Mining heterogeneous information networks : principles and methodologies
- Responsibility
- Yizhou Sun and Jiawei Han.
- Imprint
- Cham, Switzerland : Springer, ©2012.
- Physical description
- 1 online resource (xi, 147 pages) : illustrations (some color)
- Series
- Synthesis lectures on data mining and knowledge discovery ; #5.
Online
Available online
More options
Description
Creators/Contributors
- Author/Creator
- Sun, Yizhou.
- Contributor
- Han, Jiawei.
Contents/Summary
- Bibliography
- Includes bibliographical references (pages 139-146).
- Contents
-
- Introduction Ranking-Based Clustering Classification of Heterogeneous Information Networks Meta-Path-Based Similarity Search Meta-Path-Based Relationship Prediction Relation Strength-Aware Clustering with Incomplete Attributes User-Guided Clustering via Meta-Path Selection Research Frontiers.
- (source: Nielsen Book Data)
- Publisher's summary
-
Real-world physical and abstract data objects are interconnected, forming gigantic, interconnected networks. By structuring these data objects and interactions between these objects into multiple types, such networks become semi-structured heterogeneous information networks. Most real-world applications that handle big data, including interconnected social media and social networks, scientific, engineering, or medical information systems, online e-commerce systems, and most database systems, can be structured into heterogeneous information networks. Therefore, effective analysis of large-scale heterogeneous information networks poses an interesting but critical challenge. In this book, we investigate the principles and methodologies of mining heterogeneous information networks. Departing from many existing network models that view interconnected data as homogeneous graphs or networks, our semi-structured heterogeneous information network model leverages the rich semantics of typed nodes and links in a network and uncovers surprisingly rich knowledge from the network. This semi-structured heterogeneous network modeling leads to a series of new principles and powerful methodologies for mining interconnected data, including: (1) rank-based clustering and classification; (2) meta-path-based similarity search and mining; (3) relation strength-aware mining, and many other potential developments. This book introduces this new research frontier and points out some promising research directions.
(source: Nielsen Book Data)
Subjects
- Subjects
- Data mining.
- Information networks.
- Exploration de données (Informatique)
- Réseaux d'information.
- COMPUTERS > Enterprise Applications > Business Intelligence Tools.
- COMPUTERS > Intelligence (AI) & Semantics.
- information network mining
- heterogeneous information networks
- link analysis
- clustering
- classification
- ranking
- similarity search
- relationship prediction
- user-guided clustering
- probabilistic models
- real-world applications
- efficient and scalable algorithms
Bibliographic information
- Publication date
- 2012
- Series
- Synthesis lectures on data mining and knowledge discovery, 2151-0075 ; #5
- ISBN
- 9781608458813 (electronic bk.)
- 1608458814 (electronic bk.)
- 9781608458806 (pbk.)
- 9783031019029 (electronic bk.)
- 3031019024 (electronic bk.)
- DOI
- 10.2200/S00433ED1V01Y201207DMK005
- 10.1007/978-3-031-01902-9
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