Viral Marketing for Multiple Products

Datta, Samik; Majumder, Anirban; Shrivastava, Nisheeth

doi:10.1109/icdm.2010.52

Cited by 55 publications

(40 citation statements)

References 19 publications

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“…Mainly two application scenarios of viral marketing, namely influence maximization and seed minimization, have been extensively studied. Influence maximization [1,2]. The influence maximization problem is a discrete optimization problem [1]: Given a social network ( , ) and an integer , the problem is to find seeds such that the incurred influence is maximized.…”

Section: B Viral Marketingmentioning

confidence: 99%

“…In [2], Datta et al proposed to promote multiple products via marketing in the same social network, where each user is assumed to have a seed constraint which specifies the maximum times of being selected as seeds for the products. Given the quota of the number of the seeds for each product, the problem is to select a set of seeds for each product such that the seed constraint of each user is satisfied and the overall influence incurred by the seeds (for all products) is maximized.…”

Section: B Viral Marketingmentioning

confidence: 99%

“…Thus, in the following, we focus on the influence maximization problem with multiple products. Two algorithms, called Greedy and Fair-Greedy, are designed for the influence maximization problem in [2]. Greedy is a simple greedy algorithm, which repeatedly selects a seed that incurs the largest gain in terms of influence.…”

Section: B Viral Marketingmentioning

confidence: 99%

“…We capture the information diffusion process by a traversal from the seeds on the social network. Same as the existing studies on viral marketing [1][2][3][4], we use Monte-Carlo sampling for estimating the influence incurred in the information diffusion process. Besides, we visualize the information diffusion process in Visual-VM by dynamically highlighting those users when they are first influenced and also those edges through which the information diffuses.…”

Section: Social Network Visualizationmentioning

confidence: 99%

“…Viral marketing [1][2][3][4] is an advertising strategy which utilizes the "word-of-mouth" effect among the friends in social networks. Specifically, instead of covering massive users directly as traditional advertising methods do, viral marketing targets a limited number of initial users (e.g., by providing incentives) and utilizes their social relationships, such as friends, families and co-workers, to further spread the awareness of the product among individuals.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Visual-VM: A Social Network Visualization Tool for Viral Marketing

Long

Wong

2014

2014 IEEE International Conference on Data Mining Workshop

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Abstract-The paper presents Visual-VM, a social network visualization tool, where the main focus is to provide utilities for viral marketing (e.g., influence maximization). Besides, Visual-VM utilizes the location information of each user (which could be estimated from the user's profile) for social network visualization, which is not used in existing social network visualization tools. Visual-VM also supports common utilities for social network exploration. Keywords-Social network visualization, viral marketingI. INTRODUCTION Viral marketing [1][2][3][4] is an advertising strategy which utilizes the "word-of-mouth" effect among the friends in social networks. Specifically, instead of covering massive users directly as traditional advertising methods do, viral marketing targets a limited number of initial users (e.g., by providing incentives) and utilizes their social relationships, such as friends, families and co-workers, to further spread the awareness of the product among individuals. Each individual who gets the awareness of the product is said to be influenced. The number of all influenced individuals corresponds to the influence incurred by the initial users.The propagation process of viral marketing within a social network can be described as follows. At the beginning, the advertiser targets a set of initial users, e.g., by providing some incentives, which we call seeds. Then, the seeds initiate the diffusion process of the product information in the social network. Many models studying how the above diffusion process works have been proposed. Among them, the Independent Cascade Model (IC model) [5] and the Linear Threshold Model (LT model) [6] are the two widely-used models.As mentioned above, in a viral marketing campaign, a company (advertiser) first targets a limited number of seeds and then these seeds would initiate the diffusion process of the product information in the social network. Thus, the most critical issue for viral marketing is to decide which users should be targeted as seeds at the beginning.To answer this question, existing studies on viral marketing mainly consider the following two scenarios.• The budget of how many seeds could be targeted is given, e.g., , and the goal is to maximize the influence resulted from the diffusion process initiated by the seeds. The seed selection problem in this scenario is called influence maximization [1,2].• The influence requirement has been specified, e.g., at least users should be influenced, and the goal is to minimize

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Section: B Viral Marketingmentioning

confidence: 99%

Section: B Viral Marketingmentioning

confidence: 99%

Section: B Viral Marketingmentioning

confidence: 99%