Conformity-aware influence maximization in online social networks

2014 ◽  
Vol 24 (1) ◽  
pp. 117-141 ◽  
Author(s):  
Hui Li ◽  
Sourav S. Bhowmick ◽  
Aixin Sun ◽  
Jiangtao Cui
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Influence Maximization

Topic based time-sensitive influence maximization in online social networks

2020 ◽  
Vol 23 (3) ◽  
pp. 1831-1859 ◽  
Author(s):  
Huiyu Min ◽  
Jiuxin Cao ◽  
Tangfei Yuan ◽  
Bo Liu
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Influence Maximization

scholarly journals Multiplex Influence Maximization in Online Social Networks With Heterogeneous Diffusion Models

2018 ◽  
Vol 5 (2) ◽  
pp. 418-429 ◽  
Author(s):  
Alan Kuhnle ◽  
Md Abdul Alim ◽  
Xiang Li ◽  
Huiling Zhang ◽  
My T. Thai
Keyword(s):  
Social Networks ◽  
Online Social Networks ◽  
Influence Maximization ◽  
Diffusion Models

Better approximation algorithms for influence maximization in online social networks

2013 ◽  
Vol 30 (1) ◽  
pp. 97-108 ◽  
Author(s):  
Yuqing Zhu ◽  
Weili Wu ◽  
Yuanjun Bi ◽  
Lidong Wu ◽  
Yiwei Jiang ◽  
...  
Keyword(s):  
Social Networks ◽  
Approximation Algorithms ◽  
Online Social Networks ◽  
Influence Maximization

Parallel Greedy Algorithm to Multiple Influence Maximization in Social Network

2021 ◽  
Vol 15 (3) ◽  
pp. 1-21
Author(s):  
Guanhao Wu ◽  
Xiaofeng Gao ◽  
Ge Yan ◽  
Guihai Chen
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Parallel Algorithms ◽  
Real World ◽  
Online Social Networks ◽  
Heuristic Algorithms ◽  
Influence Maximization ◽  
Memory Overhead ◽  
Influence Spread ◽  
Real World Applications

Influence Maximization (IM) problem is to select influential users to maximize the influence spread, which plays an important role in many real-world applications such as product recommendation, epidemic control, and network monitoring. Nowadays multiple kinds of information can propagate in online social networks simultaneously, but current literature seldom discuss about this phenomenon. Accordingly, in this article, we propose Multiple Influence Maximization (MIM) problem where multiple information can propagate in a single network with different propagation probabilities. The goal of MIM problems is to maximize the overall accumulative influence spreads of different information with the limit of seed budget . To solve MIM problems, we first propose a greedy framework to solve MIM problems which maintains an -approximate ratio. We further propose parallel algorithms based on semaphores, an inter-thread communication mechanism, which significantly improves our algorithms efficiency. Then we conduct experiments for our framework using complex social network datasets with 12k, 154k, 317k, and 1.1m nodes, and the experimental results show that our greedy framework outperforms other heuristic algorithms greatly for large influence spread and parallelization of algorithms reduces running time observably with acceptable memory overhead.

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