Heterogeneous Information Network Analysis and Applications

1. Auflage 2017
ISBN: 978-3-319-56211-7
Verlag: Springer International Publishing

Buch, Englisch, 227 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 4853 g

Reihe: Data Analytics

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 or pattern recognition. 

Zielgruppe

Research

Autoren/Hrsg.

Yu, Philip S.

Shi, Chuan

Fachgebiete

• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Data Mining
• Technische Wissenschaften Elektronik | Nachrichtentechnik Nachrichten- und Kommunikationstechnik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Mathematik | Informatik EDV | Informatik Computerkommunikation & -vernetzung

Inhaltsverzeichnis

1.       Introduction

1.1   Basic concepts and definitions

1.2   Comparisons with related concepts

1.3   Example Datasets of HIN

1.4   Why Heterogeneous Information Network Analysis

1.5   Organization of the book

2.       Summarization of the developments

2.1 Similarity search

2.2 Clustering

2.3 Classification

2.4 Link Prediction

2.5 Ranking

2.6 Recommendation

2.7 Information fusion

2.8 Other applications

2.9 Application systems

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