Harnessing Heterogeneous Information Networks: A systematic literature review

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.