 Ting Chen and Yizhou Sun . 2017. Task-Guided and Path-Augmented Heterogeneous Network Embedding for Author Identification WSDM. 10.1145/3018661.3018735

4. Hanjun Dai Bo Dai and Le Song . 2016. Discriminative embeddings of latent variable models for structured data ICML. Hanjun Dai Bo Dai and Le Song . 2016. Discriminative embeddings of latent variable models for structured data ICML.

5. Yuxiao Dong Nitesh V Chawla and Ananthram Swami . 2017. metapath2vec: Scalable representation learning for heterogeneous networks KDD. 10.1145/3097983.3098036 Yuxiao Dong Nitesh V Chawla and Ananthram Swami . 2017. metapath2vec: Scalable representation learning for heterogeneous networks KDD. 10.1145/3097983.3098036

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