Heterogeneous Information Network Analysis and Applications

Author:   Chuan Shi ,  Philip S. Yu
Publisher:   Springer International Publishing AG
Edition:   Softcover reprint of the original 1st ed. 2017
ISBN:  

9783319858555


Pages:   227
Publication Date:   02 August 2018
Format:   Paperback
Availability:   Manufactured on demand   Availability explained
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Heterogeneous Information Network Analysis and Applications


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Overview

This book offers researchers an understanding of the fundamental issues and a good starting point to work on this rapidly expanding field. It provides a comprehensive survey of current developments of heterogeneous information network. It also presents the newest research in applications of heterogeneous information networks to similarity search, ranking, clustering, recommendation.  This information will help researchers to understand how to analyze networked data with heterogeneous information networks. Common data mining tasks are explored, including similarity search, ranking, and recommendation. The book illustrates some prototypes which analyze networked data. Professionals and academics working in data analytics, networks, machine learning, and data mining will find this content valuable. It is also suitable for advanced-level students in computer science who are interested in networking orpattern recognition. 

Full Product Details

Author:   Chuan Shi ,  Philip S. Yu
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Edition:   Softcover reprint of the original 1st ed. 2017
Weight:   0.454kg
ISBN:  

9783319858555


ISBN 10:   3319858556
Pages:   227
Publication Date:   02 August 2018
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand   Availability explained
We will order this item for you from a manufactured on demand supplier.

Table of Contents

1. Introduction.- 2. Summarization of the developments.- 3.Uniform relevance measure of heterogeneous objects.- 4. Path based Ranking.- 5. Ranking based Clustering.- 6. Recommendation with heterogeneous information.- 7.  Information fusion with heterogeneous network.- 8. Prototype system.- 9. Future research directions.- 10. Conclusion.

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