Better bounds on the adaptivity gap of influence maximization under full-adoption feedback-Reference-Cited by-同舟云学术

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Better bounds on the adaptivity gap of influence maximization under full-adoption feedback

Published:2023-05 Issue: Volume:318 Page:103895
ISSN:0004-3702
Container-title:Artificial Intelligence
language:en
Short-container-title:Artificial Intelligence

Author:

D'Angelo Gianlorenzo^ORCID,Poddar Debashmita^ORCID,Vinci Cosimo^ORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Linguistics and Language,Language and Linguistics

Reference51 articles.

1. Improved approximation algorithms for stochastic matching;Adamczyk,2015

2. Infectious Diseases of Humans: Dynamics and Control;Anderson,1992

3. Maximizing stochastic monotone submodular functions;Asadpour;Manag. Sci.,2016

4. Locally adaptive optimization: adaptive seeding for monotone submodular functions;Badanidiyuru,2016

5. Emergence of scaling in random networks;Barabási;Science,1999

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Research on maximizing real demand response based on link addition in social networks;Computer Communications;2024-12

2. Graph convolutional networks with the self-attention mechanism for adaptive influence maximization in social networks;Complex & Intelligent Systems;2024-08-28

3. Adaptivity Gap for Influence Maximization with Linear Threshold Model on Trees;Frontiers of Algorithmics;2023

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