Open Access

ARTICLE

A Positive Influence Maximization Algorithm in Signed Social Networks

by Wenlong Zhu^1,2,*, Yang Huang¹, Shuangshuang Yang³, Yu Miao¹, Chongyuan Peng¹

1 College of Computer and Control Engineering, Qiqihar University, Qiqihar, 161006, China
2 Heilongjiang Key Laboratory of Big Data Network Security Detection and Analysis, Qiqihar University, Qiqihar, 161006, China
3 College of Teacher Education, Qiqihar University, Qiqihar, 161006, China

* Corresponding Author: Wenlong Zhu. Email:

Computers, Materials & Continua 2023, 76(2), 1977-1994. https://doi.org/10.32604/cmc.2023.040998

Received 07 April 2023; Accepted 09 June 2023; Issue published 30 August 2023

Abstract

The influence maximization (IM) problem aims to find a set of seed nodes that maximizes the spread of their influence in a social network. The positive influence maximization (PIM) problem is an extension of the IM problem, which consider the polar relation of nodes in signed social networks so that the positive influence of seeds can be the most widely spread. To solve the PIM problem, this paper proposes the polar and decay related independent cascade (IC-PD) model to simulate the influence propagation of nodes and the decay of information during the influence propagation in signed social networks. To overcome the low efficiency of the greedy based algorithm, this paper defines the polar reverse reachable (PRR) set and devises a signed reverse influence sampling (SRIS) algorithm. The algorithm utilizes the IC-PD model as well as the PRR set to select seeds. There are two phases in SRIS. One is the sampling phase, which utilizes the IC-PD model to generate the PRR set and a binary search algorithm to calculate the number of needed PRR sets. The other is the node selection phase, which uses a greedy coverage algorithm to select optimal seeds. Finally, Experiments on three real-world polar social network datasets demonstrate that SRIS outperforms the baseline algorithms in effectiveness. Especially on the Slashdot dataset, SRIS achieves 24.7% higher performance than the best-performing compared algorithm under the weighted cascade model when the seed set size is 25.

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Keywords

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