MAHE-IM: Multiple Aggregation of Heterogeneous Relation Embedding for Influence Maximization on Heterogeneous Information Network
文献类型：期刊论文
| 作者 | Li, Ying; Li, Linlin; Liu, Yijun3; Li, Qianqian2,3 |
| 刊名 | EXPERT SYSTEMS WITH APPLICATIONS
 |
| 出版日期 | 2022 |
| 卷号 | 202 |
| 关键词 | Influence maximization Heterogeneous information network Network embedding |
| ISSN号 | 0957-4174 |
| DOI | 10.1016/j.eswa.2022.117289 |
| 文献子类 | Article |
| 英文摘要 | Influence 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. |
| WOS关键词 | SOCIAL NETWORKS ; ALGORITHM |
| WOS研究方向 | Computer Science ; Engineering ; Operations Research & Management Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000806605800005 |
| 源URL | [http://ir.casisd.cn/handle/190111/12033]  |
| 专题 | 系统分析与管理研究所 |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Publ Policy & Management, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Sci & Dev, Beijing 100190, Peoples R China 3.Jilin Univ, Coll Comp Sci & Technol, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Changchun 130012, Peoples R China |
| 推荐引用方式 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). |
入库方式： OAI收割
来源：科技战略咨询研究院
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