國立虎尾科技大學 |

Community search over big graphs /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Community search over big graphs // Xin Huang, Laks V.S. Lakshmanan, Jianliang Xu.
作者:	Huang, Xin
其他作者:	Lakshmanan, Laks V. S.,
面頁冊數:	1 PDF (xvii, 188 pages) :illustrations (some color). :
附註:	Part of: Synthesis digital library of engineering and computer science.
標題:	Communities - Research -
電子資源:	https://doi.org/10.2200/S00928ED1V01Y201906DTM061
電子資源:	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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100 1 $a Huang, Xin $c (Computer scientist), $e author. $3 1253123
245 1 0 $a Community search over big graphs / $c Xin Huang, Laks V.S. Lakshmanan, Jianliang Xu.
264 1 $a [San Rafael, California] : $b Morgan & Claypool, $c [2019]
300 $a 1 PDF (xvii, 188 pages) : $b illustrations (some color).
336 $a text $2 rdacontent
337 $a electronic $2 isbdmedia
338 $a online resource $2 rdacarrier
490 1 $a Synthesis lectures on data management, $x 2153-5426 ; $v #61
500 $a Part of: Synthesis digital library of engineering and computer science.
504 $a Includes bibliographical references (pages 169-185).
505 8 $a 8. Further readings and future directions -- 8.1. Further readings -- 8.2. Future directions and open problems -- 8.3. Conclusions.
505 0 $a 1. Introduction -- 1.1. Graphs and communities -- 1.2. Community search -- 1.3. Prerequisite and target reader -- 1.4. Outline of the book
505 8 $a 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
505 8 $a 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
505 8 $a 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
505 8 $a 5. Social circle analysis -- 5.1. Ego-networks -- 5.2.structural diversity search -- 5.3. Learning to discover social circles
505 8 $a 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
505 8 $a 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
506 $a Abstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0 $a Compendex
510 0 $a INSPEC
510 0 $a Google scholar
510 0 $a Google book search
520 $a 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.
530 $a Also available in print.
538 $a Mode of access: World Wide Web.
538 $a System requirements: Adobe Acrobat Reader.
588 $a Title from PDF title page (viewed on June 26, 2019).
650 0 $a Communities $x Research $x Data processing. $3 1253126
650 0 $a Social media $x Research $x Data processing. $3 1253127
650 0 $a Big data. $3 981821
650 0 $a Graphic methods. $3 768930
653 $a big data
653 $a big graphs
653 $a social networks
653 $a community detection
653 $a community search
653 $a dense subgraph
653 $a cohesive subgraph
653 $a attributed community
653 $a geo-spatial community
653 $a social circle
653 $a k-core
653 $a k-truss
700 1 $a Lakshmanan, Laks V. S., $d 1959- $3 981877
700 1 $a Xu, Jianliang, $d 1976- $e author. $3 1253124
700 1 $a Jagadish, H. V., $e author. $3 1253125
776 0 8 $i Print version: $z 9781681735955 $z 9781681735979
830 0 $a Synthesis digital library of engineering and computer science. $3 598254
830 0 $a Synthesis lectures on data management ; $v #36. $3 931356
856 4 0 $3 Abstract with links to full text $u https://doi.org/10.2200/S00928ED1V01Y201906DTM061
856 4 2 $3 Abstract with links to resource $u https://ieeexplore.ieee.org/servlet/opac?bknumber=8792419