Article: Time constraint influence maximization algorithm in the age of big data Journal: International Journal of Computational Science and Engineering (IJCSE) 2017 Vol.15 No.3/4 pp.165 - 175 Abstract: The new generation of social networks contains billions of nodes and edges. Managing and mining this data is a new academic and industrial challenge. Influence maximization is the problem of finding a set of nodes in a social network that result in the highest amount of influence diffusion. Many research works have been developed, which focus exclusively on the efficiency of algorithms, but overlook some features of social network data such as time sensitivity and the practicality in a large scale. Furthermore, the new era of 'big data' is changing dramatically right before our eyes - the increase of big data growth gives all researchers many challenges as well as opportunities. This paper proposes two new models TIC and TLT and considers the time constraint during the influence spreading process in practice. Empirical studies on different synthetic and real large scale social networks demonstrate that our models together with solutions on both Hadoop and Spark platforms are more practical as well as providing a regulatory mechanism for enhancing influence maximization. Not only that but also outperforming most existing alternative algorithms. Inderscience Publishers - linking academia, business and industry through research

Title: Time constraint influence maximization algorithm in the age of big data

Authors: Meng Han; Zhuojun Duan; Chunyu Ai; Forrest Wong Lybarger; Yingshu Li; Anu G. Bourgeois

Addresses: The Department of Computer Science, Georgia State University, Atlanta, Georgia, 30303, USA ' The Department of Computer Science, Georgia State University, Atlanta, Georgia, 30303, USA ' Division of Mathematics and Computer Science, University of South Carolina Upstate, Spartanburg, South Carolina, 29303, USA ' The Department of Computer Science, Georgia State University, Atlanta, Georgia, 30303, USA ' The Department of Computer Science, Georgia State University, Atlanta, Georgia, 30303, USA ' The Department of Computer Science, Georgia State University, Atlanta, Georgia, 30303, USA

Abstract: The new generation of social networks contains billions of nodes and edges. Managing and mining this data is a new academic and industrial challenge. Influence maximization is the problem of finding a set of nodes in a social network that result in the highest amount of influence diffusion. Many research works have been developed, which focus exclusively on the efficiency of algorithms, but overlook some features of social network data such as time sensitivity and the practicality in a large scale. Furthermore, the new era of 'big data' is changing dramatically right before our eyes - the increase of big data growth gives all researchers many challenges as well as opportunities. This paper proposes two new models TIC and TLT and considers the time constraint during the influence spreading process in practice. Empirical studies on different synthetic and real large scale social networks demonstrate that our models together with solutions on both Hadoop and Spark platforms are more practical as well as providing a regulatory mechanism for enhancing influence maximization. Not only that but also outperforming most existing alternative algorithms.

Keywords: influence maximization; cloud computing; data mining; data modelling.

DOI: 10.1504/IJCSE.2017.087401

International Journal of Computational Science and Engineering, 2017 Vol.15 No.3/4, pp.165 - 175

Received: 11 Mar 2016
Accepted: 30 Jul 2016
Published online: 15 Oct 2017 *

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Title: Time constraint influence maximization algorithm in the age of big data

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