TSIM: A Two-Stage Selection Algorithm for Influence Maximization in Social Networks

Qiu Liqing,Zhang Shuang,Zhang Mingjv,Tian Xiangbo,Gu Chunmei

doi:10.1109/access.2020.2966056

Qiu Liqing, Zhang Shuang + Show 3 more

Open Access

https://doi.org/10.1109/access.2020.2966056

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Journal: IEEE Access	Publication Date: Jan 1, 2020
Citations: 16	License type: CC BY 4.0

Affiliation: Shandong University of Science and Technology

Abstract

The influence maximization problem is aimed at finding a small subset of nodes in a social./network to maximize the expected number of nodes influenced by these nodes. Influence maximization plays an important role in viral marketing and information diffusion. However, some existing algorithms for influence maximization in social networks perform badly in either efficiency or accuracy. In this paper, we put forward an efficient algorithm, called a two-stage selection for influence maximization in social networks (TSIM). Moreover, a discount-degree descending technology and lazy-forward technology are proposed, called DDLF, to select a certain number of influential nodes as candidate nodes. Firstly, we utilize the strategy to select a certain number of nodes as candidate nodes. Secondly, this paper proposes the maximum influence value function to estimate the marginal influence of each candidate node. Finally, we select seed nodes from candidate nodes according to their maximum influence value. The experimental results on six real-world social networks show that the proposed algorithm outperforms other contrast algorithms while considering accuracy and efficiency comprehensively.

Highlights

With the development and popularity of the Internet, billions of people are connected through online social networks, such as Facebook, Twitter and YouTube
The TSIM algorithm focuses on two aspects: Firstly, the TSIM algorithm utilizes the discount-degree descending technology and lazy-forward technology search strategy to select 2k influential nodes as candidate nodes
(2) A new objective function is presented in this paper, called maximum influence value (MIV), to select seed nodes, which improves the accuracy of the TSIM algorithm

Summary

INTRODUCTION

With the development and popularity of the Internet, billions of people are connected through online social networks, such as Facebook, Twitter and YouTube. The greedy algorithm has two obvious drawbacks as follows: (1) it needs to traverse all nodes in social networks; (2) it requires tens of thousands of Monte Carlo simulations to obtain an accurate result. Q. Liqing et al.: TSIM: Two-Stage Selection Algorithm for Influence Maximization in Social Networks of the algorithm, the subsequent heuristic algorithms were presented. We propose an improved algorithm, called TSIM (a two-stage selection algorithm for influence maximization in social networks). The TSIM algorithm focuses on two aspects: Firstly, the TSIM algorithm utilizes the discount-degree descending technology and lazy-forward technology (called DDLF) search strategy to select 2k (where k represents the size of the seed set S) influential nodes as candidate nodes. (2) A new objective function is presented in this paper, called MIV , to select seed nodes, which improves the accuracy of the TSIM algorithm.

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