An improved influence maximization method for social networks based on genetic algorithm

Lotf, Jalil Jabari; Azgomi, Mohammad Abdollahi; Dishabi, Mohammad Reza Ebrahimi

doi:10.1016/j.physa.2021.126480

Cited by 56 publications

(12 citation statements)

References 30 publications

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“…15 Moreover, as science and technology develop, new research methods continue to emerge for the analysis of information dissemination in OSNs. Genetic algorithm, 16 deep learning neural network 17 and Markov random field 18 are just a few examples of the development. Li et al 19 and Xiao et al 20 used mathematical models to study the macro process of online information diffusion.…”

Section: Related Workmentioning

confidence: 99%

Modeling the dissemination of privacy information in online social networks

Zhu,

Li,

Pan

et al. 2024

Trans Emerging Tel Tech

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Driven by the rapid development of information technology, online social networks (OSNs) have experienced a fast development in recent years, allowing increasingly more people to share and spread information over OSNs. The rapid rise of OSN platforms such as Facebook and Twitter is sufficient evidence of such development. As one type of information, privacy information can also be created and disseminated over an OSN, posing a severe threat to individual privacy. This article attempts to construct a model for disseminating privacy information in OSNs and to analyze the model by simulating the dissemination process of privacy information in OSNs. First, we establish network models that exhibit the main characteristics of OSNs. Second, by considering the factors related to social relationships, especially intimacy between users and the attention of users to the privacy subject, we derive the parameters for privacy information dissemination models in OSNs. Third, based on the theory of information dissemination dynamics, we construct a model for information dissemination that conforms to the properties of privacy information. We also present some experimental results based on the constructed model and analyze the characteristics of privacy information dissemination. Fourth, we study and verify the various properties of the model through a set of experiments. The proposed model provides the opportunity to better understand the dynamics of privacy information dissemination in OSNs and the effect of user behavior on dissemination.

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Section: Related Workmentioning

confidence: 99%

Modeling the dissemination of privacy information in online social networks

Zhu,

Li,

Pan

et al. 2024

Trans Emerging Tel Tech

View full text Add to dashboard Cite

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“…The approaches in this category can separate into heuristic, meta‐heuristic, and community‐based approaches. For example, we can refer to SIMPATH Chen, Yuan, and Zhang (2010) Gong, Song, et al (2016) or Jabari Lotf et al (2022) which are heuristic methods. Gong, Yan, et al (2016) and SIMSEK and KARA (2018) which is a discrete particle swarm optimization algorithm, and Shang et al (2016) which uses community detection of the underlying social network as an intermediate step to scale down the SIM Problem to the community level.…”

Section: Related Workmentioning

confidence: 99%

Classify nodes based on their degree distribution: A more scalable method for influence maximization

Boroujeni

Soleimani

2022

Expert Systems

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One of the main problems in viral marketing is influence maximization (IM). With a social network and a predefined propagation model, the aim is to seek a subset of nodes that spread the influence widely into the network. Most scalable methods with provable approximation guarantees are presented for this problem based on the reverse influence sampling (RIS) framework. The RIS framework has two phases: sampling and node selection. The sampling phase encountered two challenges in the sampling phase: the number of required samples and the sampling method. Most methods have focused on the first challenge, that is, sample size, and have tried to provide a rigid sample size. In this paper, we focus on the second challenge: how to improve the precision of sampling. We propose to use stratified sampling rather than simple random sampling. Since the degree of each node is one of the affecting factors in the diffusion process. This issue leads us to use stratified sampling based on a degree distribution. The results show that with the application of the proposed method, the solution can estimate with fewer samples, which is faster than the stateof-the-art methods.

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“…Diferent from traditional optimization techniques that use the frst-order or second-order derivatives, EAs do not require any problem-specifc information and have strong global search abilities. Tey have been shown to be powerful optimization techniques for solving various types of black box optimization problems [6][7][8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Differential Evolution with a Level-Based Learning Strategy for Multimodal Optimization

Zhang,

Wei,

Zhao

et al. 2023

International Journal of Intelligent Systems

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Multimodal optimization aims at efficiently finding multiple optimal solutions of a problem. Owing to the population-based search mechanism, evolutionary algorithms (EAs) are becoming increasingly popular in solving multimodal optimization problems (MOPs). Most existing work focuses on designing and incorporating niching techniques into EAs so that multiple subpopulations can be formed and assigned to locate different optima. To further enhance the exploration and exploitation abilities of existing EAs, this paper developed a multimodal level-based learning strategy. The basic idea is that individuals should be treated differently according to their positions in the subpopulation. In the evolutionary process, a subpopulation is formed for each candidate solution by grouping its neighboring solutions. Then, individuals in the subpopulation are sorted according to their fitness. Subsequently, the multimodal level-based learning strategy applies different mutation operators to different individuals according to their rankings. Experiments are conducted on a set of benchmark problems to verify the efficacy of the multimodal level-based learning strategy. The results show that the proposed learning strategy can significantly enhance the performance of the existing algorithm. In addition, the algorithm integrated with the proposed strategy is applied to the task of finding multiple roots of nonlinear equation systems (NESs). The results indicate that with the support of the proposed learning strategy, the integrated algorithm compares favorably with state-of-the-art root finding algorithms.

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An improved influence maximization method for social networks based on genetic algorithm

Cited by 56 publications

References 30 publications

Modeling the dissemination of privacy information in online social networks

Modeling the dissemination of privacy information in online social networks

Classify nodes based on their degree distribution: A more scalable method for influence maximization

Differential Evolution with a Level-Based Learning Strategy for Multimodal Optimization

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