Influence maximization in mobile social networks based on RWP-CELF

IF 3.3 3区计算机科学 Q2 COMPUTER SCIENCE, THEORY & METHODS Computing Pub Date : 2024-03-21 DOI:10.1007/s00607-024-01276-z

{"title":"Influence maximization in mobile social networks based on RWP-CELF","authors":"","doi":"10.1007/s00607-024-01276-z","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>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.</p>","PeriodicalId":10718,"journal":{"name":"Computing","volume":"19 1","pages":""},"PeriodicalIF":3.3000,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s00607-024-01276-z","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, THEORY & METHODS","Score":null,"Total":0}

引用次数: 0

Abstract

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.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

基于 RWP-CELF 的移动社交网络中的影响力最大化

摘要信息传播的影响力最大化（IM）问题是移动社交网络中的一个重要课题。传播过程的成功与否取决于有影响力用户的选择机制。除了选择有影响力的用户，该机制还需要考虑计算和运行时间，以确保准确性和高效性。本文考虑到精确计算的开销随数据量的波动呈非线性变化，设计了复杂度变化更平滑的随机算法，结合贪婪算法解决 IM 问题。首先，我们提出了一种名为 "两跳邻居网络影响力估计器 "的方法，用于评估两跳邻居网络中所有节点的影响力。然后，我们开发了一种新颖的贪婪算法，即随机漫步概率高性价比懒惰前向（RWP-CELF）算法，该算法通过对高性价比懒惰前向（CELF）算法进行修改，使用时间比最先进算法少 25-50 个数量级。我们将 RWP-CELF 在真实数据集上的影响扩散效应与理论上证明的近似最优算法进行了比较。实验表明，RWP-CELF 的扩散效果与该算法相当，而运行时间则比该算法低得多。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

Computing 工程技术-计算机：理论方法

CiteScore

8.20

自引率

2.70%

发文量

107

审稿时长

3 months

期刊介绍： Computing publishes original papers, short communications and surveys on all fields of computing. The contributions should be written in English and may be of theoretical or applied nature, the essential criteria are computational relevance and systematic foundation of results.