{"title":"Harnessing Heterogeneous Information Networks: A systematic literature review","authors":"Leila Outemzabet , Nicolas Gaud , Aurélie Bertaux , Christophe Nicolle , Stéphane Gerart , Sébastien Vachenc","doi":"10.1016/j.cosrev.2024.100633","DOIUrl":null,"url":null,"abstract":"<div><p>The integration of multiple heterogeneous data into graph models has been the subject of extensive research in recent years. Harnessing these resulting Heterogeneous Information Networks (HINs) is a complex task that requires reasoning to perform various prediction tasks.</p><p>In the last decade, multiple Artificial Intelligence (AI) approaches have been developed to bridge the gap between the abundance of diverse data within various fields, their heterogeneity and complexity within HINs. A focus has been directed on developing graph-oriented algorithms that can effectively analyze and leverage the rich information in HINs.</p><p>Given the sheer volume of approaches being developed, selecting the most suitable one for a specific objective has become a daunting challenge. This article reviews the recent advances in AI methods for modeling and analyzing HINs. It proposes a cartography of these approaches, structured as a pipeline, offering diverse options at each stage. This structured framework aims to guide practitioners in choosing the most fitting methods based on the nature of their data and specific objectives.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"52 ","pages":"Article 100633"},"PeriodicalIF":13.3000,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Science Review","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1574013724000170","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
The integration of multiple heterogeneous data into graph models has been the subject of extensive research in recent years. Harnessing these resulting Heterogeneous Information Networks (HINs) is a complex task that requires reasoning to perform various prediction tasks.
In the last decade, multiple Artificial Intelligence (AI) approaches have been developed to bridge the gap between the abundance of diverse data within various fields, their heterogeneity and complexity within HINs. A focus has been directed on developing graph-oriented algorithms that can effectively analyze and leverage the rich information in HINs.
Given the sheer volume of approaches being developed, selecting the most suitable one for a specific objective has become a daunting challenge. This article reviews the recent advances in AI methods for modeling and analyzing HINs. It proposes a cartography of these approaches, structured as a pipeline, offering diverse options at each stage. This structured framework aims to guide practitioners in choosing the most fitting methods based on the nature of their data and specific objectives.
近年来,将多种异构数据整合到图模型中一直是广泛研究的主题。在过去的十年中,人们开发了多种人工智能(AI)方法,以弥补各领域丰富多样的数据与 HIN 中的异构性和复杂性之间的差距。鉴于开发的方法数量庞大,为特定目标选择最合适的方法已成为一项艰巨的挑战。本文回顾了用于 HINs 建模和分析的人工智能方法的最新进展。文章提出了这些方法的结构图,作为一个流水线,在每个阶段提供不同的选择。这一结构化框架旨在指导从业人员根据数据性质和具体目标选择最合适的方法。
期刊介绍:
Computer Science Review, a publication dedicated to research surveys and expository overviews of open problems in computer science, targets a broad audience within the field seeking comprehensive insights into the latest developments. The journal welcomes articles from various fields as long as their content impacts the advancement of computer science. In particular, articles that review the application of well-known Computer Science methods to other areas are in scope only if these articles advance the fundamental understanding of those methods.