Predicting kills in Game of Thrones using network analysis

J. Stavanja, Matej Klemen, L. Šubelj
{"title":"Predicting kills in Game of Thrones using network analysis","authors":"J. Stavanja, Matej Klemen, L. Šubelj","doi":"10.31449/upinf.vol28.num2.79","DOIUrl":null,"url":null,"abstract":"TV series such as HBO’s Game of Thrones have a high number of dedicated followers, mostly due to the dramatic murders of the most important characters. In our work, we try to predict killer and victim pairs using data on previous kills as well as additionalmetadata. We construct a network where two character nodes are linked if one killed the other, then use a link prediction framework to evaluate different techniques for kill predictions. Lastly, we compute various network properties on a social network of characters anduse them as features in conjunction with classic data mining techniques. Due to the small size of the dataset and the somewhatrandom kill distribution, we cannot make accurate predictions with standard indices alone, although using them in conjunction with additional rules based on degrees has yielded results that are more reliable. The features we compute on the social network help theclassic machine learning approaches; however, they do not yield very accurate predictions. The best results overall are achieved using indices that use simple degree information, the best of which result in the Area Under the ROC Curve of 0.875.","PeriodicalId":393713,"journal":{"name":"Uporabna informatika","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Uporabna informatika","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.31449/upinf.vol28.num2.79","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

TV series such as HBO’s Game of Thrones have a high number of dedicated followers, mostly due to the dramatic murders of the most important characters. In our work, we try to predict killer and victim pairs using data on previous kills as well as additionalmetadata. We construct a network where two character nodes are linked if one killed the other, then use a link prediction framework to evaluate different techniques for kill predictions. Lastly, we compute various network properties on a social network of characters anduse them as features in conjunction with classic data mining techniques. Due to the small size of the dataset and the somewhatrandom kill distribution, we cannot make accurate predictions with standard indices alone, although using them in conjunction with additional rules based on degrees has yielded results that are more reliable. The features we compute on the social network help theclassic machine learning approaches; however, they do not yield very accurate predictions. The best results overall are achieved using indices that use simple degree information, the best of which result in the Area Under the ROC Curve of 0.875.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用网络分析预测《权力的游戏》中的杀戮
像HBO的《权力的游戏》这样的电视连续剧有很多忠实的粉丝,主要是因为最重要的角色的戏剧性谋杀。在我们的工作中,我们尝试使用以前的杀戮数据以及额外的元数据来预测凶手和受害者的配对。我们构建了一个网络,如果其中一个杀死了另一个,则两个角色节点连接在一起,然后使用链接预测框架来评估不同的杀死预测技术。最后,我们在角色的社交网络上计算各种网络属性,并将它们作为特征与经典数据挖掘技术相结合。由于数据集的规模较小,并且杀戮分布有些随机,我们不能单独使用标准指标做出准确的预测,尽管将它们与基于程度的附加规则结合使用会产生更可靠的结果。我们在社交网络上计算的特征有助于经典的机器学习方法;然而,它们并不能产生非常准确的预测。使用简单度信息的指标总体上取得了最好的结果,其中最好的结果是ROC曲线下面积为0.875。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Analiza uporabe aplikacije za sledenje stikov med mladimi: študija primera Nemčije ONLINE NOTES: sistem za razpoznavo govora in strojno prevajanje v realnem času na ravni univerzitetnih predavanj Metodologije za kvalitativno vrednotenje kakovosti odprtih podatkov Iz Islovarja Digitalne kompetence slovenskih študentov
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1