An approach of cost optimized influence maximization in social networks

Talukder, Ashis; Alam, Md. Golam Rabiul; Bairagi, Anupam Kumar; Abedin, Sarder Fakhrul; Layek, Md. Abu; Nguyễn, Hoàng Tùng; Hong, Choong Seon

doi:10.1109/apnoms.2017.8094146

Cited by 8 publications

(15 citation statements)

References 9 publications

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“…Again, some Reverse Influence Maximization (RIM) models are available in the literature to optimize the seeding cost [24]- [26]. These studies also mention some challenges of the RIM models; however, the models are incapable of resolving the challenges and providing the optimal seeding cost simultaneously.…”

Section: A Background and Motivationmentioning

confidence: 99%

“…The authors in [24] introduce the Reverse Influence Maximization (RIM) problem in which the IM technique is applied in the reverse direction. The RIM model computes the seeding cost, which is the cost of activating initial seed nodes.…”

Section: Reverse Influence Maximization Modelsmentioning

confidence: 99%

“…• RLT-RIM: The model also chooses nodes randomly; however, it activates the nodes by the LT model [24].…”

Section: Comparative Algorithmsmentioning

confidence: 99%

“…The inefficient influence happens when all the in-neighbors together can not influence the target node and is mathematically given by [3], [24],…”

Section: ) Insufficient Influencementioning

confidence: 99%

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CM2D: Cost Minimization Under the Competitive Market in Dynamic Social Networks

Talukder

Hong

2020

IEEE Access

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The Reverse Influence Maximization (RIM) model deals with the viral marketing cost minimization in social networks. On the other hand, the Influence maximization (IM) technique finds the small number of influential users that maximize the viral marketing profit. Here, the profit is defined by the maximum number of nodes that can be influenced by seed users when they are initially activated. On the other hand, the cost is measured by the minimum number of nodes required to activate all the seed nodes. However, most of the existing studies focus on profit maximization without considering the seeding cost. Moreover, the most profitable node may not always be a cost-effective one. Thus, in this research, we introduce an epidemic Cost Minimization model under the Competitive Market in Dynamic social networks (CMCMD, i.e., CM2D). In the CM2D model, we modify the Susceptible-Infected-Recovered (SIR) technique as the Reverse SIR (RSIR) model to employ in the node activation process. The proposed CM2D model also uses the greedy Set Cover approximation technique to optimize the seeding cost. Moreover, the CM2D model tackles the challenging issues of the RIM problem more efficiently than the existing models. Finally, we simulate our model using datasets of two synthesized and four real datasets, and the results show that the CM2D model outperforms the existing models.

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Section: A Background and Motivationmentioning

confidence: 99%

Section: Reverse Influence Maximization Modelsmentioning

confidence: 99%

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CM2D: Cost Minimization Under the Competitive Market in Dynamic Social Networks

Talukder

Hong

2020

IEEE Access

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“…Therefore, Reverse Influence Maximization (RIM) models are introduced to find the seeding cost of viral marketing in which the models diffuse influence reversely as compared to the traditional one [19], [20]. In general, the IM examines who are activated by the seed nodes whereas, the RIM investigates by whom the seed nodes are activated.…”

Section: A Background and Motivationmentioning

confidence: 99%

Knapsack-Based Reverse Influence Maximization for Target Marketing in Social Networks

et al. 2019

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With the dramatic proliferation in recent years, the social networks have become a ubiquitous medium of marketing and the influence maximization (IM) technique, being such a viral marketing tool, has gained significant research interest in recent years. The IM determines the influential users who maximize the profit defined by the maximum number of nodes that can be activated by a given seed set. However, most of the existing IM studies do not focus on estimating the seeding cost which is identified by the minimum number of nodes that must be activated in order to influence the given seed set. They either assume the seed nodes are initially activated, or some free products or services are offered to activate the seed nodes. However, seed users might also be activated by some other influential users, and thus, the reverse influence maximization (RIM) models have been proposed to find the seeding cost of target marketing. However, the existing RIM models are incapable of resolving the challenging issues and providing better seeding cost simultaneously. Therefore, in this paper, we propose a Knapsack-based solution (KRIM) under linear threshold (LT) model which not only resolves the RIM challenges efficiently, but also yields optimized seeding cost. The experimental results on both the synthesized and real datasets show that our model performs better than existing RIM models concerning estimated seeding cost, running time, and handling RIM-challenges.

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Threshold Estimation Models for Linear Threshold-Based Influential User Mining in Social Networks

et al. 2019

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Influence Maximization (IM) is a popular social network mining mechanism that mines influential users for viral marketing in social networks. Most of the Influence Maximization techniques employ either the independent cascade (IC) or linear threshold (LT) model in the node activation process. In the IC model, all the active in-neighbors are given a single chance to activate a node with a particular probability whereas, in the LT model, a node is activated if the aggregated influence of all the activated in-neighbors is no less than a threshold value. Thus, the threshold plays a significant role in the LT-based influence maximization. In this paper, we comprehensively survey the different threshold values used in various IM models. Based on the survey, we observe that the current studies lack threshold estimation models. Therefore, we develop a system model and propose four threshold estimation models based on influenceweight and degree distribution. The empirical results show that our algorithms generate threshold values that resemble the thresholds used by most IM algorithms along with faster running time. Besides, the proposed models are scalable and applicable to any influence-weight estimation technique and offer narrower threshold ranges rather than the broad ranges used in many existing works. INDEX TERMSInfluence maximization, threshold estimation, linear threshold model, unique threshold model, random individual threshold model, social network mining, viral marketing.

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An approach of cost optimized influence maximization in social networks

Cited by 8 publications

References 9 publications

CM2D: Cost Minimization Under the Competitive Market in Dynamic Social Networks

CM2D: Cost Minimization Under the Competitive Market in Dynamic Social Networks

Knapsack-Based Reverse Influence Maximization for Target Marketing in Social Networks

Threshold Estimation Models for Linear Threshold-Based Influential User Mining in Social Networks

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