Reinforcement-Learning-Based Competitive Opinion Maximization Approach in Signed Social Networks

Wang, Xingwei; Zhao, Yong; Yi, Bo; Lu, Xijia; Yang, Mingzhou; Huang, Min

doi:10.1109/tcss.2021.3120421

Cited by 18 publications

(2 citation statements)

References 39 publications

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“…With the similar idea in [2], Ali et al [4] model the CIM problem under topology-unknown network scenario as an RL problem, then apply DQN to learn when to explore the unknown network and how to select seed set, achieving better performance than heuristic methods. Next, He et al [74] study competitive opinion maximization in signed social network where negative weights of edges are introduced to model the relationship of dislike CLT variant hand-crafted features Q-learning 2020 Ali et al [4] CLT variant hand-crafted features DQN 2021 He et al [74] CIC hand-crafted features Q-learning 2021 Ali et al [5] CLT variant node2Vec DQN 2022 Ali et al [3] CLT hand-crafted features Q-learning 2018 Yadav et al [207] Conting or distrust. They exploit Q-learning to select nodes against unknown opponent strategy.…”

Section: Competitive Influence Maximization (Cim)mentioning

confidence: 99%

A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization

Li¹,

Gao²,

Yunxuan³

et al. 2022

Preprint

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Influence Maximization (IM) is a classical combinatorial optimization problem, which can be widely used in mobile networks, social computing, and recommendation systems. It aims at selecting a small number of users such that maximizing the influence spread across the online social network. Because of its potential commercial and academic value, there are a lot of researchers focusing on studying the IM problem from different perspectives. The main challenge comes from the NP-hardness of the IM problem and #P-hardness of estimating the influence spread, thus traditional algorithms for overcoming them can be categorized into two classes: heuristic algorithms and approximation algorithms. However, there is no theoretical guarantee for heuristic algorithms, and the theoretical design is close to the limit. Therefore, it is almost impossible to further optimize and improve their performance.With the rapid development of artificial intelligence, the technology based on Machine Learning (ML) has achieved remarkable achievements in many fields. In view of this, in recent years, a number of new methods have emerged to solve combinatorial optimization problems by using ML-based techniques. These methods have the advantages of fast solving speed and strong generalization ability to unknown graphs, which provide a brand-new direction for solving combinatorial optimization problems. Therefore, we abandon the traditional algorithms based on iterative search and review the recent development of ML-based methods, especially Deep Reinforcement Learning, to solve the IM problem and other variants in social networks. We focus on summarizing the relevant background knowledge, basic principles, common methods, and applied research. Finally, the challenges that need to be solved urgently in future IM research are pointed out. CCS Concepts: • Networks → Network algorithms; • Theory of computation → Design and analysis of algorithms; • Computing methodologies → Machine learning approaches; Knowledge representation and reasoning.

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Section: Competitive Influence Maximization (Cim)mentioning

confidence: 99%

A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization

Li¹,

Gao²,

Yunxuan³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…By incorporating Sparse Gaussian Process models, the algorithm achieves high accuracy and computational efficiency, showcasing its capability to enhance wind farm performance and its applicability to realworld engineering problems. He et al [30] present a reinforcement-learning-based approach for competitive opinion maximization in signed social networks. The authors develop a two-phase model incorporating an activated dynamic opinion model and a reinforcement-learning-based seeding process to identify and influence key individuals in social networks, optimizing the spread of opinions against competitive opinions.…”

Section: Introductionmentioning

confidence: 99%

Efficient Surrogate Model Assisted Estimation of Distribution Algorithm for Expensive Optimization

Shang,

Li,

Hao

et al. 2024

IEEE Access

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In recent years, several surrogate assisted evolutionary algorithms (SAEAs) have been proposed to solve expensive optimization problems. These problems lack explicit expressions and are characterized by high invocation costs. SAEAs leverage surrogate models to accelerate convergence towards the optimal region and reduce the number of function evaluations. While Gaussian Processes (GPs) are widely used due to their robustness and capability of providing uncertainty estimates, their applicability becomes limited in scenarios involving a large number of samples or high-dimensional spaces. This is due to their cubic time complexity in relation to the number of samples, which results in prohibitive computational demands for large-scale problems. To address the challenge, this work presents an efficient surrogate model-assisted estimation of the distribution algorithm (ESAEDA). This method employs a random forest as a surrogate model and combines it with a GP-hedge acquisition strategy to ensure the efficiency and accuracy of model-assisted selection. An improved EDA model called the variable-width histogram model with some unevaluated solutions is used to generate new solutions. To demonstrate the benefits of the proposed method, we compared ESAEDA with several state-of-the-art surrogate-assisted evaluation algorithms and the Bayesian optimization method. Experimental results demonstrate the superiority of the proposed algorithm over these comparison algorithms for two well-known test suites.

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A Genetic Algorithm-Based Heuristic for Rumour Minimization in Social Networks

Rajak,

Kare

2024

Lecture Notes in Computer Science

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Reinforcement-Learning-Based Competitive Opinion Maximization Approach in Signed Social Networks

Cited by 18 publications

References 39 publications

A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization

A Survey on Influence Maximization: From an ML-Based Combinatorial Optimization

Efficient Surrogate Model Assisted Estimation of Distribution Algorithm for Expensive Optimization

A Genetic Algorithm-Based Heuristic for Rumour Minimization in Social Networks

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