Adversarial Training Model Unifying Feature Driven and Point Process Perspectives for Event Popularity Prediction-Reference-Cited by-同舟云学术

Adversarial Training Model Unifying Feature Driven and Point Process Perspectives for Event Popularity Prediction

Published:2018-10-17 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 27th ACM International Conference on Information and Knowledge Management
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Author:

Wu Qitian¹,Yang Chaoqi¹,Zhang Hengrui¹,Gao Xiaofeng¹,Weng Paul¹,Chen Guihai¹

Affiliation:

1. Shanghai Jiao Tong University, Shanghai, China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3269206.3271714

Reference43 articles.

1. Martín Arjovsky Soumith Chintala and Lé on Bottou. 2017. Wasserstein Generative Adversarial Networks. In ICML. 214--223. Martín Arjovsky Soumith Chintala and Lé on Bottou. 2017. Wasserstein Generative Adversarial Networks. In ICML. 214--223.

2. Peng Bao Huawei Shen Xiaolong Jin and Xueqi Cheng. 2015. Modeling and Predicting Popularity Dynamics of Microblogs using Self-Excited Hawkes Processes. In WWW. 9--10. 10.1145/2740908.2742744 Peng Bao Huawei Shen Xiaolong Jin and Xueqi Cheng. 2015. Modeling and Predicting Popularity Dynamics of Microblogs using Self-Excited Hawkes Processes. In WWW. 9--10. 10.1145/2740908.2742744

3. Justin Cheng Lada A. Adamic P. Alex Dow Jon M. Kleinberg and Jure Leskovec. 2014. Can cascades be predicted?. In WWW. 925--936. 10.1145/2566486.2567997 Justin Cheng Lada A. Adamic P. Alex Dow Jon M. Kleinberg and Jure Leskovec. 2014. Can cascades be predicted?. In WWW. 925--936. 10.1145/2566486.2567997

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