Mining heterogeneous information networks : principles and methodologies

著者

書誌事項

Mining heterogeneous information networks : principles and methodologies

Yizhou Sun and Jiawei Han

(Synthesis lectures on data mining and knowledge discovery, #5)

Morgan & Claypool Publishers, c2012

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注記

Includes bibliographical references (p. 139-146)

内容説明・目次

内容説明

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.

目次

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

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詳細情報

  • NII書誌ID(NCID)
    BB10657997
  • ISBN
    • 9781608458806
  • 出版国コード
    us
  • タイトル言語コード
    eng
  • 本文言語コード
    eng
  • 出版地
    [San Rafael, Calif.]
  • ページ数/冊数
    xi, 147 p.
  • 大きさ
    24 cm
  • 親書誌ID
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