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Community search over big graphs /

紀錄類型:	書目-語言資料,印刷品 : 單行本
正題名/作者:	Community search over big graphs // Xin Huang, Laks V.S. Lakshmanan, Jianliang Xu.
作者:	Huang, Xin.
その他の著者:	Lakshmanan, Laks V. S.,
出版された:	[San Rafael, California] :Morgan & Claypool Publishers,c2019.
記述:	xvii, 188 p. :ill. (some col.) ;24 cm.
主題:	Big data - Social aspects. -
国際標準図書番号 (ISBN):	9781681735955 (pbk.) :
国際標準図書番号 (ISBN):	1681735954 (pbk.)
国際標準図書番号 (ISBN):	9781681735962 (ebk.)
国際標準図書番号 (ISBN):	1681735962 (ebk.)
国際標準図書番号 (ISBN):	9781681735979 (bound)
国際標準図書番号 (ISBN):	1681735970 (bound)

Community search over big graphs /
Huang, Xin.

Community search over big graphs /Xin Huang, Laks V.S. Lakshmanan, Jianliang Xu. - [San Rafael, California] :Morgan & Claypool Publishers,c2019. - xvii, 188 p. :ill. (some col.) ;24 cm. - Synthesis lectures on data management,#612153-5418 ;. - Synthesis lectures on data management ;#61..

Includes bibliographical references (p. 169-185).

1. Introduction -- 1.1. Graphs and communities -- 1.2. Community search -- 1.3. Prerequisite and target reader -- 1.4. Outline of the book -- 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 -- 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 -- 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 -- 5. Social circle analysis -- 5.1. Ego-networks -- 5.2.structural diversity search -- 5.3. Learning to discover social circles -- 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 -- 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 -- 8. Further readings and future directions -- 8.1. Further readings -- 8.2. Future directions and open problems -- 8.3. Conclusions.

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.

ISBN: 9781681735955 (pbk.) :NT1751Subjects--Topical Terms:

1000128484
Big data
--Social aspects.

LC Class. No.: QA76.9.B45 / H83 2019

Dewey Class. No.: 001.4/2

Community search over big graphs /
LDR:03556cam a2200277 a 4500 001 1000103574
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008 200210s2019 caua b 000 0 eng c
020 $a 9781681735955 (pbk.) : $c NT1751
020 $a 1681735954 (pbk.)
020 $a 9781681735962 (ebk.)
020 $a 1681735962 (ebk.)
020 $a 9781681735979 (bound)
020 $a 1681735970 (bound)
035 $a (OCoLC)1140102246 $z (OCoLC)1112425040 $z (OCoLC)1112425888
035 $a on1140102246
040 $a NMS $b eng $c NMS $d YDX $d OCLCF
042 $a pcc
050 # 4 $a QA76.9.B45 $b H83 2019
082 0 4 $a 001.4/2 $2 23
100 1 $a Huang, Xin. $3 1000128480
245 1 0 $a Community search over big graphs / $c Xin Huang, Laks V.S. Lakshmanan, Jianliang Xu.
260 # $a [San Rafael, California] : $b Morgan & Claypool Publishers, $c c2019.
300 $a xvii, 188 p. : $b ill. (some col.) ; $c 24 cm.
490 1 $a Synthesis lectures on data management, $x 2153-5418 ; $v #61
504 $a Includes bibliographical references (p. 169-185).
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 -- 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 -- 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 -- 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 -- 5. Social circle analysis -- 5.1. Ego-networks -- 5.2.structural diversity search -- 5.3. Learning to discover social circles -- 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 -- 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 -- 8. Further readings and future directions -- 8.1. Further readings -- 8.2. Future directions and open problems -- 8.3. Conclusions.
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.
650 # 0 $a Big data $x Social aspects. $3 1000128484
650 # 0 $a Online social networks. $3 184875
700 1 # $a Lakshmanan, Laks V. S., $d 1959- $3 1000128481
700 1 # $a Xu, Jianliang, $d 1976- $3 1000128482
830 0 $a Synthesis lectures on data management ; $v #61. $3 1000128483