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Influence Maximization in Social Network Considering Memory Effect and Social Reinforcement Effect

Department of Information Science and Engineering, Shandong Normal University, Jinan 250357, China
Department of Information Science and Electrical Engineering, Shandong Jiaotong University, Jinan 250357, China
Department of Accounting, Shandong Institute of Management, Jinan 250357, China
Author to whom correspondence should be addressed.
Future Internet 2019, 11(4), 95;
Received: 13 February 2019 / Revised: 31 March 2019 / Accepted: 8 April 2019 / Published: 11 April 2019
(This article belongs to the Special Issue Multi-Agent Systems for Social Media Analysis)
PDF [1979 KB, uploaded 25 April 2019]


Social networks have attracted a lot of attention as novel information or advertisement diffusion media for viral marketing. Influence maximization describes the problem of finding a small subset of seed nodes in a social network that could maximize the spread of influence. A lot of algorithms have been proposed to solve this problem. Recently, in order to achieve more realistic viral marketing scenarios, some constrained versions of influence maximization, which consider time constraints, budget constraints and so on, have been proposed. However, none of them considers the memory effect and the social reinforcement effect, which are ubiquitous properties of social networks. In this paper, we define a new constrained version of the influence maximization problem that captures the social reinforcement and memory effects. We first propose a novel propagation model to capture the dynamics of the memory and social reinforcement effects. Then, we modify two baseline algorithms and design a new algorithm to solve the problem under the model. Experiments show that our algorithm achieves the best performance with relatively low time complexity. We also demonstrate that the new version captures some important properties of viral marketing in social networks, such as such as social reinforcements, and could explain some phenomena that cannot be explained by existing influence maximization problem definitions. View Full-Text
Keywords: influence maximization; viral marketing; social network analysis influence maximization; viral marketing; social network analysis

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Wang, F.; Zhu, Z.; Liu, P.; Wang, P. Influence Maximization in Social Network Considering Memory Effect and Social Reinforcement Effect. Future Internet 2019, 11, 95.

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