Most real systems consist of a large number of interacting, multityped components, while most contemporary researches model them as homogeneous information networks, without distinguishing different types of objects and links in the networks. Recently, more and more researchers begin to consider these interconnected, multi-typed data as heterogeneous information networks (HIN), and develop structural analysis approaches by leveraging the rich semantic meaning of structural types of objects and links in the networks. Furthermore, recent advancement on deep learning and network embedding poses new opportunities and challenges to mining HIN, and heterogeneous network embedding, even heterogeneous graph neural network, is becoming a hot topic.
In this tutorial, we will give a survey on recent developments of heterogeneous information network analysis, especially on newly emerging heterogeneous network embedding. It will include the following aspects: (1) basic concepts in this field; (2) meta-path based data mining; (3) HIN embedding; (4) applications; (5) future work. This tutorial shall help researchers and practitioners to share new techniques for identifying and analyzing relationships in networks that integrate multiple types or sources of information.Tutorial Materials Recent Developments of Deep HIN Analysis-Part I:Introduction
Recent Developments of Deep HIN Analysis-Part II:Metapath based DataMining
Recent Developments of Deep HIN Analysis-Part III:Heterogeneous Information Network Embedding
Recent Developments of Deep HIN Analysis-Part IV:Applications
Recent Developments of Deep HIN Analysis-Part V:Conclusion and FutureWork
Chuan Shi, Beijing University of Posts and Telecommunications
Philip Yu, University of Illinois at Chicago
Tel: (312) 996-0498
The topics of this tutorial cover main research directions of social network analysis; and the target audiences are those who are interesting in heterogeneous network mining, representation learning and analysis from both academia and industry. The expected audiences have background on data mining. It is better for them to know social network analysis.
Social network network is a mainstream direction of data mining. Moreover, heterogeneous network mining is becoming a hot topic, and it is also widely used in many applications. The participants will know the recent developments of this direction, and learn to apply them to solve real problems.Outline of the tutorial
In this tutorial, we will give a survey on heterogeneous information network (HIN) analysis and introduce recent developments in this field. It includes the following aspects: (1) basic concepts in this field; (2) meta-path based data mining; (3) HIN embedding; (4) applications; (5) future work. This tutorial shall help researchers and practitioners to share new techniques for identifying and analyzing relationships in networks that integrate multiple types or sources of information.
The tutorial will cover the main and important work in this field. We have collected most of work in my webpage: http://www.shichuan.org/HIN_topic.html. We will select some important and representative work to present in the tutorial.A list of the most important references that will be covered in the tutorial.