Mining Graph Data
暫譯: 圖形數據挖掘
Diane J. Cook, Lawrence B. Holder
- 出版商: Wiley
- 出版日期: 2006-11-01
- 定價: $3,980
- 售價: 9.5 折 $3,781
- 語言: 英文
- 頁數: 500
- 裝訂: Hardcover
- ISBN: 0471731900
- ISBN-13: 9780471731900
-
相關分類:
大數據 Big-data、Data Science
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商品描述
Description
This text takes a focused and comprehensive look at mining data represented as a graph, with the latest findings and applications in both theory and practice provided. Even if you have minimal background in analyzing graph data, with this book you’ll be able to represent data as graphs, extract patterns and concepts from the data, and apply the methodologies presented in the text to real datasets.There is a misprint with the link to the accompanying Web page for this book. For those readers who would like to experiment with the techniques found in this book or test their own ideas on graph data, the Web page for the book should be http://www.eecs.wsu.edu/MGD.
Table of Contents
Preface.Acknowledgments.
Contributors.
1 INTRODUCTION (Lawrence B. Holder and Diane J. Cook).
1.1 Terminology.
1.2 Graph Databases.
1.3 Book Overview.
References.
Part I GRAPHS.
2 GRAPH MATCHING—EXACT AND ERROR-TOLERANT METHODS AND THE AUTOMATIC LEARNING OF EDIT COSTS (Horst Bunke and Michel Neuhaus).
2.1 Introduction.
2.2 Definitions and Graph Matching Methods.
2.3 Learning Edit Costs.
2.4 Experimental Evaluation.
2.5 Discussion and Conclusions.
References.
3 GRAPH VISUALIZATION AND DATA MINING (Walter Didimo and Giuseppe Liotta).
3.1 Introduction.
3.2 Graph Drawing Techniques.
3.3 Examples of Visualization Systems.
3.4 Conclusions.
References.
4 GRAPH PATTERNS AND THE R-MAT GENERATOR (Deepayan Chakrabarti and Christos Faloutsos).
4.1 Introduction.
4.2 Background and Related Work.
4.3 NetMine and R-MAT.
4.4 Experiments.
4.5 Conclusions.
References.
Part II MINING TECHNIQUES.
5 DISCOVERY OF FREQUENT SUBSTRUCTURES (Xifeng Yan and Jiawei Han).
5.1 Introduction.
5.2 Preliminary Concepts.
5.3 Apriori-based Approach.
5.4 Pattern Growth Approach.
5.5 Variant Substructure Patterns.
5.6 Experiments and Performance Study.
5.7 Conclusions.
References.
6 FINDING TOPOLOGICAL FREQUENT PATTERNS FROM GRAPH DATASETS (Michihiro Kuramochi and George Karypis).
6.1 Introduction.
6.2 Background Definitions and Notation.
6.3 Frequent Pattern Discovery from Graph Datasets—Problem Definitions.
6.4 FSG for the Graph-Transaction Setting.
6.5 SIGRAM for the Single-Graph Setting.
6.6 GREW—Scalable Frequent Subgraph Discovery Algorithm.
6.7 Related Research.
6.8 Conclusions.
References.
7 UNSUPERVISED AND SUPERVISED PATTERN LEARNING IN GRAPH DATA (Diane J. Cook, Lawrence B. Holder, and Nikhil Ketkar).
7.1 Introduction.
7.2 Mining Graph Data Using Subdue.
7.3 Comparison to Other Graph-Based Mining Algorithms.
7.4 Comparison to Frequent Substructure Mining Approaches.
7.5 Comparison to ILP Approaches.
7.6 Conclusions.
References.
8 GRAPH GRAMMAR LEARNING (Istvan Jonyer).
8.1 Introduction.
8.2 Related Work.
8.3 Graph Grammar Learning.
8.4 Empirical Evaluation.
8.5 Conclusion.
References.
9 CONSTRUCTING DECISION TREE BASED ON CHUNKINGLESS GRAPH-BASED INDUCTION (Kouzou Ohara, Phu Chien Nguyen, Akira Mogi, Hiroshi Motoda, and Takashi Washio).
9.1 Introduction.
9.2 Graph-Based Induction Revisited.
9.3 Problem Caused by Chunking in B-GBI.
9.4 Chunkingless Graph-Based Induction (Cl-GBI).
9.5 Decision Tree Chunkingless Graph-Based Induction (DT-ClGBI).
9.6 Conclusions.
References.
