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Community search over big graphs /
~
Xu, Jianliang, (1976-)
Community search over big graphs /
Record Type:
Language materials, printed : Monograph/item
Title/Author:
Community search over big graphs // Xin Huang, Laks V.S. Lakshmanan, Jianliang Xu.
Author:
Huang, Xin
other author:
Lakshmanan, Laks V. S.,
Description:
1 PDF (xvii, 188 pages) :illustrations (some color). :
Notes:
Part of: Synthesis digital library of engineering and computer science.
Subject:
Communities - Research -
Online resource:
https://doi.org/10.2200/S00928ED1V01Y201906DTM061
Online resource:
https://ieeexplore.ieee.org/servlet/opac?bknumber=8792419
ISBN:
9781681735962
Community search over big graphs /
Huang, Xin(Computer scientist),
Community search over big graphs /
Xin Huang, Laks V.S. Lakshmanan, Jianliang Xu. - 1 PDF (xvii, 188 pages) :illustrations (some color). - Synthesis lectures on data management,#612153-5426 ;. - Synthesis digital library of engineering and computer science..
Part of: Synthesis digital library of engineering and computer science.
Includes bibliographical references (pages 169-185).
8. Further readings and future directions -- 8.1. Further readings -- 8.2. Future directions and open problems -- 8.3. Conclusions.
Abstract freely available; full-text restricted to subscribers or individual document purchasers.
Compendex
Communities serve as basic structural building blocks for understanding the organization of many real-world networks, including social, biological, collaboration, and communication networks. Recently, community search over graphs has attracted significantly increasing attention, from small, simple, and static graphs to big, evolving, attributed, and location-based graphs. In this book, we first review the basic concepts of networks, communities, and various kinds of dense subgraph models. We then survey the state of the art in community search techniques on various kinds of networks across different application areas. Specifically, we discuss cohesive community search, attributed community search, social circle discovery, and geo-social group search. We highlight the challenges posed by different community search problems. We present their motivations, principles, methodologies, algorithms, and applications, and provide a comprehensive comparison of the existing techniques. This book finally concludes by listing publicly available real-world datasets and useful tools for facilitating further research, and by offering further readings and future directions of research in this important and growing area.
Mode of access: World Wide Web.
ISBN: 9781681735962
Standard No.: 10.2200/S00928ED1V01Y201906DTM061doiSubjects--Topical Terms:
1253126
Communities
--ResearchSubjects--Index Terms:
big data
LC Class. No.: HM756 / .G833 2019eb
Dewey Class. No.: 001.4/2
Community search over big graphs /
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Community search over big graphs /
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Xin Huang, Laks V.S. Lakshmanan, Jianliang Xu.
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Synthesis lectures on data management,
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Part of: Synthesis digital library of engineering and computer science.
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Includes bibliographical references (pages 169-185).
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8. Further readings and future directions -- 8.1. Further readings -- 8.2. Future directions and open problems -- 8.3. Conclusions.
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1. Introduction -- 1.1. Graphs and communities -- 1.2. Community search -- 1.3. Prerequisite and target reader -- 1.4. Outline of the book
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2. Cohesive subgraphs -- 2.1. Community search and cohesive subgraphs -- 2.2. Notations and notions -- 2.3. Classical dense subgraphs -- 2.4. K-core and k-truss -- 2.5. More dense subgraphs -- 2.6. Summary
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3. Cohesive community search -- 3.1. Quasi-clique community models -- 3.2. Core-based community models -- 3.3. Truss-based community models -- 3.4. Query-biased densest community model -- 3.5. Summary
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4. Attributed community search -- 4.1. Introduction -- 4.2. k-core-based attribute community model -- 4.3. k-truss-based attribute community model -- 4.4. Summary
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5. Social circle analysis -- 5.1. Ego-networks -- 5.2.structural diversity search -- 5.3. Learning to discover social circles
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6. Geo-social group search -- 6.1. Geo-social group search -- 6.2. Proximity-based geo-social group search -- 6.3. Geo-social k-cover group search -- 6.4. Geo-social group search based on minimum covering circle
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7. Datasets and tools -- 7.1. Real-world datasets -- 7.2. Query generation and evaluation -- 7.3. Software and demo systems -- 7.4. Suggestions on dense subgraph selection for community models
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Abstract freely available; full-text restricted to subscribers or individual document purchasers.
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Communities serve as basic structural building blocks for understanding the organization of many real-world networks, including social, biological, collaboration, and communication networks. Recently, community search over graphs has attracted significantly increasing attention, from small, simple, and static graphs to big, evolving, attributed, and location-based graphs. In this book, we first review the basic concepts of networks, communities, and various kinds of dense subgraph models. We then survey the state of the art in community search techniques on various kinds of networks across different application areas. Specifically, we discuss cohesive community search, attributed community search, social circle discovery, and geo-social group search. We highlight the challenges posed by different community search problems. We present their motivations, principles, methodologies, algorithms, and applications, and provide a comprehensive comparison of the existing techniques. This book finally concludes by listing publicly available real-world datasets and useful tools for facilitating further research, and by offering further readings and future directions of research in this important and growing area.
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Also available in print.
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Mode of access: World Wide Web.
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System requirements: Adobe Acrobat Reader.
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Title from PDF title page (viewed on June 26, 2019).
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Communities
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Research
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Data processing.
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Social media
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big data
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community detection
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community search
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Lakshmanan, Laks V. S.,
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1959-
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Xu, Jianliang,
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1976-
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Jagadish, H. V.,
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9781681735955
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9781681735979
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Synthesis digital library of engineering and computer science.
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Synthesis lectures on data management ;
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#36.
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Abstract with links to full text
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https://doi.org/10.2200/S00928ED1V01Y201906DTM061
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Abstract with links to resource
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https://ieeexplore.ieee.org/servlet/opac?bknumber=8792419
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