基于TOPSIS的异质网络影响力最大化

doi:10.6040/j.issn.1672-3961.0.2021.279

山东大学学报 (工学版) ›› 2022, Vol. 52 ›› Issue (2): 31-40.doi: 10.6040/j.issn.1672-3961.0.2021.279

• • 上一篇

基于TOPSIS的异质网络影响力最大化

郭茂林¹,包崇明²,周丽华¹,丁涛³,孔兵^1*

1.云南大学信息学院, 云南昆明 650504;2.云南大学软件学院, 云南昆明 650504;3.云南省科学技术院, 云南昆明 650228

发布日期:2022-04-20
作者简介:郭茂林(1996— ),男,四川自贡人,硕士研究生,主要研究方向为社会网络分析与机器学习. E-mail:guomaolin@mail.ynu.edu.cn. *通信作者简介:孔兵(1968— ),男,云南昆明人,副教授,博士,主要研究方向为社会网络分析与机器学习. E-mail:kongbing@ynu.edu.cn
基金资助:
国家自然科学基金项目(61762090,62062066);云南省重点研发计划(2018IA054);云南省教育厅科学研究基金项目(2019J0005)

Influence maximization of heterogeneous networks based on TOPSIS

GUO Maolin¹, BAO Chongming², ZHOU Lihua¹, DING Tao³, KONG Bing^1*

1. School of Information, Yunnan University, Kunming 650504, Yunnan, China;
2. School of Software, Yunnan University, Kunming 650504, Yunnan, China;
3. Yunnan Provincial Academy of Science and Technology, Kunming 650228, Yunnan, China

Published:2022-04-20

摘要/Abstract

摘要： 为了解决异质网络影响力最大化问题,提出一种通过计算不同元路径的信息熵将异质网络影响力最大化建模为一个多标准决策问题,再使用逼近理想解排序法(technique for order preference by similarity to an ideal solution,TOPSIS)进行求解。为了评估异质网络中的信息扩散,使用线性阈值模型来进行种子集的扩散数量验证。在3个真实世界网络上进行试验,试验结果表明所提算法的扩散数量高于其他算法结果几十到几百个节点不等,运行时间也是线性时常,表明所提算法是确实有效。

关键词: 异质信息网络, 信息扩散, 影响力最大化, 信息熵, 元路径

中图分类号:

TP391

郭茂林,包崇明,周丽华,丁涛,孔兵. 基于TOPSIS的异质网络影响力最大化[J]. 山东大学学报 (工学版), 2022, 52(2): 31-40.

GUO Maolin, BAO Chongming, ZHOU Lihua, DING Tao, KONG Bing. Influence maximization of heterogeneous networks based on TOPSIS[J]. Journal of Shandong University(Engineering Science), 2022, 52(2): 31-40.

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基于TOPSIS的异质网络影响力最大化

Influence maximization of heterogeneous networks based on TOPSIS

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