10 SOME LINKS BETWEEN FORMAL CONCEPT ANALYSIS AND GRAPH MINING (Michel Liquière).
10.1 Presentation.
10.2 Basic Concepts and Notation.
10.3 Formal Concept Analysis.
10.4 Extension Lattice and Description Lattice Give Concept Lattice.
10.5 Graph Description and Galois Lattice.
10.6 Graph Mining and Formal Propositionalization.
10.7 Conclusion.
References.
11 KERNEL METHODS FOR GRAPHS (Thomas Gärtner, Tamás Horváth, Quoc V. Le, Alex J. Smola, and Stefan Wrobel).
11.1 Introduction.
11.2 Graph Classification.
11.3 Vertex Classification.
11.4 Conclusions and Future Work.
References.
12 KERNELS AS LINK ANALYSIS MEASURES (Masashi Shimbo and Takahiko Ito).
12.1 Introduction.
12.2 Preliminaries.
12.3 Kernel-based Unified Framework for Importance and Relatedness.
12.4 Laplacian Kernels as a Relatedness Measure.
12.5 Practical Issues.
12.6 Related Work.
12.7 Evaluation with Bibliographic Citation Data.
12.8 Summary.
References.
13 ENTITY RESOLUTION IN GRAPHS (Indrajit Bhattacharya and Lise Getoor).
13.1 Introduction.
13.2 Related Work.
13.3 Motivating Example for Graph-Based Entity Resolution.
13.4 Graph-Based Entity Resolution: Problem Formulation.
13.5 Similarity Measures for Entity Resolution.
13.6 Graph-Based Clustering for Entity Resolution.
13.7 Experimental Evaluation.
13.8 Conclusion.
References.
Part III APPLICATIONS.
14 MINING FROM CHEMICAL GRAPHS (Takashi Okada).
14.1 Introduction and Representation of Molecules.
14.2 Issues for Mining.
14.3 CASE: A Prototype Mining System in Chemistry.
14.4 Quantitative Estimation Using Graph Mining.
14.5 Extension of Linear Fragments to Graphs.
14.6 Combination of Conditions.
14.7 Concluding Remarks.
References.
15 UNIFIED APPROACH TO ROOTED TREE MINING: ALGORITHMS AND APPLICATIONS (Mohammed Zaki).
15.1 Introduction.
15.2 Preliminaries.
15.3 Related Work.
15.4 Generating Candidate Subtrees.
15.5 Frequency Computation.
15.6 Counting Distinct Occurrences.
15.7 The SLEUTH Algorithm.
15.8 Experimental Results.
15.9 Tree Mining Applications in Bioinformatics.
15.10 Conclusions.
References.
16 DENSE SUBGRAPH EXTRACTION (Andrew Tomkins and Ravi Kumar).
16.1 Introduction.
16.2 Related Work.
16.3 Finding the densest subgraph.
16.4 Trawling.
16.5 Graph Shingling.
16.6 Connection Subgraphs.
16.7 Conclusions.
References.
17 SOCIAL NETWORK ANALYSIS (Sherry E. Marcus, Melanie Moy, and Thayne Coffman).
17.1 Introduction.
17.2 Social Network Analysis.
17.3 Group Detection.
17.4 Terrorist Modus Operandi Detection System.
17.5 Computational Experiments.
17.6 Conclusion.
