社会基因——寻找新星的新方法

Zhaolong Ning, Yuqing Liu, Xiangjie Kong
{"title":"社会基因——寻找新星的新方法","authors":"Zhaolong Ning, Yuqing Liu, Xiangjie Kong","doi":"10.1109/ISNCC.2017.8072031","DOIUrl":null,"url":null,"abstract":"Finding rising star in social networks becomes a popular research topic in recent years. Rising star means he or she may be not so charming at the initial time but turns out to be an outstanding star over time. In academic network, rising star means the scholar who just starts his research career with not so many papers published. While in the future, the sum of citations and papers will increase and the scholar will be more outstanding. There are some works of scholarly assessment, however, few works are about finding rising stars. Most of the algorithms of finding rising star are based on random walk on a heterogeneous network constructed by bibliography. These methods need entire information of networks and fit for a long time. In this paper, we propose a method based on “Social Genes”, which are defined as the inside factors of scholars' activities characteristics. We use factor analysis to find the inside factors, calculate the weights by neural network, and make assessments via AHP method. The experiment results on APS dataset show our method is able to find more authors with high rank.","PeriodicalId":176998,"journal":{"name":"2017 International Symposium on Networks, Computers and Communications (ISNCC)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Social gene — A new method to find rising stars\",\"authors\":\"Zhaolong Ning, Yuqing Liu, Xiangjie Kong\",\"doi\":\"10.1109/ISNCC.2017.8072031\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Finding rising star in social networks becomes a popular research topic in recent years. Rising star means he or she may be not so charming at the initial time but turns out to be an outstanding star over time. In academic network, rising star means the scholar who just starts his research career with not so many papers published. While in the future, the sum of citations and papers will increase and the scholar will be more outstanding. There are some works of scholarly assessment, however, few works are about finding rising stars. Most of the algorithms of finding rising star are based on random walk on a heterogeneous network constructed by bibliography. These methods need entire information of networks and fit for a long time. In this paper, we propose a method based on “Social Genes”, which are defined as the inside factors of scholars' activities characteristics. We use factor analysis to find the inside factors, calculate the weights by neural network, and make assessments via AHP method. The experiment results on APS dataset show our method is able to find more authors with high rank.\",\"PeriodicalId\":176998,\"journal\":{\"name\":\"2017 International Symposium on Networks, Computers and Communications (ISNCC)\",\"volume\":\"15 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 International Symposium on Networks, Computers and Communications (ISNCC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ISNCC.2017.8072031\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 International Symposium on Networks, Computers and Communications (ISNCC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ISNCC.2017.8072031","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 8

摘要

近年来,在社交网络中寻找新星成为一个热门的研究课题。冉冉升起的明星是指他或她一开始可能不那么迷人,但随着时间的推移,他或她会成为一颗耀眼的明星。在学术网络中,后起之秀是指刚刚开始研究生涯,发表论文不多的学者。而在未来,引文和论文的数量将会增加,学者将会更加优秀。有一些学术评价的作品,但很少有作品是关于寻找新星的。大多数寻找新星的算法都是基于书目构建的异构网络上的随机漫步。这些方法需要网络的全部信息,适合时间较长。本文提出了一种基于“社会基因”的方法,将社会基因定义为学者活动特征的内部因素。利用因子分析法寻找内部因素,利用神经网络计算权重,运用层次分析法进行评价。在APS数据集上的实验结果表明,我们的方法能够找到更多高排名的作者。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Social gene — A new method to find rising stars
Finding rising star in social networks becomes a popular research topic in recent years. Rising star means he or she may be not so charming at the initial time but turns out to be an outstanding star over time. In academic network, rising star means the scholar who just starts his research career with not so many papers published. While in the future, the sum of citations and papers will increase and the scholar will be more outstanding. There are some works of scholarly assessment, however, few works are about finding rising stars. Most of the algorithms of finding rising star are based on random walk on a heterogeneous network constructed by bibliography. These methods need entire information of networks and fit for a long time. In this paper, we propose a method based on “Social Genes”, which are defined as the inside factors of scholars' activities characteristics. We use factor analysis to find the inside factors, calculate the weights by neural network, and make assessments via AHP method. The experiment results on APS dataset show our method is able to find more authors with high rank.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信