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Adaptive Submodular Influence Maximization with Myopic Feedback [article]

Guillaume Salha, Nikolaos Tziortziotis, Michalis Vazirgiannis
2018 arXiv   pre-print
This paper examines the problem of adaptive influence maximization in social networks.  ...  This strategy maximizes an alternative utility function that has been proven to be adaptive monotone and adaptive submodular.  ...  The utility functionf is not adaptive submodular under the standard IC model with myopic feedback. Proof.  ... 
arXiv:1704.06905v6 fatcat:3r76j74jsbhghohidro7hzevkm

Adaptive Greedy versus Non-adaptive Greedy for Influence Maximization [article]

Wei Chen, Binghui Peng, Grant Schoenebeck, Biaoshuai Tao
2019 arXiv   pre-print
Our second result shows that, for the general submodular cascade model with full-adoption feedback, the adaptive greedy algorithm can outperform the non-adaptive greedy algorithm by an unbounded factor  ...  Our first result shows that, for submodular influence maximization, the adaptive greedy algorithm can perform up to a (1-1/e)-fraction worse than the non-adaptive greedy algorithm, and that this ratio  ...  The adaptivity gap with myopic feedback is defined similarly.  ... 
arXiv:1911.08164v1 fatcat:ftlk6ic73neihjfszgwy5jt6vm

Adaptive Greedy versus Non-Adaptive Greedy for Influence Maximization

Wei Chen, Binghui Peng, Grant Schoenebeck, Biaoshuai Tao
2020 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
Our second result shows that, for the general submodular cascade model with full-adoption feedback, the adaptive greedy algorithm can outperform the non-adaptive greedy algorithm by an unbounded factor  ...  Our first result shows that, for submodular influence maximization, the adaptive greedy algorithm can perform up to a (1-1/e)-fraction worse than the non-adaptive greedy algorithm, and that this ratio  ...  The adaptivity gap with myopic feedback is defined similarly.  ... 
doi:10.1609/aaai.v34i01.5398 fatcat:6mqkszaa3bcwjfyoa3n5hivd3u

Improved Approximation Factor for Adaptive Influence Maximization via Simple Greedy Strategies [article]

Gianlorenzo D'Angelo, Debashmita Poddar, Cosimo Vinci
2021 arXiv   pre-print
with an independent probability of diffusing influence.  ...  We focus on the myopic feedback model, in which we can only observe which neighbors of previously selected seeds have been influenced and on the independent cascade model, where each edge is associated  ...  Influence Maximization under the Myopic Feedback Model: Preliminaries Independent Cascade Model.  ... 
arXiv:2007.09065v2 fatcat:ixdmeznqxjd3tcpvxbwjnoryoa

Adaptive Greedy versus Non-adaptive Greedy for Influence Maximization

Wei Chen, Binghui Peng, Grant Schoenebeck, Biaoshuai Tao
2022 The Journal of Artificial Intelligence Research  
Our second result shows that, for the general submodular diffusion model with full-adoption feedback, the adaptive greedy algorithm can outperform the non-adaptive greedy algorithm by an unbounded factor  ...  Our first result shows that, for submodular influence maximization, the adaptive greedy algorithm can perform up to a (1 − 1/e)-fraction worse than the non-adaptive greedy algorithm, and that this ratio  ...  The adaptivity gap with myopic feedback is defined similarly.  ... 
doi:10.1613/jair.1.12997 fatcat:pc2lnv7en5a5lhx74mofx5hf5e

Better Bounds on the Adaptivity Gap of Influence Maximization under Full-adoption Feedback

Gianlorenzo D'Angelo, Debashmita Poddar, Cosimo Vinci
2021 PROCEEDINGS OF THE THIRTIETH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE TWENTY-EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE  
In the influence maximization (IM) problem, we are given a social network and a budget k, and we look for a set of k nodes in the network, called seeds, that maximize the expected number of nodes that  ...  We focus on the full-adoption feedback in which we can observe the entire cascade of each previously selected seed and on the independent cascade model where each edge is associated with an independent  ...  They show that the adaptivity gap of the independent cascade model with myopic feedback belongs to [ −1 , 4].  ... 
doi:10.1609/aaai.v35i13.17433 fatcat:cr2csamokzfxjhgdawfupauzci

Better Bounds on the Adaptivity Gap of Influence Maximization under Full-adoption Feedback [article]

Gianlorenzo D'Angelo, Debashmita Poddar, Cosimo Vinci
2020 arXiv   pre-print
In the influence maximization (IM) problem, we are given a social network and a budget k, and we look for a set of k nodes in the network, called seeds, that maximize the expected number of nodes that  ...  To prove our bounds, we introduce new techniques to relate adaptive policies with non-adaptive ones that might be of their own interest.  ...  Adaptive submodular influence maximization with myopic feedback.  ... 
arXiv:2006.15374v1 fatcat:44g3d3naefavpc37a3jjbk6sxu

