Mining Heterogeneous Information Networks: Principles and Methodologies (Paperback)

Yizhou Sun, Jiawei Han

  • 出版商: Morgan & Claypool
  • 出版日期: 2012-07-23
  • 定價: $1,400
  • 售價: 9.0$1,260
  • 語言: 英文
  • 頁數: 160
  • 裝訂: Paperback
  • ISBN: 1608458806
  • ISBN-13: 9781608458806
  • 相關分類: 大數據 Big-dataData Science
  • 立即出貨 (庫存=1)

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商品描述

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.

Table of 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

商品描述(中文翻譯)

現實世界中的物理和抽象數據對象相互關聯,形成龐大而相互連接的網絡。通過將這些數據對象和對象之間的交互結構化為多種類型,這些網絡變成了半結構化的異構信息網絡。大多數處理大數據的現實應用,包括相互連接的社交媒體和社交網絡、科學、工程或醫學信息系統、在線電子商務系統和大多數數據庫系統,都可以結構化為異構信息網絡。因此,對大規模異構信息網絡的有效分析提出了一個有趣但關鍵的挑戰。

在本書中,我們研究了挖掘異構信息網絡的原則和方法。與許多現有的將相互連接的數據視為同質圖或網絡的網絡模型不同,我們的半結構化異構信息網絡模型利用了網絡中類型化節點和連結的豐富語義,從網絡中發現了驚人的豐富知識。這種半結構化異構網絡建模引發了一系列關於挖掘相互連接數據的新原則和強大方法,包括:(1)基於排名的聚類和分類;(2)基於元路徑的相似性搜索和挖掘;(3)關係強度感知挖掘,以及其他潛在的發展。本書介紹了這一新的研究前沿,並指出了一些有前景的研究方向。

目錄:引言 / 基於排名的聚類 / 異構信息網絡的分類 / 基於元路徑的相似性搜索 / 基於元路徑的關係預測 / 帶有不完整屬性的關係強度感知聚類 / 通過元路徑選擇的用戶引導聚類 / 研究前沿