A survey on influence maximization in a social network

Banerjee, Suman; Jenamani, Mamata; Pratihar, Dilip Kumar

doi:10.1007/s10115-020-01461-4

Cited by 177 publications

(83 citation statements)

References 144 publications

(159 reference statements)

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“…where S is the chosen cardinality for the seed set. Several algorithms are available in the related literature to approximately solve this problem [31]. In this section we first recall two wellknown approaches (namely the SelectTopS and the Greedy algorithm); subsequently, we present two novel algorithms to effectively find near-optimal solutions to the influence maximization problem in a computationally efficient fashion.…”

Section: Formulation and Resolution Of The Influence Maximizationmentioning

confidence: 99%

On Fast and Effective Influence Spread Maximization in Social Networks

Scarabaggio¹,

Carli²,

Dotoli³

2022

Preprint

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The main characteristic of social networks is their ability to quickly spread information between a large group of people. This phenomenon is generated by the social influence that individuals induce on each other.<br>The widespread use of online social networks (e.g., Facebook) increases researchers' interest in how influence propagates through these networks. One of the most important research issues in this field is the so-called influence maximization problem, which essentially consists in selecting the most influential users (i.e., those who are able to maximize the spread of influence through the social network).<br>Due to its practical importance in various applications (e.g., viral marketing, target advertisement, personalized recommendation), such a problem has been studied in several variants. Different solution methodologies have been proposed. Nevertheless, the current open challenge in the resolution of the influence maximization problem still concerns achieving a good trade-off between accuracy and computational time. <br>In this context, based on the well-known independent cascade and the linear threshold models of social networks, we propose a novel low-complexity and highly accurate algorithm for selecting an initial group of nodes to maximize the spread of influence in large-scale networks. In particular, the key idea consists in iteratively removing the overlap of influence spread induced by different seed nodes. Application to several numerical experiments based on real datasets proves that the proposed algorithm effectively finds practical near-optimal solutions of the addressed influence maximization problem in a computationally efficient fashion. Finally, comparison with the best performing state of the art algorithms demonstrates that in large scale scenarios, the proposed approach shows higher performance in terms of influence spread and running time.

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Section: Formulation and Resolution Of The Influence Maximizationmentioning

confidence: 99%

On Fast and Effective Influence Spread Maximization in Social Networks

Scarabaggio¹,

Carli²,

Dotoli³

2022

Preprint

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“…Researchers have proposed various types of information diffusion models [2,[36][37][38][39][40]. One of the most widely used diffusion models is the independent cascade model, which has been used in most studies [2,5,37,41,42].…”

Section: Diffusion Modelmentioning

confidence: 99%

Influence maximization algorithm based on reducing search space in the social networks

Aghaee

Kianian

2020

SN Appl. Sci.

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The influence maximization problem in social networks is an optimization problem in viral marketing. This problem is concerned with identifying a certain number of people with the most influence in the social network level. Considering the NP-hard of this problem, finding an optimal solution with acceptable accuracy and the low running time is of the high importance. To this purpose, GIN (Group of Influential Nodes) algorithm is presented in this article which creates different groups of graph nodes with more connections than other groups. Then, it selects specific nodes from each group to reduce the search space to find the most influential nodes. Following the greedy method, it selects the seed nodes with the highest expected diffusion value. Experimental results show that the GIN algorithm has provided high influence spread along with low running time in comparison algorithms on all seven real-world datasets.

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“…Thus, the researchers, during years, have been and are seeking manners to develop algorithms that can find nodes of seed set and have appropriate influence spread. These algorithms are categorized as follows [9]:  Approximant algorithms: In this context, approximant algorithms produce high approximant rate and acceptable influence spread, while most of them are not scalable and by growing the size of the network, running time increases severely. Basic Greedy, CELF, CELF++ are among the approximant algorithms.…”

Section: Related Workmentioning

confidence: 99%

Influence Maximization in Social Networks using Deterministic Crowding Algorithm

Kaveh¹,

Bateni²

2019

IJITEE

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In a social network the individuals connected to one another become influenced by one another, while some are more influential than others and able to direct groups of individuals towards a move, an idea and an entity. These individuals are named influential users. Attempt is made by the social network researchers to identify such individuals because by changing their behaviors and ideologies due to communications and the high influence on one another would change many others' behaviors and ideologies in a given community. In information diffusion models, at all stages, individuals are influenced by their neighboring people. These influences and impressions thereof are constructive in an information diffusion process. In the Influence Maximization problem, the goal is to finding a subset of individuals in a social network such that by activating them, the spread of influence is maximized. In this work a new algorithm is presented to identify most influential users under the linear threshold diffusion model. It uses explicit multimodal evolutionary algorithms. Four different datasets are used to evaluate the proposed method. The results show that the precision of our method in average is improved 4.8% compare to best known previous works.

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A survey on influence maximization in a social network

Cited by 177 publications

References 144 publications

On Fast and Effective Influence Spread Maximization in Social Networks

On Fast and Effective Influence Spread Maximization in Social Networks

Influence maximization algorithm based on reducing search space in the social networks

Influence Maximization in Social Networks using Deterministic Crowding Algorithm

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