Optimal marketing strategies over social networks

Jason Hartline, Vahab Mirrokni, Mukund Sundararajan
2008 Proceeding of the 17th international conference on World Wide Web - WWW '08  
While influence maximization has been studied in this context (see Chapter 24 of [10]), we study revenue maximization, arguably, a more natural objective.  ...  We first argue why such strategies are reasonable and then show how to use recently developed set-function maximization techniques to find the right set of buyers to influence.  ...  Offer buyer i the optimal (myopic) price as a function of the distribution Fi,S. Note that the optimal (myopic) price is adaptive, and is based on the history of sales.  ... 
doi:10.1145/1367497.1367524 dblp:conf/www/HartlineMS08 fatcat:eiyfizjcsfhjbc7qphrc3wyqv4

On Adaptivity Gaps of Influence Maximization Under the Independent Cascade Model with Full-Adoption Feedback

Wei Chen, Binghui Peng, Michael Wagner
2019 International Symposium on Algorithms and Computation  
In this paper, we study the adaptivity gap of the influence maximization problem under the independent cascade model when full-adoption feedback is available.  ...  Our analysis provides several novel ideas to tackle the correlated feedback appearing in adaptive stochastic optimization, which may be of independent interest.  ...  myopic feedback and full-adoption feedback.  ... 
doi:10.4230/lipics.isaac.2019.24 dblp:conf/isaac/ChenP19 fatcat:hknq7q2yubgcnleve74rildi7i

Beyond Adaptive Submodularity: Approximation Guarantees of Greedy Policy with Adaptive Submodularity Ratio [article]

Kaito Fujii, Shinsaku Sakaue
2019 arXiv   pre-print
Examples of newly analyzed problems include important applications such as adaptive influence maximization and adaptive feature selection.  ...  Our adaptive submodularity ratio also provides bounds of adaptivity gaps.  ...  With this example instance, we can readily see that the adaptive submodularity ratio can be very small under the myopic feedback model.  ... 
arXiv:1904.10748v1 fatcat:h3dodct7kbaylezd7n6zrfvoce

Adaptive Influence Maximization under General Feedback Models [article]

Guangmo Tong, Ruiqi Wang
2019 arXiv   pre-print
In this paper, we provide a systematic study on the adaptive influence maximization problem, focusing on the algorithmic analysis of the scenarios when it is not adaptive submodular.  ...  The classic influence maximization problem explores the strategies for deploying seed users before the start of the diffusion process such that the total influence can be maximized.  ...  ., Myopic feedback model) the AIM problem is unfortunately not adaptive submodular anymore, and to the best of our knowledge there is no analysis technique available for such general feedback models.  ... 
arXiv:1902.00192v3 fatcat:w7i2wi34azejhnjbfvez5umw6y

No Time to Observe: Adaptive Influence Maximization with Partial Feedback [article]

Jing Yuan, Shaojie Tang
2019 arXiv   pre-print
Although influence maximization problem has been extensively studied over the past ten years, majority of existing work adopt one of the following models: full-feedback model or zero-feedback model.  ...  In the zero-feedback model, we have to commit the seed users all at once in advance, this strategy is also known as non-adaptive policy.  ...  The second category is adaptive influence maximization, which is closely related to adaptive/stochastic submodular maximization Golovin and Krause (2011), Badanidiyuru et al. (2016) , Tong et al. (2016  ... 
arXiv:1609.00427v6 fatcat:romfqvtscvbrdpuxc3lofrif7m

Maximizing Stochastic Monotone Submodular Functions [article]

Arash Asadpour, Hamid Nazerzadeh
2015 arXiv   pre-print
We study the problem of maximizing a stochastic monotone submodular function with respect to a matroid constraint.  ...  We propose a polynomial-time non-adaptive policy that achieves this bound. We also present an adaptive myopic policy that obtains at least half of the optimal value.  ...  Hence, the problem of maximizing influence can be seen as a maximizing submodular function problem subject to cardinality constraints.  ... 
arXiv:0908.2788v3 fatcat:uyy7jcwjnncwlh6iy4uoyrvklu

No Time to Observe: Adaptive Influence Maximization with Partial Feedback

Jing Yuan, Shaojie Tang
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
Although influence maximization problem has been extensively studied over the past ten years, majority of existing work adopt one of the following models: full-feedback model or zero-feedback model.  ...  In the zero-feedback model, we have to commit the seed users all at once in advance, this strategy is also known as non-adaptive policy.  ...  Adaptive Influence Maximization with Partial Feedback First of all, we want to emphasize the difference between "round" and "slot".  ... 
doi:10.24963/ijcai.2017/546 dblp:conf/ijcai/YuanT17 fatcat:pm2ekbhwdrhjvd34trduukbicu

Adaptive Submodular Optimization under Matroid Constraints [article]

Daniel Golovin, Andreas Krause
2011 arXiv   pre-print
Specifically, we prove that a natural adaptive greedy algorithm provides a 1/(p+1) approximation for the problem of maximizing an adaptive monotone submodular function subject to p matroid constraints,  ...  Many important problems in discrete optimization require maximization of a monotonic submodular function subject to matroid constraints.  ...  Many important problems, such as facility location [1] , coverage [2] , influence maximization [3] , and experimental design [4] can be reduced to constrained maximization of a submodular set function  ... 
arXiv:1101.4450v1 fatcat:tpsg3bpibbcqtdm4gcpyliy2eu
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