Influence maximization in mobile social networks based on RWP-CELF

被引:0
作者
Xu, Zhenyu [1 ,2 ]
Zhang, Xinxin [3 ]
Chen, Mingzhi [1 ,2 ]
Xu, Li [1 ,2 ]
机构
[1] Fujian Normal Univ, Coll Comp & Cyber Secur, Fuzhou, Fujian, Peoples R China
[2] Fujian Normal Univ, Fujian Prov Key Lab Network Secur & Cryptol, Fuzhou, Fujian, Peoples R China
[3] Henan Univ Technol, Sch Artificial Intelligence & Big Data, Zhengzhou, Peoples R China
基金
中国国家自然科学基金;
关键词
Influence maximization; Mobile social network; Two-hop neighbor network influence estimator; Random algorithm; Greedy algorithm; 9103D; STRATEGY; NODES;
D O I
10.1007/s00607-024-01276-z
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Influence maximization (IM) problem for messages propagation is an important topic in mobile social networks. The success of the spreading process depends on the mechanism for selection of the influential user. Beside selection of influential users, the computation and running time should be considered in this mechanism to ensure the accurecy and efficient. In this paper, considering that the overhead of exact computation varies nonlinearly with fluctuations in data size, random algorithm with smoother complexity change was designed to solve the IM problem in combination with greedy algorithm. Firstly, we proposed a method named two-hop neighbor network influence estimator to evaluate the influence of all nodes in the two-hop neighbor network. Then, we developed a novel greedy algorithm, the random walk probability cost-effective with lazy-forward (RWP-CELF) algorithm by modifying cost-effective with lazy-forward (CELF) with random algorithm, which uses 25-50 orders of magnitude less time than the state-of-the-art algorithms. We compared the influence spread effect of RWP-CELF on real datasets with a theoretically proven algorithm that is guaranteed to be approximately optimal. Experiments show that the spread effect of RWP-CELF is comparable to this algorithm, and the running time is much lower than this algorithm.
引用
收藏
页码:1913 / 1931
页数:19
相关论文
共 50 条
[41]   CIM: Community-Based Influence Maximization in Social Networks [J].
Chen, Yi-Cheng ;
Zhu, Wen-Yuan ;
Peng, Wen-Chih ;
Lee, Wang-Chien ;
Lee, Suh-Yin .
ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY, 2014, 5 (02)
[42]   Influence maximization for large social networks [J].
Yue, Feifei ;
Tu, Zhibing ;
Feng, Shengzhong .
INFORMATION SCIENCE AND MANAGEMENT ENGINEERING, VOLS 1-3, 2014, 46 :1823-1830
[43]   Influence maximization on multiple social networks [J].
Li G.-L. ;
Chu Y.-P. ;
Feng J.-H. ;
Xu Y.-Q. .
Jisuanji Xuebao/Chinese Journal of Computers, 2016, 39 (04) :643-656
[44]   Research on the Influence Maximization Problem in Social Networks Based on the Multi-Functional Complex Networks Model [J].
Bin, Sheng ;
Sun, Gengxin .
JOURNAL OF ORGANIZATIONAL AND END USER COMPUTING, 2022, 34 (03)
[45]   Dynamic node influence tracking based influence maximization on dynamic social networks [J].
Chandran, Jyothimon ;
Viswanatham, V. Madhu .
MICROPROCESSORS AND MICROSYSTEMS, 2022, 95
[46]   TPH: A Three-Phase-based Heuristic algorithm for influence maximization in social networks [J].
Jia, Wei ;
Yan, Li ;
Ma, Zongmin ;
Niu, Weinan .
JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2020, 39 (03) :4393-4403
[47]   SpreadMax: A Scalable Cascading Model for Influence Maximization in Social Networks [J].
Cheriyan, Jo ;
Sajeev, G. P. .
2018 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI), 2018, :1290-1296
[48]   A survey on information diffusion and competitive influence maximization in social networks [J].
Solanki, Shano ;
Kumar, Mukesh ;
Kumar, Rakesh .
SOCIAL NETWORK ANALYSIS AND MINING, 2025, 15 (01)
[49]   Conformity-aware influence maximization in online social networks [J].
Hui Li ;
Sourav S. Bhowmick ;
Aixin Sun ;
Jiangtao Cui .
The VLDB Journal, 2015, 24 :117-141
[50]   Conformity-aware influence maximization in online social networks [J].
Li, Hui ;
Bhowmick, Sourav S. ;
Sun, Aixin ;
Cui, Jiangtao .
VLDB JOURNAL, 2015, 24 (01) :117-141