On the optimal solution of budgeted influence maximization problem in social networks

Güney, Evren

doi:10.1007/s12351-017-0305-x

Cited by 20 publications

(17 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Recently, Wang and Yu [2020] studied the BIM Problem and proposed a solution methodology that gives an approximation ratio of 1 2 (1 − 1 e ). They further showed that this can be improved upto Güney [2019] proposed an integer programming-based approach to solve this problem under the IC Model of diffusion. Han et al [2014] proposed a couple of heuristics for this problem that carefully considers both cost effective nodes and influential nodes.…”

Section: Related Workmentioning

confidence: 99%

ComBIM: A community-based solution approach for the Budgeted Influence Maximization Problem

Banerjee

Jenamani

Pratihar

2019

Expert Systems with Applications

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

ComBIM: A community-based solution approach for the Budgeted Influence Maximization Problem

Banerjee

Jenamani

Pratihar

2019

Expert Systems with Applications

View full text Add to dashboard Cite

“…erefore, researchers have proposed many improved models to improve the efficiency of influence maximization [13][14][15]. Banerjee et al [16] designed a new heuristic algorithm for the prefix elimination maximum influence tree (PMIA) model and proposed the LDAG algorithm of the LT model.…”

Section: Introductionmentioning

confidence: 99%

Channel Optimization of Marketing Based on Users’ Social Network Information

Peng

2020

Complexity

View full text Add to dashboard Cite

Marketing in the social network environment integrates current advanced internet and information technologies. This marketing method not only broadens marketing channels and builds a network communication platform but also meets the purchase needs of customers in the entire market and shortens customer purchases. The process is also an inevitable product of the development of the times. However, when companies use social networks for product marketing, they usually face the impact of multiple realistic factors. This article takes the maximization of influence as the main idea to find seed users for product information dissemination and also considers the users’ interest preferences. The target users can influence the product, and the company should control marketing costs to obtain a larger marginal benefit. Based on this, this paper considers factors such as the scale of information diffusion, user interest preferences, and corporate budgets, takes the influence maximization model as a multiobjective optimization problem, and proposes a multiobjective maximization of influence (MOIM) model. To solve the NP-hard problem of maximizing influence, this paper uses Monte Carlo sampling to calculate high-influence users. Next, a seed user selection algorithm based on NSGA-II is proposed to optimize the above three objective functions and find the optimal solution. We use real social network data to verify the performance of models and methods. Experiments show that the proposed model can generate appropriate seed sets and can meet different purposes of information dissemination. Sensitivity analysis proves that our model is robust under different actual conditions.

show abstract

“…Recently, a variant of this problem has been introduced by Nguyen and Zheng Nguyen and Zheng [2013], where the users of the network are associated with a selection cost and the seed set selection is to be done within an allocated budget to maximize the influence in the network. There are a few solution methodologies of the problem such as directed acyclic graph (DAG)-based heuristics and (1− 1 √ e )-factor approximation algorithm by Nguyen and Zheng [2013], balanced seed selection heuristics by Han et al [2014], integer programming-based approach Güney [2017], community-based solution approach by Banerjee et al [2019a]. In all these studies it is implicitly assumed that irrespective of the context, influence probability between two users will be the same, i.e., there is a single influence probability associated with every edge.…”

Section: Introductionmentioning

confidence: 99%

Budgeted Influence Maximization with Tags in Social Networks

Banerjee

Pal

Jenamani

2020

Web Information Systems Engineering – WISE 2020

View full text Add to dashboard Cite

Given a social network, where each user is associated with a selection cost, the problem of Budgeted Influence Maximization (BIM Problem in short) asks to choose a subset of them (known as seed users) within an allocated budget whose initial activation leads to the maximum number of influenced nodes.Existing Studies on this problem do not consider the tag-specific influence probability. However, in reality, influence probability between two users always depends upon the context (e.g., sports, politics, etc.). To address this issue, in this paper we introduce the Tag-Based Budgeted Influence Maximization problem (TBIM Problem in short), where along with the other inputs, a tag set (each of them is also associated with a selection cost) is given, each edge of the network has the tag specific influence probability, and here the goal is to select influential users as well as influential tags within the allocated budget to maximize the influence. Considering the fact that real-world campaigns targeted in nature, we also study the Earned Benefit Maximization Problem in tag specific influence probability setting, which formally we call the Tag-Based $ A part of this paper has been previously published as Banerjee et al. [2020c]

show abstract

On the optimal solution of budgeted influence maximization problem in social networks

Cited by 20 publications

References 20 publications

ComBIM: A community-based solution approach for the Budgeted Influence Maximization Problem

ComBIM: A community-based solution approach for the Budgeted Influence Maximization Problem

Channel Optimization of Marketing Based on Users’ Social Network Information

Budgeted Influence Maximization with Tags in Social Networks

Contact Info

Product

Resources

About