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MAHE-IM: Multiple Aggregation of Heterogeneous Relation Embedding for Influence Maximization on Heterogeneous Information Network
Li, Ying; Li, Linlin; Liu, Yijun1; Li, Qianqian1,2
Source PublicationEXPERT SYSTEMS WITH APPLICATIONS
KeywordInfluence maximization Heterogeneous information network Network embedding
AbstractInfluence maximization (IM), as an essential problem in social network analysis, can identify a minimum group of the influential nodes to maximize the spread of information on the network. The majority of IM studies focus on homogeneous networks, whereas in real world, heterogeneous networks are ubiquitous. The existing IM methods based on homogeneous networks do not consider a variety of complex heterogeneous relationships and the attribute of different types of nodes, which don't accommodate most real scenario. It is of great significance to study IM methods based on heterogeneous networks, where efficient integrating complex multiple semantic relationships and structures embedded in the heterogeneous network is the key breakthrough point.In this paper, a novel deep learning algorithm for influence maximization on heterogeneous networks based on a multiple aggregation of heterogeneous relation embedding named MAHE-IM is proposed, which can capture the heterogeneous high-order structure and semantic features of heterogeneous information networks. For more comprehensive and systematical evaluation of MAHE-IM, we further extend fourteen state-of-the-art homogeneous and heterogeneous network embedding and graph neural network methods for IM problem and propose fourteen extented IM algorithms. Four popular IM algorithms and our extended fourteen IM algorithms are taken as eighteen baseline algorithms, which can be categorized three types: greedy-based, network embedding-based and GNN-based IM algorithms. Compared with eighteen baseline algorithms, the experimental results illustrate that MAHE-IM is significantly efficient and effective. MAHE-IM is more practical on heterogeneous networks. In addition, in order to maximize the convenience for users, a webserver (https://mahe-im.com/) is developed. The users can obtain the IM results by submitting their heterogeneous networks to the webserver. The corresponding source code and used data in this paper can also be available at https://mahe-im.com/ and https://codeocean. com/capsule/4091031/tree/v1.
2022
Volume202
ISSN0957-4174
SubtypeArticle
DOI10.1016/j.eswa.2022.117289
WOS KeywordSOCIAL NETWORKS ; ALGORITHM
Language英语
WOS Research AreaComputer Science ; Engineering ; Operations Research & Management Science
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS IDWOS:000806605800005
Citation statistics
Document Type期刊论文
Identifierhttp://ir.casisd.cn/handle/190111/12033
Collection系统分析与管理研究所
Affiliation1.Jilin Univ, Coll Comp Sci & Technol, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Changchun 130012, Peoples R China
2.Chinese Acad Sci, Inst Sci & Dev, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Sch Publ Policy & Management, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Li, Ying,Li, Linlin,Liu, Yijun,et al. MAHE-IM: Multiple Aggregation of Heterogeneous Relation Embedding for Influence Maximization on Heterogeneous Information Network[J]. EXPERT SYSTEMS WITH APPLICATIONS,2022,202.
APA Li, Ying,Li, Linlin,Liu, Yijun,&Li, Qianqian.(2022).MAHE-IM: Multiple Aggregation of Heterogeneous Relation Embedding for Influence Maximization on Heterogeneous Information Network.EXPERT SYSTEMS WITH APPLICATIONS,202.
MLA Li, Ying,et al."MAHE-IM: Multiple Aggregation of Heterogeneous Relation Embedding for Influence Maximization on Heterogeneous Information Network".EXPERT SYSTEMS WITH APPLICATIONS 202(2022).
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