TSIM: A Two-Stage Selection Algorithm for Influence Maximization in Social Networks

Qiu, Liqing; Gu, Changzhi; Zhang, Shuang; Tian, Xiangbo; Zhang, Mingjv

doi:10.1109/access.2020.2966056

Cited by 19 publications

(4 citation statements)

References 28 publications

(35 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…DDSE is an evolutionary algorithm based on the degree descending search strategy, which is divided into four steps: initialization, mutation, crossover and selection. An efficient algorithm, called a two‐stage selection for influence maximization in social networks (TSIM) was proposed recently by the authors in Liqing et al (2020). Another algorithm called positive influence maximization in signed networks was presented in Ju et al (2020).…”

Section: Literature Surveymentioning

confidence: 99%

A novel meta‐heuristic approach for influence maximization in social networks

et al. 2021

View full text Add to dashboard Cite

Influence maximization in a social network focuses on the task of extracting a small set of nodes from a network which can maximize the propagation in a cascade model. Though greedy methods produce good solutions to the aforementioned problem, their high computational complexity is a major drawback. Centrality‐based heuristic methods often fail to overcome local optima, thereby producing sub‐optimal results. To this end, in this article, a framework has been presented which involves community detection in a social network and the utilization of the Shuffled Frog Leaping algorithm, in maximizing the two‐hop spread of influence under the independent cascade model. Local search strategies like the Late acceptance based hill climbing have been employed to improve the solution further. Experiments performed on three real‐world datasets have shown that our method performs markedly well with respect to the comparing algorithms.

show abstract

Section: Literature Surveymentioning

confidence: 99%

A novel meta‐heuristic approach for influence maximization in social networks

et al. 2021

View full text Add to dashboard Cite

show abstract

“…The overall aim was to maximize the influence, e.g. [24,25]. Most of these diffusion models considered only the effect of peer-influence except a few exceptions.…”

Section: Related Workmentioning

confidence: 99%

Fuzzy Relative Willingness: Modeling Influence of Exogenous Factors in Driving Information Propagation Through a Social Network

et al. 2020

View full text Add to dashboard Cite

A high percentage of information that propagates through a social network is sourced from different exogenous sources. E.g., individuals may form their opinions about products based on their own experience or reading a product review, and then share that with their social network. This sharing then diffuses through the network, evolving as a combination of both network and external effects. Besides, different individuals (nodes in a social network) have different degrees of exposition to their external sources, as well. Modeling this influence of external sources is important in order to understand the diffusion process and predict future content sharing patterns. Recognizing this fusion of intrinsic (network) effect and exogenous (external) effect, this paper develops a novel fuzzy relative willingness (FRW) model. Leveraging a fuzzy set approach provides a way to handle the uncertainties arising within the human concept of willingness. We demonstrate that FRW is able to accurately identify both top-k most content producers and diffusion effect based on external influence. We also demonstrate that the fuzzy set theory provides a compelling framework to model uncertainties pertaining to the influence as well as the susceptibility of individuals for both network and exogenous effects.

show abstract

“…Figure 1 is the MSNs model. Influence Maximization (IM) [8][9][10][11][12][13][14][15][16] problem is proposed for the study of social networks, and it comes from marketing of economics. Using social network method to analyze the social relations of mobile users in the network can further improve the efficiency of information transmission and forwarding.…”

Section: Introductionmentioning

confidence: 99%

A novel indicator weight tuning method based on fuzzy theory in mobile social networks

Gao¹,

Xu²,

Wang³

et al. 2020

Preprint

View full text Add to dashboard Cite

Mobile social network supports mobile communication and asynchronous social networking. How to measure the importance of nodes is critical and this problem remains to be answered. Most of the existing methods are subjective, so how to determine the weights of the centrality indicators is the key to solve the problem. In this paper, 9 common centrality indicators are viewed as our research object. We introduce fuzzy theory to partition the indicator weights, and to be more specific, we define a membership degree function to get the initial weight interval. With relative entropy, the weights of each centrality indicator can be obtained. By calculating the random data generated by simulation, genetic algorithm with single point crossover is used to optimize the weight of each indicator. Experiments show that the optimized weights are more effective and differentiable.

show abstract

TSIM: A Two-Stage Selection Algorithm for Influence Maximization in Social Networks

Cited by 19 publications

References 28 publications

A novel meta‐heuristic approach for influence maximization in social networks

A novel meta‐heuristic approach for influence maximization in social networks

Fuzzy Relative Willingness: Modeling Influence of Exogenous Factors in Driving Information Propagation Through a Social Network

A novel indicator weight tuning method based on fuzzy theory in mobile social networks

Contact Info

Product

Resources

About