[1]杨宇迪,周丽华,杜国王,等.异质信息网络中基于网络嵌入的影响力最大化[J].智能系统学报,2021,16(4):757-765.[doi:10.11992/tis.202009047]
 YANG Yudi,ZHOU Lihua,DU Guowang,et al.Influence maximization based on network embedding in heterogeneous information networks[J].CAAI Transactions on Intelligent Systems,2021,16(4):757-765.[doi:10.11992/tis.202009047]
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异质信息网络中基于网络嵌入的影响力最大化(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第16卷
期数:
2021年4期
页码:
757-765
栏目:
学术论文—知识工程
出版日期:
2021-07-05

文章信息/Info

Title:
Influence maximization based on network embedding in heterogeneous information networks
作者:
杨宇迪1 周丽华12 杜国王1 邹星竹1 丁海燕1
1. 云南大学 信息学院,云南 昆明 650504;
2. 云南大学 滇池学院,云南 昆明 650228
Author(s):
YANG Yudi1 ZHOU Lihua12 DU Guowang1 ZOU Xingzhu1 DING Haiyan1
1. School of Information, Yunnan University, Kunming 650504, China;
2. Dianchi College, Yunnan University, Kunming 650228, China
关键词:
异质信息网络同质信息网络影响力最大化信息扩散网络嵌入直接影响力间接影响力全局影响力
Keywords:
heterogeneous information networkhomogeneous information networkinfluence maximizationinformation diffusionnetwork embeddingdirect influenceindirect influenceglobal influence
分类号:
TP391
DOI:
10.11992/tis.202009047
摘要:
针对当前大部分影响力最大化算法忽略了异质信息网络包含多种节点类型和多种关系类型,且不同类型节点在原始空间无法直接度量的问题,提出了一种异质信息网络中基于网络嵌入的影响力最大化模型(influence maximization based on network embedding,IMNE),用于选择初始扩散节点实现影响力最大化。该模型不仅可以在对异质信息网络进行编码的同时表征异质信息网络中潜在的信息,还可以捕获不同类型节点间影响力的不确定和复杂性。在3个真实数据集上的实验验证了IMNE算法的有效性。
Abstract:
Most current influence maximization algorithms ignore the problem that heterogeneous information networks contain multiple node types and relationship types, and different types of nodes cannot be measured in the original workspace. Accordingly, to solve these issues, this paper proposes a novel model for influence maximization based on network embedding in heterogeneous information networks, which helps to realize influence maximization by choosing initial diffusion nodes. The model can not only manifest the potential information in heterogeneous information networks while encoding it but also capture the uncertainty and complexity of influence among different types of nodes. Experimental results on three real datasets demonstrate the effectiveness of the proposed model.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2020-09-30。
基金项目:国家自然科学基金项目(61762090,62062066,61966036);国家社会科学基金项目(18XZZ005);云南省高等学校科技创新团队项目(IRTSTYN);云南省教育厅科学研究基金项目(2021Y026)
作者简介:杨宇迪,硕士研究生,主要研究方向为社会网络分析、数据挖掘;周丽华,教授,博士生导师,CCF会员,主要研究方向为数据挖掘、多视角学习、异质社交网络分析。主持国家自然科学基金项目3项、云南省重点基金项目1项。发表学术论文80余篇,出版学术著作2部;杜国王,博士研究生,主要研究方向为数据挖掘、多视角聚类
通讯作者:周丽华.E-mail:lhzhou@ynu.edu.cn
更新日期/Last Update: 1900-01-01