References.
Index.
商品描述(中文翻譯)
**描述**
本書專注且全面地探討以圖形表示的數據挖掘，提供最新的理論與實踐發現與應用。即使您在分析圖形數據方面的背景知識有限，透過本書，您將能夠將數據表示為圖形，從數據中提取模式和概念，並將書中所介紹的方法應用於實際數據集。
本書的附屬網頁鏈接有誤。對於希望實驗本書中技術或測試自己在圖形數據上想法的讀者，書籍的網頁應為 [http://www.eecs.wsu.edu/MGD](http://www.eecs.wsu.edu/MGD)。
**目錄**
前言
致謝
貢獻者
1 引言（Lawrence B. Holder 和 Diane J. Cook）
1.1 術語
1.2 圖形數據庫
1.3 書籍概述
參考文獻
第一部分 圖形
2 圖形匹配—精確與容錯方法及自動學習編輯成本（Horst Bunke 和 Michel Neuhaus）
2.1 引言
2.2 定義與圖形匹配方法
2.3 學習編輯成本
2.4 實驗評估
2.5 討論與結論
參考文獻
3 圖形可視化與數據挖掘（Walter Didimo 和 Giuseppe Liotta）
3.1 引言
3.2 圖形繪製技術
3.3 可視化系統示例
3.4 結論
參考文獻
4 圖形模式與 R-MAT 生成器（Deepayan Chakrabarti 和 Christos Faloutsos）
4.1 引言
4.2 背景與相關工作
4.3 NetMine 和 R-MAT
4.4 實驗
4.5 結論
參考文獻
第二部分 挖掘技術
5 頻繁子結構的發現（Xifeng Yan 和 Jiawei Han）
5.1 引言
5.2 初步概念
5.3 基於 Apriori 的方法
5.4 模式增長方法
5.5 變體子結構模式
5.6 實驗與性能研究
5.7 結論
參考文獻
6 從圖形數據集發現拓撲頻繁模式（Michihiro Kuramochi 和 George Karypis）
6.1 引言
6.2 背景定義與符號
6.3 從圖形數據集發現頻繁模式—問題定義
6.4 圖形交易設置的 FSG
6.5 單圖設置的 SIGRAM
6.6 GREW—可擴展的頻繁子圖發現算法
6.7 相關研究
6.8 結論
參考文獻
7 圖形數據中的無監督與監督模式學習（Diane J. Cook、Lawrence B. Holder 和 Nikhil Ketkar）
7.1 引言
7.2 使用 Subdue 挖掘圖形數據
7.3 與其他基於圖形的挖掘算法的比較
7.4 與頻繁子結構挖掘方法的比較
7.5 與 ILP 方法的比較
7.6 結論
參考文獻
8 圖形文法學習（Istvan Jonyer）
8.1 引言
8.2 相關工作
8.3 圖形文法學習
8.4 實證評估
8.5 結論
參考文獻
9 基於無分塊圖形的決策樹構建（Kouzou Ohara、Phu Chien Nguyen、Akira Mogi、Hiroshi Motoda 和 Takashi Washio）
9.1 引言
9.2 重新檢視基於圖形的歸納
9.3 B-GBI 中由於分塊造成的問題
9.4 無分塊的基於圖形的歸納（Cl-GBI）
9.5 決策樹無分塊的基於圖形的歸納（DT-ClGBI）
9.6 結論
參考文獻
10 正式概念分析與圖形挖掘之間的一些聯繫（Michel Liquiére）
10.1 簡介
10.2 基本概念與符號
10.3 正式概念分析
10.4 擴展格與描述格給出概念格
10.5 圖形描述與 Galois 格
10.6 圖形挖掘與正式命題化
10.7 結論
參考文獻
11 圖形的核方法（Thomas Gärtner、Tamás Horváth、Quoc V. Le、Alex J. Smola 和 Stefan Wrobel）
11.1 引言
11.2 圖形分類
11.3 頂點分類
11.4 結論與未來工作
參考文獻
12 作為鏈接分析度量的核（Masashi Shimbo 和 Takahiko Ito）
12.1 引言
12.2 初步知識
12.3 基於核的統一框架以衡量重要性和相關性
12.4 拉普拉斯核作為相關性度量
12.5 實際問題
12.6 相關工作
12.7 使用文獻引用數據的評估
12.8 總結
參考文獻
13 圖形中的實體解析（Indrajit Bhattacharya 和 Lise Getoor）
13.1 引言
13.2 相關工作
13.3 基於圖形的實體解析的激勵示例
13.4 基於圖形的實體解析：問題表述
13.5 實體解析的相似性度量
13.6 基於圖形的實體解析聚類
13.7 實驗評估
13.8 結論
參考文獻
第三部分 應用
14 從化學圖形中挖掘（Takashi Okada）
14.1 引言與分子表示
14.2 挖掘的問題
14.3 案例：化學中的原型挖掘系統
14.4 使用圖形挖掘的定量估算
14.5 將線性片段擴展到圖形
14.6 條件的組合
14.7 結語
參考文獻
15 根樹挖掘的統一方法：算法與應用（Mohammed Zaki）
15.1 引言
15.2 初步知識
15.3 相關工作
15.4 生成候選子樹
15.5 頻率計算
15.6 計算不同的出現次數
15.7 SLEUTH 算法
15.8 實驗結果
15.9 生物信息學中的樹挖掘應用
15.10 結論
參考文獻
16 密集子圖提取（Andrew Tomkins 和 Ravi Kumar）
16.1 引言
16.2 相關工作
16.3 尋找最密集的子圖
16.4 拖網
16.5 圖形分片
16.6 連接子圖
16.7 結論
參考文獻
17 社交網絡分析（Sherry E. Marcus、Melanie Moy 和 Thayne Coffman）
17.1 引言
17.2 社交網絡分析
17.3 群體檢測
17.4 恐怖分子作業模式檢測系統
17.5 計算實驗
17.6 結論
參考文獻
索引
