Target Set Selection in Social Networks with Influence and Activation Thresholds

Qiang, Zhecheng; Pasiliao, Eduardo L.; Zheng, Qipeng P.

doi:10.1007/978-3-030-91434-9_32

Computational Data and Social Networks

2021

DOI: 10.1007/978-3-030-91434-9_32

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Target Set Selection in Social Networks with Influence and Activation Thresholds

Zhecheng Qiang

Eduardo L. Pasiliao

Qipeng P. Zheng

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Target set selection in social networks with tiered influence and activation thresholds

Qiang¹,

Pasiliao²,

Zheng³

2023

J Comb Optim

Self Cite

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Thanks to the mass adoption of internet and mobile devices, users of the social media can seamlessly and spontaneously connect with their friends, followers and followees. Consequently, social media networks have gradually become the major venue for broadcasting and relaying information, and is casting great influences on the people in many aspects of their daily lives. Thus locating those influential users in social media has become crucially important for the successes of many viral marketing, cyber security, politics, and safety-related applications. In this study, we address the problem through solving the tiered influence and activation thresholds target set selection problem, which is to find the seed nodes that can influence the most users within a limited time frame. Both the minimum influential seeds and maximum influence within budget problems are considered in this study. Besides, this study proposes several models exploiting different requirements on seed nodes selection, such as maximum activation, early activation and dynamic threshold. These time-indexed integer program models suffer from the computational difficulties due to the large numbers of binary variables to model influence actions at each time epoch. To address this challenge, this paper designs and leverages several efficient algorithms, i.e., Graph Partition, Nodes Selection, Greedy algorithm, recursive threshold back algorithm and two-stage approach in time, especially for large-scale networks. Computational results show that it is beneficial to apply either the breadth first search or depth first search greedy algorithms for the large instances. In addition, algorithms based on node selection methods perform better in the long-tailed networks.

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Target set selection in social networks with tiered influence and activation thresholds

Qiang¹,

Pasiliao²,

Zheng³

2023

J Comb Optim

Self Cite

View full text Add to dashboard Cite

show abstract

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Target Set Selection in Social Networks with Influence and Activation Thresholds

Cited by 1 publication

References 23 publications

Target set selection in social networks with tiered influence and activation thresholds

Target set selection in social networks with tiered influence and activation thresholds

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