Adaptive Influence Maximization-Reference-Cited by-同舟云学术

Adaptive Influence Maximization

Published:2019-07-25 Issue: Volume: Page:
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Container-title:Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
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Author:

Cautis Bogdan¹,Maniu Silviu¹,Tziortziotis Nikolaos²

Affiliation:

1. LRI, Université Paris-Sud, Orsay, France

2. Tradelab and LRI, Université Paris-Sud, Paris, France

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3292500.3332285

Reference19 articles.

1. A. Arora S. Galhotra and S. Ranu. 2017. Debunking the Myths of Influence Maximization: An In-Depth Benchmarking Study. In SIGMOD. ACM. 10.1145/3035918.3035924 A. Arora S. Galhotra and S. Ranu. 2017. Debunking the Myths of Influence Maximization: An In-Depth Benchmarking Study. In SIGMOD. ACM. 10.1145/3035918.3035924

2. cC igdem Aslay Laks V. S. Lakshmanan Wei Lu and Xiaokui Xiao. 2018. Influence Maximization in Online Social Networks -- Tutorial. In WSDM. 10.1145/3159652.3162007 cC igdem Aslay Laks V. S. Lakshmanan Wei Lu and Xiaokui Xiao. 2018. Influence Maximization in Online Social Networks -- Tutorial. In WSDM. 10.1145/3159652.3162007

3. Prediction, Learning, and Games

4. Wei Chen Yajun Wang and Yang Yuan. 2013. Combinatorial Multi-armed Bandit: General Framework Results and Applications. In ICML. Wei Chen Yajun Wang and Yang Yuan. 2013. Combinatorial Multi-armed Bandit: General Framework Results and Applications. In ICML.

5. Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization;Golovin D.;J. Artif. Int. Res.,2011

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